Risk Engineering - User contributions [en]

Uncertainty and Risk in Civil Engineering Practice

2026-01-20T01:38:28Z

WikiSysop: 2026 01 19 GPT recommendations

'''Life is short and the art long; the occasion instant, experiment perilous, decision difficult.'''
(Hippocrates, as quoted in Fox)

:''See also [[Project Definition]], [[Project Life Cycle and Phase Models]], [[Project Delivery Methods]]''
:''See also [[Technical Outcomes for Civil Engineering: Risk and Uncertainty]]''

==Basic considerations==
Civil engineering practice requires decision-making in environments characterized by incomplete, imperfect, and evolving knowledge. Engineers routinely act in the presence of uncertainty related to physical phenomena, economic constraints, regulatory requirements, and human behavior. The professional problem is not the elimination of uncertainty, which is impossible in practice, but its reduction, partitioning, and management to a degree sufficient to support responsible action.

In this chapter, '''risk''' is treated as a bounded and decision-relevant subset of broader uncertainty. Uncertainty that can be reduced, structured, prioritized, and acted upon using professional knowledge, models, and experience is termed risk. Related but distinct is the concept of '''hazard''', which denotes a source or condition with the potential to cause harm, independent of likelihood or consequence.

This framing aligns with interdisciplinary treatments of risk analysis, which emphasize the role of uncertainty in shaping social, technical, and governance systems over long historical periods. McDaniels and Small describe risk analysis as emerging from the need to act despite imperfect knowledge and argue that societies have progressively shifted from fatalistic acceptance of uncertainty toward its active management through structured analysis and decision frameworks.<ref name="McDaniels">McDaniels, Timothy, and Mitchell Small. ''Risk Analysis and Society: An Interdisciplinary Characterization of the Field''. Cambridge University Press, 2004, p. 1.</ref>

Within civil engineering, this evolution has produced specialized practices focused on managing uncertainty through modeling, design standards, safety factors, reliability analysis, and project controls. These practices rely not only on domain knowledge of materials, loads, and systems, but also on '''meta-knowledge''': knowledge about the limitations, reliability, and appropriate use of engineering knowledge itself.

==Semantic, epistemic, and logical frameworks==
Clear terminology is a prerequisite for coherent risk analysis. In professional practice, inconsistent or interchangeable use of terms such as uncertainty, risk, hazard, error, and bias can obscure decision responsibility and complicate communication among stakeholders.

The semantic framework adopted in this chapter proceeds in stages:
# distinction between certainty and uncertainty;
# refinement of uncertainty into epistemic, aleatory, and behavioral components;
# use of error and bias as operational proxies for uncertainty;
# transition from general uncertainty to discipline-specific risk taxonomies.

Beyond this point, the discussion shifts from semantics to logical frameworks, where uncertainty is partitioned into structured taxonomies suitable for analysis, communication, and decision-making in engineering projects.

==Semantics framework==

===Certainty and uncertainty===
Certainty is commonly described as an epistemic property of belief, implying immunity from doubt or criticism. In practical contexts, however, absolute certainty is neither attainable nor desirable. As Mill observed, assurance sufficient for the purposes of human life does not require freedom from all doubt, but rather justified confidence grounded in experience and evidence.

Uncertainty arises whenever beliefs or propositions are subject to doubt, criticism, or incomplete support. Contemporary treatments emphasize that uncertainty is not merely a feature of the external world, but also a cognitive condition reflecting awareness of incomplete knowledge. Han et al. characterize uncertainty as a meta-cognitive state: knowing that one does not know, or knowing that one’s knowledge may be incomplete or unreliable.<ref name="Han">Han, Paul K. J., William M. P. Klein, and Neeraj K. Arora. “Varieties of Uncertainty in Health Care: A Conceptual Taxonomy.” ''Medical Decision Making'' 31, no. 6 (2011): 828–838.</ref>

In professional reasoning, uncertainty manifests as the potential for error in observation, modeling, judgment, and action. Reasoning under uncertainty therefore differs fundamentally from reasoning under certainty: conclusions are provisional, contingent, and subject to revision as new information becomes available.

===Error, bias, and uncertainty===
In applied disciplines, uncertainty is rarely managed directly. Instead, it is addressed through its observable proxies: error and bias. Error refers to deviation between an estimate, measurement, or prediction and a reference value or observed outcome. Bias denotes systematic or directional error arising from model structure, data selection, judgment, or institutional practice.

Error can often be quantified, propagated, and reduced using analytical and statistical methods. Bias is more difficult to detect and correct, frequently requiring theoretical analysis, validation studies, or comparison across independent methods. The management of uncertainty in civil engineering therefore depends critically on understanding how error and bias arise, how they propagate through sequential decisions, and how they can be bounded through professional controls.

===Propagation of error and uncertainty===
Engineering decisions typically involve multi-step processes: observation, interpretation, modeling, design, construction, and operation. Errors introduced early in this chain can propagate and amplify downstream, while later errors may have limited impact depending on system sensitivity and redundancy.

Professional practice therefore emphasizes identifying where uncertainty enters the decision sequence and prioritizing error and bias reduction where it has the greatest effect on outcomes. This ordering principle underlies practices such as conservative design assumptions, staged investigations, peer review, and independent verification.

===Epistemic, aleatory, and behavioral uncertainty===
For practical purposes, uncertainty in civil engineering can be partitioned into three broad components:
* '''Epistemic uncertainty''', arising from incomplete knowledge, limited data, or imperfect models;
* '''Aleatory uncertainty''', reflecting inherent variability in physical processes;
* '''Behavioral uncertainty''', associated with human judgment, decision-making, and organizational interaction.

This partitioning supports targeted mitigation strategies. Epistemic uncertainty may be reduced through investigation and analysis; aleatory uncertainty may be managed through design margins and reliability methods; behavioral uncertainty requires institutional controls, contractual clarity, and governance mechanisms.

==From uncertainty to risk and hazard==
Risk and hazard are not synonymous with uncertainty, but structured subsets of it. Risk refers to uncertainty that has been framed in terms of potential consequences and that is sufficiently bounded to support decision-making. Hazard denotes a source or condition with the potential to cause harm, independent of likelihood.

In this chapter, '''general risk''' is defined as:
:'''a bounded subset of uncertainty associated with the potential for material error or bias when acting or choosing in physical, economic, or social environments, and that can be represented using coherent analytical or probabilistic frameworks and subjected to testing or validation.'''

This definition is discipline-neutral and applies across engineering, economics, medicine, and policy. Subsequent sections introduce discipline-specific taxonomies that adapt this general concept to the particular decision contexts of civil engineering projects.

==Logical framework for specific risk taxonomies==
Once uncertainty has been reduced to bounded, decision-relevant forms, it becomes possible to develop taxonomies that support analysis, communication, and management. Taxonomies are not required to be epistemically complete; rather, they must be logically distinct, practically exhaustive for their purpose, and aligned with decision needs.

The following sections examine how scientific, legal, economic, and engineering disciplines construct uncertainty and risk taxonomies, and how these perspectives inform civil engineering practice.

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===Scientific risk taxonomies===
Scientific inquiry proceeds by observing phenomena, proposing explanatory models, and testing those models against experience. At every stage of this process, uncertainty is present. Scientific risk taxonomies arise from the need to identify, distinguish, and manage different sources of uncertainty that affect observation, modeling, inference, and prediction.

Most scientific taxonomies of uncertainty have focused on uncertainties associated with data, parameters, and models, particularly those that can be represented statistically. While multiple taxonomies are possible, a common organizing principle is that uncertainty is environment-specific, phenomenon-oriented, and model-centric.

====Environment-specific, phenomenon-oriented, model-centric taxonomy of uncertainty====
A widely used scientific taxonomy distinguishes uncertainty according to its relationship to models and data:

* '''Parameter uncertainty'''
Parameter uncertainty arises from uncertainty in the numerical values of model inputs. These parameters may be physically unobservable, indirectly inferred, or only partially constrained by available data.
** '''Experimental (observational) uncertainty''' results from variability in repeated measurements under nominally identical conditions.
** '''Parametric variability''' reflects natural or contextual variability in input variables that are treated as fixed values within a model.

* '''Structural uncertainty'''
Structural uncertainty, also referred to as model inadequacy or model discrepancy, arises when a model fails to capture relevant features of the underlying phenomenon. Because all models are simplifications of reality, structural uncertainty is unavoidable and depends on model assumptions, scope, and fitness for purpose.
** Structural uncertainty is distinct from parameter uncertainty and cannot be reduced solely by collecting additional data.
** Inadequate model structure can lead to systematically misleading conclusions even when parameter estimates appear precise.

* '''Algorithmic (numerical) uncertainty'''
Algorithmic uncertainty arises from numerical approximations used to implement models computationally. Examples include discretization error, convergence error, and truncation error associated with numerical solution methods such as finite difference or finite element approximations.
** These uncertainties depend**

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==Uncertainty taxonomies in a legal environment==
Legal decision-making operates under conditions of uncertainty that differ in purpose and structure from those of science or engineering. From a legal perspective, a taxonomy is a systematic scheme for distinguishing, ordering, and naming types within a subject field. Such taxonomies are not required to be epistemically complete; rather, they must be sufficient to organize the kinds of descriptive information commonly encountered by decision-makers and to support fair, effective, and defensible judgments.

Walker characterizes legal taxonomies of scientific and technical uncertainty as tools for cataloging uncertainty associated with descriptive assertions, particularly those involving cause-and-effect claims. These taxonomies emphasize clarity, logical distinction, and practical exhaustiveness over theoretical completeness.

===Core components of legal uncertainty===
Legal frameworks distinguish multiple, logically distinct sources of uncertainty that may affect descriptive assertions and decisions:

* '''Linguistic uncertainty'''
Linguistic uncertainty, often described as vagueness, arises from limitations of language rather than from data or models.
** '''Context dependence''' occurs when the applicability of a statement depends on unstated or shifting contextual conditions.
** '''Ambiguity''' arises when terms admit multiple meanings that cannot be resolved within the given context.
** '''Underspecification''' occurs when descriptions lack sufficient detail to support a unique interpretation.
Linguistic uncertainty underlies all other components of legal uncertainty, as it affects how evidence, concepts, and models are interpreted.

* '''Conceptual uncertainty'''
Conceptual uncertainty arises from the choice and definition of concepts used to describe phenomena. Whenever predicates are selected to characterize an object, event, or condition, alternative conceptualizations are excluded.
Conceptualization generates useful structure, but it can also constrain understanding by privileging certain interpretations over others. Poorly chosen or inadequately defined concepts can therefore introduce systematic distortion into legal and technical reasoning.

Conceptual uncertainty is closely related to issues of validity. A concept is valid to the extent that it accurately represents the phenomenon it is intended to describe. Conceptual bias, like systematic error in measurement, is difficult to detect and often recognizable only through theoretical analysis or comparison across alternative frameworks.

* '''Measurement and classification uncertainty'''
Measurement uncertainty arises when concepts are applied to specific cases through observation, assessment, or classification. Even when conceptual uncertainty has been minimized, misclassification may occur due to procedural limitations, observer judgment, or data quality.
Measurement uncertainty affects the reliability and precision of descriptive assertions. Errors at this stage can propagate through subsequent reasoning and reduce the quality of causal inference or decision outcomes.

* '''Sampling uncertainty'''
Sampling uncertainty arises when conclusions about a population are inferred from a limited subset of observations. The uncertainty reflects the extent to which the sample represents the relevant population.
In legal and regulatory contexts, sampling uncertainty affects the credibility of generalizations, forecasts, and risk characterizations based on empirical evidence.

* '''Modeling uncertainty'''
Modeling uncertainty arises when descriptive or causal claims rely on simplified representations of complex systems. This includes uncertainty associated with variable selection, functional relationships, and parameter specification.
** '''Causal uncertainty''' arises when relationships between variables are inferred rather than directly observed. Legal decision-makers often rely on causal models to explain why an outcome occurred or to predict the consequences of alternative actions.
** '''Epistemic choice''' refers to uncertainty introduced by selecting among competing interpretive frameworks or explanatory models.

===Aggregation and interaction of uncertainties===
The components of legal uncertainty do not operate independently. Linguistic, conceptual, measurement, sampling, and modeling uncertainties can combine in different ways to produce aggregate uncertainty affecting the credibility, relevance, and weight of evidence.

Legal taxonomies therefore focus not on eliminating uncertainty, but on making its sources explicit so that decision-makers can assess reliability, assign weight, and determine appropriate standards of proof or precaution.

===Relationship to scientific and engineering practice===
While legal taxonomies do not require quantitative completeness, they depend critically on validated scientific and engineering methods to support descriptive and causal claims. Scientific and engineering disciplines provide procedures and models capable of integrating qualitative and quantitative information into structured representations of uncertainty.

However, the legal use of such information remains distinct. The objective is not optimization or prediction per se, but defensible judgment under uncertainty. Validation, transparency, and clarity of assumptions are therefore central concerns at the interface between legal, scientific, and engineering domains.

These distinctions inform how uncertainty is framed, communicated, and relied upon in civil engineering projects subject to regulatory review, contractual interpretation, or dispute resolution.

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===Economics uncertainty taxonomies===
Like scientific and engineering treatments of uncertainty, economic approaches are environment-specific, phenomenon-oriented, and model-centric. In economics, the relevant environment is the market or sector of economic activity, and the phenomena of interest are human actions, organizational behavior, and manufactured constructs such as firms, contracts, and markets.

Economics does not address all forms of uncertainty. Instead, it focuses on subsets of uncertainty that possess causal connections to material outcomes, particularly those affecting prices, profits, losses, and allocation of resources. These subsets are commonly labeled as risks.

A foundational contribution to the economic treatment of uncertainty is associated with Frank Knight, who distinguished between measurable risk and unmeasurable uncertainty. Knight characterized uncertainty as arising from defects or limitations in knowledge, particularly managerial and organizational knowledge, rather than from inherent randomness alone. In this framework, risk refers to situations in which probabilities can be assigned to outcomes, while uncertainty refers to situations in which such assignment is not possible.

Knight defined risk as the class of uncertainties that can be reduced or managed through probabilistic reasoning and mechanisms such as insurance. By contrast, uncertainty in the strict sense reflects imperfect knowledge that cannot be reduced to numerical probabilities. For Knight, measuring uncertainty involved assessing both the likelihood of occurrence and the severity of economic consequences, while recognizing that some consequences remain fundamentally indeterminate.

Knight emphasized that economic decision-making is rarely conducted under conditions of complete ignorance or perfect information. Instead, action is typically based on partial knowledge, judgment, and opinion formed under uncertainty. Economic behavior therefore reflects degrees of belief and confidence rather than certainty, and the practical problem lies in managing variation in knowledge rather than eliminating uncertainty altogether.

In his analysis of insurance markets, Knight illustrated the difficulty of classifying certain uncertainties. Events such as accidental death could be treated probabilistically, while sickness or business failure resisted objective classification due to heterogeneity, judgment, and contextual dependence. Uncertainties that could not be classified or grouped were therefore not insurable and remained outside the domain of measurable risk.

===Core distinctions in economic uncertainty===
Economic treatments of uncertainty commonly rely on a small number of foundational distinctions that shape both theory and practice.

* Uncertainty about the economic environment, including prices, demand, technology, and external conditions, is distinct from uncertainty about the behavior or choices of other actors.
* Risk is a subset of uncertainty in which the decision-maker can enumerate possible actions, identify possible outcomes arising from each action, and assign probabilities to those outcomes.
* Uncertainty arises when such enumeration or probability assignment is not feasible due to incomplete knowledge, structural change, heterogeneity, or reliance on judgment.

These distinctions form an idealized classification that has been influential in economic theory, despite remaining philosophically and empirically contested. They nevertheless provide a useful framework for distinguishing calculable exposure from judgment-dependent uncertainty in economic analysis.

===Uncertainty, knowledge, and economic models===
Like the theory of the firm, an economic theory of uncertainty seeks to explain and predict the behavior of specific subsets of uncertainty that affect material economic outcomes. These theories examine how the presence, distribution, and variability of knowledge influence decision-making, pricing, and allocation of resources.

For analytical convenience, many economic models assume certainty or perfect information. The economics of uncertainty exists as an alternative framework that explicitly incorporates imperfect information, unknown preferences, and judgment-based decisions when doing so improves explanatory or predictive power.

Unlike physical systems, economic activity produces artificial constructs such as firms, contracts, markets, financial instruments, and institutions. Uncertainty in economics therefore arises not only from external conditions but also from institutional design, incentives, strategic interaction, and information asymmetry.

===Risk, hazard, and moral hazard===
Economic analysis distinguishes between risk and hazard. While the terminology is not always used consistently, the distinction becomes most explicit in the concept of moral hazard.

Moral hazard arises when the presence of insurance, guarantees, or protection alters behavior, leading actors to take actions that increase exposure to loss. In such cases, uncertainty is not merely a property of the environment but is generated endogenously through behavior and information asymmetry.

This distinction is particularly relevant for engineering projects involving insurance, contracting, public–private partnerships, and regulatory oversight, where incentives and information structure influence both technical and economic outcomes.

In summary, economic uncertainty taxonomies emphasize the role of partial knowledge, judgment, and incentives in shaping material outcomes. These frameworks inform how risk is identified, priced, transferred, or retained and provide an essential foundation for understanding cost, schedule, and financial uncertainty in civil engineering practice.

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===Uncertainty and risk in policy and regulatory environment===
Decision-making in policy and regulatory contexts must proceed under conditions of incomplete and potentially inaccurate information. The central problem is not eliminating uncertainty, but selecting decision rules that remain defensible in its presence.

As a social enterprise, risk regulation, regardless of substantive objectives, is commonly evaluated against three process objectives: effectiveness, efficiency, and equity. These objectives serve as meta-goals guiding regulatory choice under uncertainty.

From the standpoint of uncertainty analysis, causal information relevant to regulation can be divided into two broad categories:
* information about groups or populations, and
* information about individuals or specific cases.

Each category is associated with distinct forms of uncertainty. Group-level information is typically subject to sampling error, model uncertainty, and aggregation effects, while individual-level information is more strongly influenced by measurement error, classification uncertainty, and contextual variation. These uncertainties are logically distinct, generally independent, and cumulative, contributing to the overall uncertainty faced by decision-makers.

Policy and regulatory frameworks therefore rely on structured approaches to risk assessment and uncertainty characterization to determine appropriate thresholds for action, precaution, and intervention.

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===Engineering uncertainty taxonomies===
Like scientific and economic treatments of uncertainty, engineering approaches are environment-specific, phenomenon-oriented, and model-centric. Engineering is explicitly driven by decision-making in the face of uncertainty and is concerned primarily with uncertainties that have determinable and material consequences.

Like economics, engineering conceives risk as a specific and unambiguous subset of uncertainty, characterized by the probability of occurrence and the severity of consequences, including economic, safety, and performance impacts. Engineering practice also recognizes the central role of information and knowledge in constructing and operating the built environment.

In contrast to scientific inquiry, engineering is not concerned with uncertainty for its own sake. It focuses on uncertainty insofar as it affects design choices, system performance, project execution, and stakeholder outcomes.

===Behavioral and interaction uncertainty in engineering===
In addition to epistemic and aleatory uncertainty, engineering explicitly addresses behavioral or interaction uncertainty, which arises from how individuals and organizations act or interact within projects and systems.

Behavioral uncertainty arises from several sources:
* '''Design uncertainty''', which reflects choices among alternatives under the direct control of engineers or project teams that have not yet been resolved.
* '''Requirement uncertainty''', which includes parameters determined by stakeholders and external parties, independent of the engineer or designer.
* '''Volitional uncertainty''', which concerns uncertainty about future decisions and actions of individuals or organizations, particularly in multi-party or contractual environments.
* '''Human error''', which arises from mistakes, lapses, or blunders during the development, construction, or operation of systems and projects.

These sources of behavioral uncertainty interact with technical and economic uncertainties and must be managed through engineering judgment, governance structures, contractual arrangements, and professional standards.

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==Practice frameworks for uncertainty and risk in civil engineering practice==
Professional practice frameworks in civil engineering address uncertainty indirectly through their treatment of risk, assumptions, variability, and decision-making. These frameworks differ in how explicitly they define uncertainty and in how they relate risk to underlying knowledge limitations.

===Project Management Institute Body of Knowledge (PMIBoK)===
The PMI Body of Knowledge does not provide a formal definition of uncertainty. Nevertheless, the term is used repeatedly throughout the document to describe properties affecting projects. Uncertainty is treated contextually as a condition inherent in all projects and as the underlying source of project risk.

In contrast, PMI provides an explicit definition of risk. Risk is defined as an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives. Within the PMIBoK, risk is understood as a caused condition with identifiable triggers, multiple causes, and multiple impacts. Risk conditions are contextual factors that contribute to the likelihood or impact of risk events, including stakeholder behavior and project environment.

PMI further distinguishes between potential risks and realized risks. Risks that approach certainty are no longer treated as risks but as issues requiring direct management. Risk therefore retains an implicit probabilistic character, with values strictly less than certainty.

Risk analysis within the PMIBoK emphasizes the quality of underlying information. Risk data are evaluated in terms of accuracy, reliability, integrity, and completeness. Techniques such as influence diagrams and risk impact matrices are used to map causal relationships between events and outcomes and to prioritize responses based on likelihood and consequence.

====Commentary====
PMI explicitly acknowledges that every project is conceived and planned based on assumptions, scenarios, and hypotheses. Risk identification therefore includes assessing the accuracy, stability, consistency, and completeness of those assumptions. Deficiencies in assumptions and plans are treated as indicators of project risk.

From an economic perspective, PMI embeds cost and schedule considerations within its risk framework by requiring practitioners to identify which risks may materially affect project objectives. Risk identification is an iterative process, evolving alongside scope, cost, and schedule development.

PMI also recognizes that total project risk is not merely the arithmetic sum of individual risks. Risk information has both descriptive and explanatory content. Understanding project assumptions represents project-specific knowledge, whereas identifying risks arising from assumption failure requires professional or program-level meta-knowledge.

Uncertainty exists in all projects, but risk, as a subset of uncertainty associated with material economic and performance impacts, functions as a key meta-knowledge element in civil engineering practice.

===Risk-focused software development processes===
Risk-focused development processes, such as the Unified Process, emphasize early identification and mitigation of critical risks. Project iterations are structured so that the highest-impact risks are addressed first, particularly during early phases when design flexibility is greatest.

This approach illustrates a general principle applicable to engineering projects: uncertainty reduction is most effective when it is integrated into project sequencing and decision-making rather than deferred to later stages.

===American Society of Civil Engineers (ASCE)===
ASCE does not provide an explicit definition of uncertainty in the Civil Engineering Body of Knowledge. The term is used to describe properties of engineering problems and design parameters, particularly variability and lack of determinacy.

The CE Body of Knowledge does not consistently use the paired phrase “uncertainty and risk” as found in PMI or AACE publications. In several instances, the ordering is reversed (“risk and uncertainty”), and references to risk primarily concern variation in technical parameters rather than cost, schedule, or project-level exposure.

A fundamental distinction between ASCE and PMI frameworks is that PMI treats risk as a subset of uncertainty, whereas ASCE does not explicitly articulate this relationship. ASCE usage of risk is narrower and more closely aligned with technical variability and engineering judgment than with probabilistic project risk management.

====Commentary====
The ASCE framework emphasizes the ability of engineers to recognize, analyze, and manage variability in design and performance. While uncertainty is acknowledged, it is not developed as an explicit conceptual foundation. Risk appears primarily as a technical consideration rather than as a comprehensive project or economic construct.

This emphasis reflects ASCE’s focus on professional competency and technical outcomes rather than on project governance or economic exposure.

===American Association of Cost Engineers (AACE)===
AACE provides explicit definitions of uncertainty, risk, and related terms through its Risk Management Dictionary. AACE defines uncertainty as the total range of events and conditions that may occur and affect a project, including both threats and opportunities.

Within the AACE framework, uncertainty is decomposed into identifiable components:
* '''Bias''' refers to systematic lack of objectivity arising from individual or system-level perspectives.
* '''Uncertain conditions''' are identifiable circumstances, such as labor availability or inflation, that may affect project outcomes.
* '''Uncertain events''' are specific actions or occurrences, including external actions or natural phenomena, that may or may not occur.
* '''Opportunities''' are uncertain events that could improve outcomes or increase the probability of success.
* '''Threats''' are uncertain events that could negatively affect outcomes or reduce the probability of success.

AACE characterizes risk as an ambiguous term that may refer to uncertainty as a whole, to negative subsets such as threats, or to the aggregate negative impact of multiple uncertainties.

====Commentary====
AACE’s approach emphasizes comprehensive enumeration of uncertainty sources and their economic implications. By explicitly defining bias, conditions, events, opportunities, and threats, AACE provides a granular framework for cost and schedule risk analysis.

Unlike PMI, which maintains a clearer distinction between uncertainty and risk, AACE permits broader and more flexible usage of the term risk. This flexibility reflects the needs of cost engineering practice, where uncertainty is often aggregated into financial exposure metrics.

Together, PMI, ASCE, and AACE frameworks illustrate differing professional perspectives on uncertainty and risk. Civil engineering practice must navigate these differences, applying discipline-specific judgment to integrate technical, economic, and project-level uncertainty into coherent decision-making.

==Limitations of the definitions==
Any definition of uncertainty and risk used in civil engineering practice is necessarily limited in scope and purpose. The definitions presented here do not attempt to resolve longstanding philosophical debates regarding determinism, probability, or the nature of knowledge. Instead, they are constrained by the practical requirements of professional decision-making.

Several limitations are inherent:

* Definitions of uncertainty rely on epistemic assumptions about what is known, knowable, or measurable at a given point in time. These assumptions may change as information, models, or institutional contexts evolve.
* Taxonomies of uncertainty are abstractions that simplify complex interactions among epistemic, aleatory, and behavioral sources of uncertainty. In practice, these components often interact in ways that resist clean separation.
* Risk definitions depend on the ability to partition uncertainty into bounded subsets associated with material consequences. This partitioning involves judgment and may vary across projects, disciplines, and stakeholders.
* Existing frameworks are less effective when uncertainty arises from conflicting interpretations rather than incomplete information, particularly in multi-disciplinary or contested environments.
* No single definition adequately serves scientific inquiry, economic analysis, legal decision-making, and engineering practice simultaneously without qualification.

These limitations do not invalidate the definitions. Rather, they define the conditions under which the definitions are intended to be applied and interpreted.

==Working definitions==
For the purposes of civil engineering practice, the following working definitions are proposed.

'''Uncertainty'''
Uncertainty is the condition arising from incomplete, inconsistent, or imperfect knowledge about physical, economic, or social phenomena relevant to decision-making. It reflects both a lack of information and an awareness of that lack, and it may arise from epistemic limitations, inherent variability, or behavioral factors.

Uncertainty exists on a continuum ranging from near ignorance to highly constrained and bounded conditions and is amenable to reduction, but not elimination, through disciplined inquiry, modeling, and experience.

'''Risk'''
Risk is a bounded subset of uncertainty associated with the potential for material consequences when acting or choosing in the face of uncertainty. A condition of uncertainty constitutes a risk when it can be partitioned into identifiable events, conditions, or sequences of actions for which the likelihood of occurrence and the severity of consequences can be meaningfully characterized.

Risk retains an irreducible probabilistic element and exists only where uncertainty has been sufficiently structured to support prioritization, comparison, and decision-making.

'''Hazard'''
A hazard is a source or condition with the inherent potential to cause harm or loss. A hazard becomes a risk when exposure, vulnerability, and uncertainty regarding occurrence or consequence are present and when those elements can be bounded and analyzed.

==Beneficial outcomes of structured uncertainty and risk frameworks==
Applying structured definitions of uncertainty and risk in civil engineering practice yields several practical benefits:

* Improved decision-making through explicit recognition of knowledge limitations and sources of error.
* Enhanced ability to prioritize actions by distinguishing bounded, material risks from broader background uncertainty.
* Greater transparency in communicating assumptions, models, and judgments to stakeholders, regulators, and the public.
* More effective allocation of resources toward uncertainty reduction efforts with the greatest potential impact.
* Increased consistency in integrating technical, economic, legal, and behavioral considerations across project phases.
* Stronger professional accountability through explicit articulation of the basis for decisions made under uncertainty.

These outcomes do not depend on eliminating uncertainty, but on managing it explicitly and coherently.

==See also==
Project definition
Project life cycle and phase models
Project delivery methods
Technical outcomes for civil engineering: risk and uncertainty

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==Notes==
<references group="note" />

# Uncertainty as meta-knowledge
Implicit in the definition of uncertainty as a “state” is its characterization as a subjective cognitive condition rather than a purely objective feature of the world. Uncertainty involves awareness of incomplete knowledge and therefore functions as a form of meta-knowledge, or knowing about knowing.

# Probability and coherence
Cox’s theorem establishes conditions under which degrees of belief may be represented coherently as probability measures. These conditions include comparability, consistency, and alignment with common sense reasoning. The theorem provides one justification for Bayesian probability as a framework for reasoning under uncertainty.

# Limits of existing taxonomies
The utility of many existing uncertainty taxonomies in civil engineering practice has been limited, particularly where uncertainty arises from conflicting interpretations rather than incomplete data. Surveys of scientific taxonomies demonstrate both the diversity of approaches and the difficulty of producing exhaustive classification schemes.

# Determinism and apparent randomness
Arguments that aleatory uncertainty reflects inherent randomness remain contested. Many processes treated as random may be deterministic but practically unpredictable due to sensitivity to initial conditions and incomplete information.

# Usage in professional bodies
Professional frameworks differ in their treatment of uncertainty and risk. In some bodies of knowledge, the terms are used interchangeably or inconsistently, reflecting differing disciplinary priorities rather than conceptual agreement.

Uncertainty and Risk in Civil Engineering Practice

2026-01-20T01:35:59Z

WikiSysop: 2026 01 19 /* Practice Frameworks for Uncertainty and Risk in Civil Engineering Practice */ GPT

'''Life is short and the art long; the occasion instant, experiment perilous, decision difficult.'''
(Hippocrates, as quoted in Fox)

:''See also [[Project Definition]], [[Project Life Cycle and Phase Models]], [[Project Delivery Methods]]''
:''See also [[Technical Outcomes for Civil Engineering: Risk and Uncertainty]]''

==Basic considerations==
Civil engineering practice requires decision-making in environments characterized by incomplete, imperfect, and evolving knowledge. Engineers routinely act in the presence of uncertainty related to physical phenomena, economic constraints, regulatory requirements, and human behavior. The professional problem is not the elimination of uncertainty, which is impossible in practice, but its reduction, partitioning, and management to a degree sufficient to support responsible action.

In this chapter, '''risk''' is treated as a bounded and decision-relevant subset of broader uncertainty. Uncertainty that can be reduced, structured, prioritized, and acted upon using professional knowledge, models, and experience is termed risk. Related but distinct is the concept of '''hazard''', which denotes a source or condition with the potential to cause harm, independent of likelihood or consequence.

This framing aligns with interdisciplinary treatments of risk analysis, which emphasize the role of uncertainty in shaping social, technical, and governance systems over long historical periods. McDaniels and Small describe risk analysis as emerging from the need to act despite imperfect knowledge and argue that societies have progressively shifted from fatalistic acceptance of uncertainty toward its active management through structured analysis and decision frameworks.<ref name="McDaniels">McDaniels, Timothy, and Mitchell Small. ''Risk Analysis and Society: An Interdisciplinary Characterization of the Field''. Cambridge University Press, 2004, p. 1.</ref>

Within civil engineering, this evolution has produced specialized practices focused on managing uncertainty through modeling, design standards, safety factors, reliability analysis, and project controls. These practices rely not only on domain knowledge of materials, loads, and systems, but also on '''meta-knowledge''': knowledge about the limitations, reliability, and appropriate use of engineering knowledge itself.

==Semantic, epistemic, and logical frameworks==
Clear terminology is a prerequisite for coherent risk analysis. In professional practice, inconsistent or interchangeable use of terms such as uncertainty, risk, hazard, error, and bias can obscure decision responsibility and complicate communication among stakeholders.

The semantic framework adopted in this chapter proceeds in stages:
# distinction between certainty and uncertainty;
# refinement of uncertainty into epistemic, aleatory, and behavioral components;
# use of error and bias as operational proxies for uncertainty;
# transition from general uncertainty to discipline-specific risk taxonomies.

Beyond this point, the discussion shifts from semantics to logical frameworks, where uncertainty is partitioned into structured taxonomies suitable for analysis, communication, and decision-making in engineering projects.

==Semantics framework==

===Certainty and uncertainty===
Certainty is commonly described as an epistemic property of belief, implying immunity from doubt or criticism. In practical contexts, however, absolute certainty is neither attainable nor desirable. As Mill observed, assurance sufficient for the purposes of human life does not require freedom from all doubt, but rather justified confidence grounded in experience and evidence.

Uncertainty arises whenever beliefs or propositions are subject to doubt, criticism, or incomplete support. Contemporary treatments emphasize that uncertainty is not merely a feature of the external world, but also a cognitive condition reflecting awareness of incomplete knowledge. Han et al. characterize uncertainty as a meta-cognitive state: knowing that one does not know, or knowing that one’s knowledge may be incomplete or unreliable.<ref name="Han">Han, Paul K. J., William M. P. Klein, and Neeraj K. Arora. “Varieties of Uncertainty in Health Care: A Conceptual Taxonomy.” ''Medical Decision Making'' 31, no. 6 (2011): 828–838.</ref>

In professional reasoning, uncertainty manifests as the potential for error in observation, modeling, judgment, and action. Reasoning under uncertainty therefore differs fundamentally from reasoning under certainty: conclusions are provisional, contingent, and subject to revision as new information becomes available.

===Error, bias, and uncertainty===
In applied disciplines, uncertainty is rarely managed directly. Instead, it is addressed through its observable proxies: error and bias. Error refers to deviation between an estimate, measurement, or prediction and a reference value or observed outcome. Bias denotes systematic or directional error arising from model structure, data selection, judgment, or institutional practice.

Error can often be quantified, propagated, and reduced using analytical and statistical methods. Bias is more difficult to detect and correct, frequently requiring theoretical analysis, validation studies, or comparison across independent methods. The management of uncertainty in civil engineering therefore depends critically on understanding how error and bias arise, how they propagate through sequential decisions, and how they can be bounded through professional controls.

===Propagation of error and uncertainty===
Engineering decisions typically involve multi-step processes: observation, interpretation, modeling, design, construction, and operation. Errors introduced early in this chain can propagate and amplify downstream, while later errors may have limited impact depending on system sensitivity and redundancy.

Professional practice therefore emphasizes identifying where uncertainty enters the decision sequence and prioritizing error and bias reduction where it has the greatest effect on outcomes. This ordering principle underlies practices such as conservative design assumptions, staged investigations, peer review, and independent verification.

===Epistemic, aleatory, and behavioral uncertainty===
For practical purposes, uncertainty in civil engineering can be partitioned into three broad components:
* '''Epistemic uncertainty''', arising from incomplete knowledge, limited data, or imperfect models;
* '''Aleatory uncertainty''', reflecting inherent variability in physical processes;
* '''Behavioral uncertainty''', associated with human judgment, decision-making, and organizational interaction.

This partitioning supports targeted mitigation strategies. Epistemic uncertainty may be reduced through investigation and analysis; aleatory uncertainty may be managed through design margins and reliability methods; behavioral uncertainty requires institutional controls, contractual clarity, and governance mechanisms.

==From uncertainty to risk and hazard==
Risk and hazard are not synonymous with uncertainty, but structured subsets of it. Risk refers to uncertainty that has been framed in terms of potential consequences and that is sufficiently bounded to support decision-making. Hazard denotes a source or condition with the potential to cause harm, independent of likelihood.

In this chapter, '''general risk''' is defined as:
:'''a bounded subset of uncertainty associated with the potential for material error or bias when acting or choosing in physical, economic, or social environments, and that can be represented using coherent analytical or probabilistic frameworks and subjected to testing or validation.'''

This definition is discipline-neutral and applies across engineering, economics, medicine, and policy. Subsequent sections introduce discipline-specific taxonomies that adapt this general concept to the particular decision contexts of civil engineering projects.

==Logical framework for specific risk taxonomies==
Once uncertainty has been reduced to bounded, decision-relevant forms, it becomes possible to develop taxonomies that support analysis, communication, and management. Taxonomies are not required to be epistemically complete; rather, they must be logically distinct, practically exhaustive for their purpose, and aligned with decision needs.

The following sections examine how scientific, legal, economic, and engineering disciplines construct uncertainty and risk taxonomies, and how these perspectives inform civil engineering practice.

[[#top|Top of current page]]
===Scientific risk taxonomies===
Scientific inquiry proceeds by observing phenomena, proposing explanatory models, and testing those models against experience. At every stage of this process, uncertainty is present. Scientific risk taxonomies arise from the need to identify, distinguish, and manage different sources of uncertainty that affect observation, modeling, inference, and prediction.

Most scientific taxonomies of uncertainty have focused on uncertainties associated with data, parameters, and models, particularly those that can be represented statistically. While multiple taxonomies are possible, a common organizing principle is that uncertainty is environment-specific, phenomenon-oriented, and model-centric.

====Environment-specific, phenomenon-oriented, model-centric taxonomy of uncertainty====
A widely used scientific taxonomy distinguishes uncertainty according to its relationship to models and data:

* '''Parameter uncertainty'''
Parameter uncertainty arises from uncertainty in the numerical values of model inputs. These parameters may be physically unobservable, indirectly inferred, or only partially constrained by available data.
** '''Experimental (observational) uncertainty''' results from variability in repeated measurements under nominally identical conditions.
** '''Parametric variability''' reflects natural or contextual variability in input variables that are treated as fixed values within a model.

* '''Structural uncertainty'''
Structural uncertainty, also referred to as model inadequacy or model discrepancy, arises when a model fails to capture relevant features of the underlying phenomenon. Because all models are simplifications of reality, structural uncertainty is unavoidable and depends on model assumptions, scope, and fitness for purpose.
** Structural uncertainty is distinct from parameter uncertainty and cannot be reduced solely by collecting additional data.
** Inadequate model structure can lead to systematically misleading conclusions even when parameter estimates appear precise.

* '''Algorithmic (numerical) uncertainty'''
Algorithmic uncertainty arises from numerical approximations used to implement models computationally. Examples include discretization error, convergence error, and truncation error associated with numerical solution methods such as finite difference or finite element approximations.
** These uncertainties depend**

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==Uncertainty taxonomies in a legal environment==
Legal decision-making operates under conditions of uncertainty that differ in purpose and structure from those of science or engineering. From a legal perspective, a taxonomy is a systematic scheme for distinguishing, ordering, and naming types within a subject field. Such taxonomies are not required to be epistemically complete; rather, they must be sufficient to organize the kinds of descriptive information commonly encountered by decision-makers and to support fair, effective, and defensible judgments.

Walker characterizes legal taxonomies of scientific and technical uncertainty as tools for cataloging uncertainty associated with descriptive assertions, particularly those involving cause-and-effect claims. These taxonomies emphasize clarity, logical distinction, and practical exhaustiveness over theoretical completeness.

===Core components of legal uncertainty===
Legal frameworks distinguish multiple, logically distinct sources of uncertainty that may affect descriptive assertions and decisions:

* '''Linguistic uncertainty'''
Linguistic uncertainty, often described as vagueness, arises from limitations of language rather than from data or models.
** '''Context dependence''' occurs when the applicability of a statement depends on unstated or shifting contextual conditions.
** '''Ambiguity''' arises when terms admit multiple meanings that cannot be resolved within the given context.
** '''Underspecification''' occurs when descriptions lack sufficient detail to support a unique interpretation.
Linguistic uncertainty underlies all other components of legal uncertainty, as it affects how evidence, concepts, and models are interpreted.

* '''Conceptual uncertainty'''
Conceptual uncertainty arises from the choice and definition of concepts used to describe phenomena. Whenever predicates are selected to characterize an object, event, or condition, alternative conceptualizations are excluded.
Conceptualization generates useful structure, but it can also constrain understanding by privileging certain interpretations over others. Poorly chosen or inadequately defined concepts can therefore introduce systematic distortion into legal and technical reasoning.

Conceptual uncertainty is closely related to issues of validity. A concept is valid to the extent that it accurately represents the phenomenon it is intended to describe. Conceptual bias, like systematic error in measurement, is difficult to detect and often recognizable only through theoretical analysis or comparison across alternative frameworks.

* '''Measurement and classification uncertainty'''
Measurement uncertainty arises when concepts are applied to specific cases through observation, assessment, or classification. Even when conceptual uncertainty has been minimized, misclassification may occur due to procedural limitations, observer judgment, or data quality.
Measurement uncertainty affects the reliability and precision of descriptive assertions. Errors at this stage can propagate through subsequent reasoning and reduce the quality of causal inference or decision outcomes.

* '''Sampling uncertainty'''
Sampling uncertainty arises when conclusions about a population are inferred from a limited subset of observations. The uncertainty reflects the extent to which the sample represents the relevant population.
In legal and regulatory contexts, sampling uncertainty affects the credibility of generalizations, forecasts, and risk characterizations based on empirical evidence.

* '''Modeling uncertainty'''
Modeling uncertainty arises when descriptive or causal claims rely on simplified representations of complex systems. This includes uncertainty associated with variable selection, functional relationships, and parameter specification.
** '''Causal uncertainty''' arises when relationships between variables are inferred rather than directly observed. Legal decision-makers often rely on causal models to explain why an outcome occurred or to predict the consequences of alternative actions.
** '''Epistemic choice''' refers to uncertainty introduced by selecting among competing interpretive frameworks or explanatory models.

===Aggregation and interaction of uncertainties===
The components of legal uncertainty do not operate independently. Linguistic, conceptual, measurement, sampling, and modeling uncertainties can combine in different ways to produce aggregate uncertainty affecting the credibility, relevance, and weight of evidence.

Legal taxonomies therefore focus not on eliminating uncertainty, but on making its sources explicit so that decision-makers can assess reliability, assign weight, and determine appropriate standards of proof or precaution.

===Relationship to scientific and engineering practice===
While legal taxonomies do not require quantitative completeness, they depend critically on validated scientific and engineering methods to support descriptive and causal claims. Scientific and engineering disciplines provide procedures and models capable of integrating qualitative and quantitative information into structured representations of uncertainty.

However, the legal use of such information remains distinct. The objective is not optimization or prediction per se, but defensible judgment under uncertainty. Validation, transparency, and clarity of assumptions are therefore central concerns at the interface between legal, scientific, and engineering domains.

These distinctions inform how uncertainty is framed, communicated, and relied upon in civil engineering projects subject to regulatory review, contractual interpretation, or dispute resolution.

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===Economics uncertainty taxonomies===
Like scientific and engineering treatments of uncertainty, economic approaches are environment-specific, phenomenon-oriented, and model-centric. In economics, the relevant environment is the market or sector of economic activity, and the phenomena of interest are human actions, organizational behavior, and manufactured constructs such as firms, contracts, and markets.

Economics does not address all forms of uncertainty. Instead, it focuses on subsets of uncertainty that possess causal connections to material outcomes, particularly those affecting prices, profits, losses, and allocation of resources. These subsets are commonly labeled as risks.

A foundational contribution to the economic treatment of uncertainty is associated with Frank Knight, who distinguished between measurable risk and unmeasurable uncertainty. Knight characterized uncertainty as arising from defects or limitations in knowledge, particularly managerial and organizational knowledge, rather than from inherent randomness alone. In this framework, risk refers to situations in which probabilities can be assigned to outcomes, while uncertainty refers to situations in which such assignment is not possible.

Knight defined risk as the class of uncertainties that can be reduced or managed through probabilistic reasoning and mechanisms such as insurance. By contrast, uncertainty in the strict sense reflects imperfect knowledge that cannot be reduced to numerical probabilities. For Knight, measuring uncertainty involved assessing both the likelihood of occurrence and the severity of economic consequences, while recognizing that some consequences remain fundamentally indeterminate.

Knight emphasized that economic decision-making is rarely conducted under conditions of complete ignorance or perfect information. Instead, action is typically based on partial knowledge, judgment, and opinion formed under uncertainty. Economic behavior therefore reflects degrees of belief and confidence rather than certainty, and the practical problem lies in managing variation in knowledge rather than eliminating uncertainty altogether.

In his analysis of insurance markets, Knight illustrated the difficulty of classifying certain uncertainties. Events such as accidental death could be treated probabilistically, while sickness or business failure resisted objective classification due to heterogeneity, judgment, and contextual dependence. Uncertainties that could not be classified or grouped were therefore not insurable and remained outside the domain of measurable risk.

===Core distinctions in economic uncertainty===
Economic treatments of uncertainty commonly rely on a small number of foundational distinctions that shape both theory and practice.

* Uncertainty about the economic environment, including prices, demand, technology, and external conditions, is distinct from uncertainty about the behavior or choices of other actors.
* Risk is a subset of uncertainty in which the decision-maker can enumerate possible actions, identify possible outcomes arising from each action, and assign probabilities to those outcomes.
* Uncertainty arises when such enumeration or probability assignment is not feasible due to incomplete knowledge, structural change, heterogeneity, or reliance on judgment.

These distinctions form an idealized classification that has been influential in economic theory, despite remaining philosophically and empirically contested. They nevertheless provide a useful framework for distinguishing calculable exposure from judgment-dependent uncertainty in economic analysis.

===Uncertainty, knowledge, and economic models===
Like the theory of the firm, an economic theory of uncertainty seeks to explain and predict the behavior of specific subsets of uncertainty that affect material economic outcomes. These theories examine how the presence, distribution, and variability of knowledge influence decision-making, pricing, and allocation of resources.

For analytical convenience, many economic models assume certainty or perfect information. The economics of uncertainty exists as an alternative framework that explicitly incorporates imperfect information, unknown preferences, and judgment-based decisions when doing so improves explanatory or predictive power.

Unlike physical systems, economic activity produces artificial constructs such as firms, contracts, markets, financial instruments, and institutions. Uncertainty in economics therefore arises not only from external conditions but also from institutional design, incentives, strategic interaction, and information asymmetry.

===Risk, hazard, and moral hazard===
Economic analysis distinguishes between risk and hazard. While the terminology is not always used consistently, the distinction becomes most explicit in the concept of moral hazard.

Moral hazard arises when the presence of insurance, guarantees, or protection alters behavior, leading actors to take actions that increase exposure to loss. In such cases, uncertainty is not merely a property of the environment but is generated endogenously through behavior and information asymmetry.

This distinction is particularly relevant for engineering projects involving insurance, contracting, public–private partnerships, and regulatory oversight, where incentives and information structure influence both technical and economic outcomes.

In summary, economic uncertainty taxonomies emphasize the role of partial knowledge, judgment, and incentives in shaping material outcomes. These frameworks inform how risk is identified, priced, transferred, or retained and provide an essential foundation for understanding cost, schedule, and financial uncertainty in civil engineering practice.

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===Uncertainty and risk in policy and regulatory environment===
Decision-making in policy and regulatory contexts must proceed under conditions of incomplete and potentially inaccurate information. The central problem is not eliminating uncertainty, but selecting decision rules that remain defensible in its presence.

As a social enterprise, risk regulation, regardless of substantive objectives, is commonly evaluated against three process objectives: effectiveness, efficiency, and equity. These objectives serve as meta-goals guiding regulatory choice under uncertainty.

From the standpoint of uncertainty analysis, causal information relevant to regulation can be divided into two broad categories:
* information about groups or populations, and
* information about individuals or specific cases.

Each category is associated with distinct forms of uncertainty. Group-level information is typically subject to sampling error, model uncertainty, and aggregation effects, while individual-level information is more strongly influenced by measurement error, classification uncertainty, and contextual variation. These uncertainties are logically distinct, generally independent, and cumulative, contributing to the overall uncertainty faced by decision-makers.

Policy and regulatory frameworks therefore rely on structured approaches to risk assessment and uncertainty characterization to determine appropriate thresholds for action, precaution, and intervention.

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===Engineering uncertainty taxonomies===
Like scientific and economic treatments of uncertainty, engineering approaches are environment-specific, phenomenon-oriented, and model-centric. Engineering is explicitly driven by decision-making in the face of uncertainty and is concerned primarily with uncertainties that have determinable and material consequences.

Like economics, engineering conceives risk as a specific and unambiguous subset of uncertainty, characterized by the probability of occurrence and the severity of consequences, including economic, safety, and performance impacts. Engineering practice also recognizes the central role of information and knowledge in constructing and operating the built environment.

In contrast to scientific inquiry, engineering is not concerned with uncertainty for its own sake. It focuses on uncertainty insofar as it affects design choices, system performance, project execution, and stakeholder outcomes.

===Behavioral and interaction uncertainty in engineering===
In addition to epistemic and aleatory uncertainty, engineering explicitly addresses behavioral or interaction uncertainty, which arises from how individuals and organizations act or interact within projects and systems.

Behavioral uncertainty arises from several sources:
* '''Design uncertainty''', which reflects choices among alternatives under the direct control of engineers or project teams that have not yet been resolved.
* '''Requirement uncertainty''', which includes parameters determined by stakeholders and external parties, independent of the engineer or designer.
* '''Volitional uncertainty''', which concerns uncertainty about future decisions and actions of individuals or organizations, particularly in multi-party or contractual environments.
* '''Human error''', which arises from mistakes, lapses, or blunders during the development, construction, or operation of systems and projects.

These sources of behavioral uncertainty interact with technical and economic uncertainties and must be managed through engineering judgment, governance structures, contractual arrangements, and professional standards.

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==Practice frameworks for uncertainty and risk in civil engineering practice==
Professional practice frameworks in civil engineering address uncertainty indirectly through their treatment of risk, assumptions, variability, and decision-making. These frameworks differ in how explicitly they define uncertainty and in how they relate risk to underlying knowledge limitations.

===Project Management Institute Body of Knowledge (PMIBoK)===
The PMI Body of Knowledge does not provide a formal definition of uncertainty. Nevertheless, the term is used repeatedly throughout the document to describe properties affecting projects. Uncertainty is treated contextually as a condition inherent in all projects and as the underlying source of project risk.

In contrast, PMI provides an explicit definition of risk. Risk is defined as an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives. Within the PMIBoK, risk is understood as a caused condition with identifiable triggers, multiple causes, and multiple impacts. Risk conditions are contextual factors that contribute to the likelihood or impact of risk events, including stakeholder behavior and project environment.

PMI further distinguishes between potential risks and realized risks. Risks that approach certainty are no longer treated as risks but as issues requiring direct management. Risk therefore retains an implicit probabilistic character, with values strictly less than certainty.

Risk analysis within the PMIBoK emphasizes the quality of underlying information. Risk data are evaluated in terms of accuracy, reliability, integrity, and completeness. Techniques such as influence diagrams and risk impact matrices are used to map causal relationships between events and outcomes and to prioritize responses based on likelihood and consequence.

====Commentary====
PMI explicitly acknowledges that every project is conceived and planned based on assumptions, scenarios, and hypotheses. Risk identification therefore includes assessing the accuracy, stability, consistency, and completeness of those assumptions. Deficiencies in assumptions and plans are treated as indicators of project risk.

From an economic perspective, PMI embeds cost and schedule considerations within its risk framework by requiring practitioners to identify which risks may materially affect project objectives. Risk identification is an iterative process, evolving alongside scope, cost, and schedule development.

PMI also recognizes that total project risk is not merely the arithmetic sum of individual risks. Risk information has both descriptive and explanatory content. Understanding project assumptions represents project-specific knowledge, whereas identifying risks arising from assumption failure requires professional or program-level meta-knowledge.

Uncertainty exists in all projects, but risk, as a subset of uncertainty associated with material economic and performance impacts, functions as a key meta-knowledge element in civil engineering practice.

===Risk-focused software development processes===
Risk-focused development processes, such as the Unified Process, emphasize early identification and mitigation of critical risks. Project iterations are structured so that the highest-impact risks are addressed first, particularly during early phases when design flexibility is greatest.

This approach illustrates a general principle applicable to engineering projects: uncertainty reduction is most effective when it is integrated into project sequencing and decision-making rather than deferred to later stages.

===American Society of Civil Engineers (ASCE)===
ASCE does not provide an explicit definition of uncertainty in the Civil Engineering Body of Knowledge. The term is used to describe properties of engineering problems and design parameters, particularly variability and lack of determinacy.

The CE Body of Knowledge does not consistently use the paired phrase “uncertainty and risk” as found in PMI or AACE publications. In several instances, the ordering is reversed (“risk and uncertainty”), and references to risk primarily concern variation in technical parameters rather than cost, schedule, or project-level exposure.

A fundamental distinction between ASCE and PMI frameworks is that PMI treats risk as a subset of uncertainty, whereas ASCE does not explicitly articulate this relationship. ASCE usage of risk is narrower and more closely aligned with technical variability and engineering judgment than with probabilistic project risk management.

====Commentary====
The ASCE framework emphasizes the ability of engineers to recognize, analyze, and manage variability in design and performance. While uncertainty is acknowledged, it is not developed as an explicit conceptual foundation. Risk appears primarily as a technical consideration rather than as a comprehensive project or economic construct.

This emphasis reflects ASCE’s focus on professional competency and technical outcomes rather than on project governance or economic exposure.

===American Association of Cost Engineers (AACE)===
AACE provides explicit definitions of uncertainty, risk, and related terms through its Risk Management Dictionary. AACE defines uncertainty as the total range of events and conditions that may occur and affect a project, including both threats and opportunities.

Within the AACE framework, uncertainty is decomposed into identifiable components:
* '''Bias''' refers to systematic lack of objectivity arising from individual or system-level perspectives.
* '''Uncertain conditions''' are identifiable circumstances, such as labor availability or inflation, that may affect project outcomes.
* '''Uncertain events''' are specific actions or occurrences, including external actions or natural phenomena, that may or may not occur.
* '''Opportunities''' are uncertain events that could improve outcomes or increase the probability of success.
* '''Threats''' are uncertain events that could negatively affect outcomes or reduce the probability of success.

AACE characterizes risk as an ambiguous term that may refer to uncertainty as a whole, to negative subsets such as threats, or to the aggregate negative impact of multiple uncertainties.

====Commentary====
AACE’s approach emphasizes comprehensive enumeration of uncertainty sources and their economic implications. By explicitly defining bias, conditions, events, opportunities, and threats, AACE provides a granular framework for cost and schedule risk analysis.

Unlike PMI, which maintains a clearer distinction between uncertainty and risk, AACE permits broader and more flexible usage of the term risk. This flexibility reflects the needs of cost engineering practice, where uncertainty is often aggregated into financial exposure metrics.

Together, PMI, ASCE, and AACE frameworks illustrate differing professional perspectives on uncertainty and risk. Civil engineering practice must navigate these differences, applying discipline-specific judgment to integrate technical, economic, and project-level uncertainty into coherent decision-making.

==Limitations of the definition==
...
==Working definition==
...
==Beneficial outcomes==
...
==See also==
Wiki articles on the project.

[[#top|Top of current page]]
==Notes==
<references group="note" />
<li> Implicit in this definition of uncertainty as a "state of' is ... </li>
: " ... a conceptualization of uncertainty as a subjective, cognitive experience of people--a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one's lack of knowledge, without which one could not feel uncertain; '''uncertainty is a form of "meta-cognition" ... ( or alternatively, its main component, meta-knowledge) ... -a knowing about knowing.'''" (Han, 2011, op. cit., Emphasis added)
</ol>
<ol start="5">
<li> Cox wanted his system to satisfy the following conditions:</li>
: Divisibility and comparability- The plausibility of a statement is a real number and depends on the information we have related to the statement.
: Common sense - Plausibilities should vary sensibly with the assessment of plausibilities in the model.
: Consistency - If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). -preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf
</ol>
<ol start="6">
<li> The utility of existing taxonomies of uncertainty in civil engineering and, by extension, "risk" has been unsatisfactory for this very reason. This is particularly true in cases where the source of the uncertainty is conflicting versus incomplete information. See Regan, et. al.,"A taxonomy and treatment of uncertainty for ecology and conservation biology" (2002)
for a survey of scientific taxonomies for uncertainty.</li>
<li> Regan et al. argue that " ... genuine examples of this kind of uncertainty are hard to find. Even classic cases of random experiments like coin tosses and the throwing of dice are deterministic; it is just that we do not have enough information about the dynamic processes and initial conditions to make any sensible estimates about the outcomes. Such processes are, for all intents and purposes, inherently random, but they are not genuinely inherently random. For similar reasons, complex systems such as ecosystems and weather patterns are unlikely to be inherently random. Similarly, chaotic systems are entirely deterministic. They are unpredictable because the deterministic processes generating them and the relevant initial conditions are hard
to fully specify (see Stewart 1989, Sugihara et al. 1990)." (Regan (2002) </li>
<li> The CEBoK uses the phrase "uncertainty and risk" twice in reference to scheduling (Section 6.5,2) and cost estimating (Section 7.2,2) as well as reversing the two (Risk and Uncertainty) also in two places in discussing project life cycle (both in Section 2.4.1) </li>
</ol>

==References==
<references />

Uncertainty and Risk in Civil Engineering Practice

2026-01-20T01:33:35Z

WikiSysop: 2026 01 19 GPT recommended edits

'''Life is short and the art long; the occasion instant, experiment perilous, decision difficult.'''
(Hippocrates, as quoted in Fox)

:''See also [[Project Definition]], [[Project Life Cycle and Phase Models]], [[Project Delivery Methods]]''
:''See also [[Technical Outcomes for Civil Engineering: Risk and Uncertainty]]''

==Basic considerations==
Civil engineering practice requires decision-making in environments characterized by incomplete, imperfect, and evolving knowledge. Engineers routinely act in the presence of uncertainty related to physical phenomena, economic constraints, regulatory requirements, and human behavior. The professional problem is not the elimination of uncertainty, which is impossible in practice, but its reduction, partitioning, and management to a degree sufficient to support responsible action.

In this chapter, '''risk''' is treated as a bounded and decision-relevant subset of broader uncertainty. Uncertainty that can be reduced, structured, prioritized, and acted upon using professional knowledge, models, and experience is termed risk. Related but distinct is the concept of '''hazard''', which denotes a source or condition with the potential to cause harm, independent of likelihood or consequence.

This framing aligns with interdisciplinary treatments of risk analysis, which emphasize the role of uncertainty in shaping social, technical, and governance systems over long historical periods. McDaniels and Small describe risk analysis as emerging from the need to act despite imperfect knowledge and argue that societies have progressively shifted from fatalistic acceptance of uncertainty toward its active management through structured analysis and decision frameworks.<ref name="McDaniels">McDaniels, Timothy, and Mitchell Small. ''Risk Analysis and Society: An Interdisciplinary Characterization of the Field''. Cambridge University Press, 2004, p. 1.</ref>

Within civil engineering, this evolution has produced specialized practices focused on managing uncertainty through modeling, design standards, safety factors, reliability analysis, and project controls. These practices rely not only on domain knowledge of materials, loads, and systems, but also on '''meta-knowledge''': knowledge about the limitations, reliability, and appropriate use of engineering knowledge itself.

==Semantic, epistemic, and logical frameworks==
Clear terminology is a prerequisite for coherent risk analysis. In professional practice, inconsistent or interchangeable use of terms such as uncertainty, risk, hazard, error, and bias can obscure decision responsibility and complicate communication among stakeholders.

The semantic framework adopted in this chapter proceeds in stages:
# distinction between certainty and uncertainty;
# refinement of uncertainty into epistemic, aleatory, and behavioral components;
# use of error and bias as operational proxies for uncertainty;
# transition from general uncertainty to discipline-specific risk taxonomies.

Beyond this point, the discussion shifts from semantics to logical frameworks, where uncertainty is partitioned into structured taxonomies suitable for analysis, communication, and decision-making in engineering projects.

==Semantics framework==

===Certainty and uncertainty===
Certainty is commonly described as an epistemic property of belief, implying immunity from doubt or criticism. In practical contexts, however, absolute certainty is neither attainable nor desirable. As Mill observed, assurance sufficient for the purposes of human life does not require freedom from all doubt, but rather justified confidence grounded in experience and evidence.

Uncertainty arises whenever beliefs or propositions are subject to doubt, criticism, or incomplete support. Contemporary treatments emphasize that uncertainty is not merely a feature of the external world, but also a cognitive condition reflecting awareness of incomplete knowledge. Han et al. characterize uncertainty as a meta-cognitive state: knowing that one does not know, or knowing that one’s knowledge may be incomplete or unreliable.<ref name="Han">Han, Paul K. J., William M. P. Klein, and Neeraj K. Arora. “Varieties of Uncertainty in Health Care: A Conceptual Taxonomy.” ''Medical Decision Making'' 31, no. 6 (2011): 828–838.</ref>

In professional reasoning, uncertainty manifests as the potential for error in observation, modeling, judgment, and action. Reasoning under uncertainty therefore differs fundamentally from reasoning under certainty: conclusions are provisional, contingent, and subject to revision as new information becomes available.

===Error, bias, and uncertainty===
In applied disciplines, uncertainty is rarely managed directly. Instead, it is addressed through its observable proxies: error and bias. Error refers to deviation between an estimate, measurement, or prediction and a reference value or observed outcome. Bias denotes systematic or directional error arising from model structure, data selection, judgment, or institutional practice.

Error can often be quantified, propagated, and reduced using analytical and statistical methods. Bias is more difficult to detect and correct, frequently requiring theoretical analysis, validation studies, or comparison across independent methods. The management of uncertainty in civil engineering therefore depends critically on understanding how error and bias arise, how they propagate through sequential decisions, and how they can be bounded through professional controls.

===Propagation of error and uncertainty===
Engineering decisions typically involve multi-step processes: observation, interpretation, modeling, design, construction, and operation. Errors introduced early in this chain can propagate and amplify downstream, while later errors may have limited impact depending on system sensitivity and redundancy.

Professional practice therefore emphasizes identifying where uncertainty enters the decision sequence and prioritizing error and bias reduction where it has the greatest effect on outcomes. This ordering principle underlies practices such as conservative design assumptions, staged investigations, peer review, and independent verification.

===Epistemic, aleatory, and behavioral uncertainty===
For practical purposes, uncertainty in civil engineering can be partitioned into three broad components:
* '''Epistemic uncertainty''', arising from incomplete knowledge, limited data, or imperfect models;
* '''Aleatory uncertainty''', reflecting inherent variability in physical processes;
* '''Behavioral uncertainty''', associated with human judgment, decision-making, and organizational interaction.

This partitioning supports targeted mitigation strategies. Epistemic uncertainty may be reduced through investigation and analysis; aleatory uncertainty may be managed through design margins and reliability methods; behavioral uncertainty requires institutional controls, contractual clarity, and governance mechanisms.

==From uncertainty to risk and hazard==
Risk and hazard are not synonymous with uncertainty, but structured subsets of it. Risk refers to uncertainty that has been framed in terms of potential consequences and that is sufficiently bounded to support decision-making. Hazard denotes a source or condition with the potential to cause harm, independent of likelihood.

In this chapter, '''general risk''' is defined as:
:'''a bounded subset of uncertainty associated with the potential for material error or bias when acting or choosing in physical, economic, or social environments, and that can be represented using coherent analytical or probabilistic frameworks and subjected to testing or validation.'''

This definition is discipline-neutral and applies across engineering, economics, medicine, and policy. Subsequent sections introduce discipline-specific taxonomies that adapt this general concept to the particular decision contexts of civil engineering projects.

==Logical framework for specific risk taxonomies==
Once uncertainty has been reduced to bounded, decision-relevant forms, it becomes possible to develop taxonomies that support analysis, communication, and management. Taxonomies are not required to be epistemically complete; rather, they must be logically distinct, practically exhaustive for their purpose, and aligned with decision needs.

The following sections examine how scientific, legal, economic, and engineering disciplines construct uncertainty and risk taxonomies, and how these perspectives inform civil engineering practice.

[[#top|Top of current page]]
===Scientific risk taxonomies===
Scientific inquiry proceeds by observing phenomena, proposing explanatory models, and testing those models against experience. At every stage of this process, uncertainty is present. Scientific risk taxonomies arise from the need to identify, distinguish, and manage different sources of uncertainty that affect observation, modeling, inference, and prediction.

Most scientific taxonomies of uncertainty have focused on uncertainties associated with data, parameters, and models, particularly those that can be represented statistically. While multiple taxonomies are possible, a common organizing principle is that uncertainty is environment-specific, phenomenon-oriented, and model-centric.

====Environment-specific, phenomenon-oriented, model-centric taxonomy of uncertainty====
A widely used scientific taxonomy distinguishes uncertainty according to its relationship to models and data:

* '''Parameter uncertainty'''
Parameter uncertainty arises from uncertainty in the numerical values of model inputs. These parameters may be physically unobservable, indirectly inferred, or only partially constrained by available data.
** '''Experimental (observational) uncertainty''' results from variability in repeated measurements under nominally identical conditions.
** '''Parametric variability''' reflects natural or contextual variability in input variables that are treated as fixed values within a model.

* '''Structural uncertainty'''
Structural uncertainty, also referred to as model inadequacy or model discrepancy, arises when a model fails to capture relevant features of the underlying phenomenon. Because all models are simplifications of reality, structural uncertainty is unavoidable and depends on model assumptions, scope, and fitness for purpose.
** Structural uncertainty is distinct from parameter uncertainty and cannot be reduced solely by collecting additional data.
** Inadequate model structure can lead to systematically misleading conclusions even when parameter estimates appear precise.

* '''Algorithmic (numerical) uncertainty'''
Algorithmic uncertainty arises from numerical approximations used to implement models computationally. Examples include discretization error, convergence error, and truncation error associated with numerical solution methods such as finite difference or finite element approximations.
** These uncertainties depend**

[[#top|Top of current page]]

==Uncertainty taxonomies in a legal environment==
Legal decision-making operates under conditions of uncertainty that differ in purpose and structure from those of science or engineering. From a legal perspective, a taxonomy is a systematic scheme for distinguishing, ordering, and naming types within a subject field. Such taxonomies are not required to be epistemically complete; rather, they must be sufficient to organize the kinds of descriptive information commonly encountered by decision-makers and to support fair, effective, and defensible judgments.

Walker characterizes legal taxonomies of scientific and technical uncertainty as tools for cataloging uncertainty associated with descriptive assertions, particularly those involving cause-and-effect claims. These taxonomies emphasize clarity, logical distinction, and practical exhaustiveness over theoretical completeness.

===Core components of legal uncertainty===
Legal frameworks distinguish multiple, logically distinct sources of uncertainty that may affect descriptive assertions and decisions:

* '''Linguistic uncertainty'''
Linguistic uncertainty, often described as vagueness, arises from limitations of language rather than from data or models.
** '''Context dependence''' occurs when the applicability of a statement depends on unstated or shifting contextual conditions.
** '''Ambiguity''' arises when terms admit multiple meanings that cannot be resolved within the given context.
** '''Underspecification''' occurs when descriptions lack sufficient detail to support a unique interpretation.
Linguistic uncertainty underlies all other components of legal uncertainty, as it affects how evidence, concepts, and models are interpreted.

* '''Conceptual uncertainty'''
Conceptual uncertainty arises from the choice and definition of concepts used to describe phenomena. Whenever predicates are selected to characterize an object, event, or condition, alternative conceptualizations are excluded.
Conceptualization generates useful structure, but it can also constrain understanding by privileging certain interpretations over others. Poorly chosen or inadequately defined concepts can therefore introduce systematic distortion into legal and technical reasoning.

Conceptual uncertainty is closely related to issues of validity. A concept is valid to the extent that it accurately represents the phenomenon it is intended to describe. Conceptual bias, like systematic error in measurement, is difficult to detect and often recognizable only through theoretical analysis or comparison across alternative frameworks.

* '''Measurement and classification uncertainty'''
Measurement uncertainty arises when concepts are applied to specific cases through observation, assessment, or classification. Even when conceptual uncertainty has been minimized, misclassification may occur due to procedural limitations, observer judgment, or data quality.
Measurement uncertainty affects the reliability and precision of descriptive assertions. Errors at this stage can propagate through subsequent reasoning and reduce the quality of causal inference or decision outcomes.

* '''Sampling uncertainty'''
Sampling uncertainty arises when conclusions about a population are inferred from a limited subset of observations. The uncertainty reflects the extent to which the sample represents the relevant population.
In legal and regulatory contexts, sampling uncertainty affects the credibility of generalizations, forecasts, and risk characterizations based on empirical evidence.

* '''Modeling uncertainty'''
Modeling uncertainty arises when descriptive or causal claims rely on simplified representations of complex systems. This includes uncertainty associated with variable selection, functional relationships, and parameter specification.
** '''Causal uncertainty''' arises when relationships between variables are inferred rather than directly observed. Legal decision-makers often rely on causal models to explain why an outcome occurred or to predict the consequences of alternative actions.
** '''Epistemic choice''' refers to uncertainty introduced by selecting among competing interpretive frameworks or explanatory models.

===Aggregation and interaction of uncertainties===
The components of legal uncertainty do not operate independently. Linguistic, conceptual, measurement, sampling, and modeling uncertainties can combine in different ways to produce aggregate uncertainty affecting the credibility, relevance, and weight of evidence.

Legal taxonomies therefore focus not on eliminating uncertainty, but on making its sources explicit so that decision-makers can assess reliability, assign weight, and determine appropriate standards of proof or precaution.

===Relationship to scientific and engineering practice===
While legal taxonomies do not require quantitative completeness, they depend critically on validated scientific and engineering methods to support descriptive and causal claims. Scientific and engineering disciplines provide procedures and models capable of integrating qualitative and quantitative information into structured representations of uncertainty.

However, the legal use of such information remains distinct. The objective is not optimization or prediction per se, but defensible judgment under uncertainty. Validation, transparency, and clarity of assumptions are therefore central concerns at the interface between legal, scientific, and engineering domains.

These distinctions inform how uncertainty is framed, communicated, and relied upon in civil engineering projects subject to regulatory review, contractual interpretation, or dispute resolution.

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===Economics uncertainty taxonomies===
Like scientific and engineering treatments of uncertainty, economic approaches are environment-specific, phenomenon-oriented, and model-centric. In economics, the relevant environment is the market or sector of economic activity, and the phenomena of interest are human actions, organizational behavior, and manufactured constructs such as firms, contracts, and markets.

Economics does not address all forms of uncertainty. Instead, it focuses on subsets of uncertainty that possess causal connections to material outcomes, particularly those affecting prices, profits, losses, and allocation of resources. These subsets are commonly labeled as risks.

A foundational contribution to the economic treatment of uncertainty is associated with Frank Knight, who distinguished between measurable risk and unmeasurable uncertainty. Knight characterized uncertainty as arising from defects or limitations in knowledge, particularly managerial and organizational knowledge, rather than from inherent randomness alone. In this framework, risk refers to situations in which probabilities can be assigned to outcomes, while uncertainty refers to situations in which such assignment is not possible.

Knight defined risk as the class of uncertainties that can be reduced or managed through probabilistic reasoning and mechanisms such as insurance. By contrast, uncertainty in the strict sense reflects imperfect knowledge that cannot be reduced to numerical probabilities. For Knight, measuring uncertainty involved assessing both the likelihood of occurrence and the severity of economic consequences, while recognizing that some consequences remain fundamentally indeterminate.

Knight emphasized that economic decision-making is rarely conducted under conditions of complete ignorance or perfect information. Instead, action is typically based on partial knowledge, judgment, and opinion formed under uncertainty. Economic behavior therefore reflects degrees of belief and confidence rather than certainty, and the practical problem lies in managing variation in knowledge rather than eliminating uncertainty altogether.

In his analysis of insurance markets, Knight illustrated the difficulty of classifying certain uncertainties. Events such as accidental death could be treated probabilistically, while sickness or business failure resisted objective classification due to heterogeneity, judgment, and contextual dependence. Uncertainties that could not be classified or grouped were therefore not insurable and remained outside the domain of measurable risk.

===Core distinctions in economic uncertainty===
Economic treatments of uncertainty commonly rely on a small number of foundational distinctions that shape both theory and practice.

* Uncertainty about the economic environment, including prices, demand, technology, and external conditions, is distinct from uncertainty about the behavior or choices of other actors.
* Risk is a subset of uncertainty in which the decision-maker can enumerate possible actions, identify possible outcomes arising from each action, and assign probabilities to those outcomes.
* Uncertainty arises when such enumeration or probability assignment is not feasible due to incomplete knowledge, structural change, heterogeneity, or reliance on judgment.

These distinctions form an idealized classification that has been influential in economic theory, despite remaining philosophically and empirically contested. They nevertheless provide a useful framework for distinguishing calculable exposure from judgment-dependent uncertainty in economic analysis.

===Uncertainty, knowledge, and economic models===
Like the theory of the firm, an economic theory of uncertainty seeks to explain and predict the behavior of specific subsets of uncertainty that affect material economic outcomes. These theories examine how the presence, distribution, and variability of knowledge influence decision-making, pricing, and allocation of resources.

For analytical convenience, many economic models assume certainty or perfect information. The economics of uncertainty exists as an alternative framework that explicitly incorporates imperfect information, unknown preferences, and judgment-based decisions when doing so improves explanatory or predictive power.

Unlike physical systems, economic activity produces artificial constructs such as firms, contracts, markets, financial instruments, and institutions. Uncertainty in economics therefore arises not only from external conditions but also from institutional design, incentives, strategic interaction, and information asymmetry.

===Risk, hazard, and moral hazard===
Economic analysis distinguishes between risk and hazard. While the terminology is not always used consistently, the distinction becomes most explicit in the concept of moral hazard.

Moral hazard arises when the presence of insurance, guarantees, or protection alters behavior, leading actors to take actions that increase exposure to loss. In such cases, uncertainty is not merely a property of the environment but is generated endogenously through behavior and information asymmetry.

This distinction is particularly relevant for engineering projects involving insurance, contracting, public–private partnerships, and regulatory oversight, where incentives and information structure influence both technical and economic outcomes.

In summary, economic uncertainty taxonomies emphasize the role of partial knowledge, judgment, and incentives in shaping material outcomes. These frameworks inform how risk is identified, priced, transferred, or retained and provide an essential foundation for understanding cost, schedule, and financial uncertainty in civil engineering practice.

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===Uncertainty and risk in policy and regulatory environment===
Decision-making in policy and regulatory contexts must proceed under conditions of incomplete and potentially inaccurate information. The central problem is not eliminating uncertainty, but selecting decision rules that remain defensible in its presence.

As a social enterprise, risk regulation, regardless of substantive objectives, is commonly evaluated against three process objectives: effectiveness, efficiency, and equity. These objectives serve as meta-goals guiding regulatory choice under uncertainty.

From the standpoint of uncertainty analysis, causal information relevant to regulation can be divided into two broad categories:
* information about groups or populations, and
* information about individuals or specific cases.

Each category is associated with distinct forms of uncertainty. Group-level information is typically subject to sampling error, model uncertainty, and aggregation effects, while individual-level information is more strongly influenced by measurement error, classification uncertainty, and contextual variation. These uncertainties are logically distinct, generally independent, and cumulative, contributing to the overall uncertainty faced by decision-makers.

Policy and regulatory frameworks therefore rely on structured approaches to risk assessment and uncertainty characterization to determine appropriate thresholds for action, precaution, and intervention.

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===Engineering uncertainty taxonomies===
Like scientific and economic treatments of uncertainty, engineering approaches are environment-specific, phenomenon-oriented, and model-centric. Engineering is explicitly driven by decision-making in the face of uncertainty and is concerned primarily with uncertainties that have determinable and material consequences.

Like economics, engineering conceives risk as a specific and unambiguous subset of uncertainty, characterized by the probability of occurrence and the severity of consequences, including economic, safety, and performance impacts. Engineering practice also recognizes the central role of information and knowledge in constructing and operating the built environment.

In contrast to scientific inquiry, engineering is not concerned with uncertainty for its own sake. It focuses on uncertainty insofar as it affects design choices, system performance, project execution, and stakeholder outcomes.

===Behavioral and interaction uncertainty in engineering===
In addition to epistemic and aleatory uncertainty, engineering explicitly addresses behavioral or interaction uncertainty, which arises from how individuals and organizations act or interact within projects and systems.

Behavioral uncertainty arises from several sources:
* '''Design uncertainty''', which reflects choices among alternatives under the direct control of engineers or project teams that have not yet been resolved.
* '''Requirement uncertainty''', which includes parameters determined by stakeholders and external parties, independent of the engineer or designer.
* '''Volitional uncertainty''', which concerns uncertainty about future decisions and actions of individuals or organizations, particularly in multi-party or contractual environments.
* '''Human error''', which arises from mistakes, lapses, or blunders during the development, construction, or operation of systems and projects.

These sources of behavioral uncertainty interact with technical and economic uncertainties and must be managed through engineering judgment, governance structures, contractual arrangements, and professional standards.

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==Practice Frameworks for Uncertainty and Risk in Civil Engineering Practice==
===PMIBoK===
PMI does not define uncertainty, but the PMI Body of Knowledge uses the term in 45 places (uncertainty) and as describing properties in 8 places (uncertainties). [See Note 8] PMI does define "risk" and uses the term over 1,300 times in the same document. In its glossary, PMI also defines terms such as "threat" and "opportunity" but not events.[391 The BoK contextually defines uncertainty when it states that project risk has its origins in the uncertainty present in all projects. (Op. Cit., pg 309) 
'''Uncertainty''' is contextually defined and taken as having the property of affecting project execution and the ability to meet stakeholder expectations. Uncertainty is more than the sum of all individual risks (known and unknown). The nature of this uncertainty is not defined, only its capacity to affect something else or its properties. 
'''Risk''' is defined as " ... an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives." (Ibid.) Beyond this formal definition, PMI adds context when, in its risk management section (Sec. 11 ), the BoK states that risk is a caused event or condition with multiple causes and impacts. The BoK even offers specific techniques for mapping cause-and-effect relationships and influence diagrams. (Sec. 11.2.2.5) Not as evident but equally important is the recognition that not only is risk caused but the impact is "triggered". The risk trigger is an event or situation that signals that it is about to occur (Glossary, pg. 566). Risk conditions are factors that affect or otherwise contribute to risk, such as project stakeholders. Risk implicitly retains some residual element of probability in it, or a value of less than 1.0 with risk that approaches a level of near certainty is termed "issues" or "realized risk" .(Op. Cit., pg 309). PMI also links risk to underlying variations in project outcomes. (Ibid.) Risk is based upon data and information which can be assessed as to its quality (Sec. 11.3.2.3). This analysis looks to determine the value of the information and examines the degree to which the risk information is understood as well as its " ... accuracy, quality, reliability and integrity ... " (Ibid.) Part of that is understanding the relationship between occurrence and impact as outlined in PMl's Figure 11-10 Risk Impact Matrix and developing a model for prioritizing risk products and setting the threshold for action.
====Commentary:====
For defining and identifying risk, PMI notes in Sec. 11.2.2.4 that " ... Every project and its plan is conceived and developed based on a set of hypotheses, scenarios, or assumptions." Part of the risk analysis process is identifying risks to the project, such as inaccuracy, instability, inconsistency, or incompleteness of assumptions. Similarly, the quality of management plans, as well as their consistency with others and the project objectives and assumptions are 11 •• .indicators of risk in the project." (Sec. 11.2.2.1) 
Similarly, the role of engineering economics in risk is embedded or implied in the PMI definition of risk when the BoK discusses risk identification as a process of assessing which risks out of the total risk exposure for the project "may affect" the project and distilling their characteristics. Embedded in this statement is the concept that not all risks can cause economic impacts (positive and negative) to the project. Also embedded is the requirement to understand risk characteristics to identify project risk (Cost and schedule). Lastly, risk identification is an iterative process of incrementing project risk documentation, much like project scope, cost, and schedule documentation. 
Lastly, PMI acknowledges the complex constitution of risk aggregates or the "sum" of all risks in its material, but is it a sum? Risk information has descriptive and explanatory content. These are knowledge and meta-knowledge components. Understanding project assumptions is project-specific knowledge, but identifying risks from assumption inaccuracy, instability, inconsistency, or incompleteness requires professional or program meta-knowledge. 
'''Uncertainty exists in all projects, but risk, a subset of uncertainty with material economic impacts, is a key meta-knowledge element for civil engineering practice.'''
===Software Development Process- Risk Focused===
The Unified Process requires the project team to focus on addressing the most critical risks early in the project life
cycle. The deliverables of each iteration, especially in the Elaboration phase, must be selected to ensure that the greatest risks are addressed first. <ref> Booch, Grady, Ivar Jacobson, and James Rumbaugh. "The unified software development process."
Reading: Addison Wesley (1999).</ref>
===American Society of Civil Engineers (ASCE)===
ASCE does not define uncertainty, but the ASCE CE Body of Knowledge (CEBoK) uses the term in 22 places (uncertainty) and describes properties in 13 places (uncertainties). 
The CEBoK does not use the phrase "uncertainty and risk" as PMI and AACE do in their publication. ASCE reverses the two (Risk and uncertainty) in twelve places regarding technical outcomes (Outcome 12). A fundamental difference between PMI and ISO is that risk is considered a subset of uncertainty. ASCE does not define "risk" but uses the term 25 times in the same document. Almost all are in the context of variation of design parameters and not in the classical context of cost, schedule uncertainty, and risk.
====Commentary:====
...
=== American Association of Cost Engineers (AACE) ===
AACE defined uncertainty, risk, and other related terms in its Risk Management Dictionary. <ref>Risk Management Committee uRisk Management Dictionary, Cost Engineering Vol. 37/No. 10, OCTOBER 1995. </ref>
AACE's overall strategy was to define risk and other terms that stem from base uncertainty. AACE first defined uncertainty as .... "(t)he total range of events that may happen and produce risks (including both threats and opportunities) affecting a project (see opportunities, events, conditions, risk, and threats" where the following sub-definitions apply:
** Biases--A lack of objectivity based on the individual's position or perspective. Systematic and predictable relationships between a person's opinion or statement and his/her underlying knowledge or circumstances. Note: there may be "system biases" as well as "individual biases."
** Condition (Uncertain Condition)-Any specific identifiable circumstance (such as the rate of inflation or the quality of labor available) that might affect the outcome of the project
** Event (Uncertain Condition)-is a specific identifiable action (such as a large government project being started in the same labor area as your project) or an act of nature that might happen and that (if it does happen) could affect the outcome of the project.
** Opportunities are Uncertain events that could improve the results or improve the probability that the desired outcome will happen
** Threats are Uncertain events that are potentially negative or reduce the probability that the desired outcome will happen.
*** AACE defines Risk as an "ambiguous term" (sic) that is synonymous with uncertainty or a negative subset such as "threats", or an overall negative impact of all possible uncertainties.
====Commentary:====
...

==Limitations of the definition==
...
==Working definition==
...
==Beneficial outcomes==
...
==See also==
Wiki articles on the project.

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==Notes==
<references group="note" />
<li> Implicit in this definition of uncertainty as a "state of' is ... </li>
: " ... a conceptualization of uncertainty as a subjective, cognitive experience of people--a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one's lack of knowledge, without which one could not feel uncertain; '''uncertainty is a form of "meta-cognition" ... ( or alternatively, its main component, meta-knowledge) ... -a knowing about knowing.'''" (Han, 2011, op. cit., Emphasis added)
</ol>
<ol start="5">
<li> Cox wanted his system to satisfy the following conditions:</li>
: Divisibility and comparability- The plausibility of a statement is a real number and depends on the information we have related to the statement.
: Common sense - Plausibilities should vary sensibly with the assessment of plausibilities in the model.
: Consistency - If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). -preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf
</ol>
<ol start="6">
<li> The utility of existing taxonomies of uncertainty in civil engineering and, by extension, "risk" has been unsatisfactory for this very reason. This is particularly true in cases where the source of the uncertainty is conflicting versus incomplete information. See Regan, et. al.,"A taxonomy and treatment of uncertainty for ecology and conservation biology" (2002)
for a survey of scientific taxonomies for uncertainty.</li>
<li> Regan et al. argue that " ... genuine examples of this kind of uncertainty are hard to find. Even classic cases of random experiments like coin tosses and the throwing of dice are deterministic; it is just that we do not have enough information about the dynamic processes and initial conditions to make any sensible estimates about the outcomes. Such processes are, for all intents and purposes, inherently random, but they are not genuinely inherently random. For similar reasons, complex systems such as ecosystems and weather patterns are unlikely to be inherently random. Similarly, chaotic systems are entirely deterministic. They are unpredictable because the deterministic processes generating them and the relevant initial conditions are hard
to fully specify (see Stewart 1989, Sugihara et al. 1990)." (Regan (2002) </li>
<li> The CEBoK uses the phrase "uncertainty and risk" twice in reference to scheduling (Section 6.5,2) and cost estimating (Section 7.2,2) as well as reversing the two (Risk and Uncertainty) also in two places in discussing project life cycle (both in Section 2.4.1) </li>
</ol>

==References==
<references />

Uncertainty and Risk in Civil Engineering Practice

2026-01-20T01:31:25Z

WikiSysop: 2026 01 19 /* Economics Uncertainty Taxonomies */ GPT edits

'''Life is short and the art long; the occasion instant, experiment perilous, decision difficult.'''
(Hippocrates, as quoted in Fox)

:''See also [[Project Definition]], [[Project Life Cycle and Phase Models]], [[Project Delivery Methods]]''
:''See also [[Technical Outcomes for Civil Engineering: Risk and Uncertainty]]''

==Basic considerations==
Civil engineering practice requires decision-making in environments characterized by incomplete, imperfect, and evolving knowledge. Engineers routinely act in the presence of uncertainty related to physical phenomena, economic constraints, regulatory requirements, and human behavior. The professional problem is not the elimination of uncertainty, which is impossible in practice, but its reduction, partitioning, and management to a degree sufficient to support responsible action.

In this chapter, '''risk''' is treated as a bounded and decision-relevant subset of broader uncertainty. Uncertainty that can be reduced, structured, prioritized, and acted upon using professional knowledge, models, and experience is termed risk. Related but distinct is the concept of '''hazard''', which denotes a source or condition with the potential to cause harm, independent of likelihood or consequence.

This framing aligns with interdisciplinary treatments of risk analysis, which emphasize the role of uncertainty in shaping social, technical, and governance systems over long historical periods. McDaniels and Small describe risk analysis as emerging from the need to act despite imperfect knowledge and argue that societies have progressively shifted from fatalistic acceptance of uncertainty toward its active management through structured analysis and decision frameworks.<ref name="McDaniels">McDaniels, Timothy, and Mitchell Small. ''Risk Analysis and Society: An Interdisciplinary Characterization of the Field''. Cambridge University Press, 2004, p. 1.</ref>

Within civil engineering, this evolution has produced specialized practices focused on managing uncertainty through modeling, design standards, safety factors, reliability analysis, and project controls. These practices rely not only on domain knowledge of materials, loads, and systems, but also on '''meta-knowledge''': knowledge about the limitations, reliability, and appropriate use of engineering knowledge itself.

==Semantic, epistemic, and logical frameworks==
Clear terminology is a prerequisite for coherent risk analysis. In professional practice, inconsistent or interchangeable use of terms such as uncertainty, risk, hazard, error, and bias can obscure decision responsibility and complicate communication among stakeholders.

The semantic framework adopted in this chapter proceeds in stages:
# distinction between certainty and uncertainty;
# refinement of uncertainty into epistemic, aleatory, and behavioral components;
# use of error and bias as operational proxies for uncertainty;
# transition from general uncertainty to discipline-specific risk taxonomies.

Beyond this point, the discussion shifts from semantics to logical frameworks, where uncertainty is partitioned into structured taxonomies suitable for analysis, communication, and decision-making in engineering projects.

==Semantics framework==

===Certainty and uncertainty===
Certainty is commonly described as an epistemic property of belief, implying immunity from doubt or criticism. In practical contexts, however, absolute certainty is neither attainable nor desirable. As Mill observed, assurance sufficient for the purposes of human life does not require freedom from all doubt, but rather justified confidence grounded in experience and evidence.

Uncertainty arises whenever beliefs or propositions are subject to doubt, criticism, or incomplete support. Contemporary treatments emphasize that uncertainty is not merely a feature of the external world, but also a cognitive condition reflecting awareness of incomplete knowledge. Han et al. characterize uncertainty as a meta-cognitive state: knowing that one does not know, or knowing that one’s knowledge may be incomplete or unreliable.<ref name="Han">Han, Paul K. J., William M. P. Klein, and Neeraj K. Arora. “Varieties of Uncertainty in Health Care: A Conceptual Taxonomy.” ''Medical Decision Making'' 31, no. 6 (2011): 828–838.</ref>

In professional reasoning, uncertainty manifests as the potential for error in observation, modeling, judgment, and action. Reasoning under uncertainty therefore differs fundamentally from reasoning under certainty: conclusions are provisional, contingent, and subject to revision as new information becomes available.

===Error, bias, and uncertainty===
In applied disciplines, uncertainty is rarely managed directly. Instead, it is addressed through its observable proxies: error and bias. Error refers to deviation between an estimate, measurement, or prediction and a reference value or observed outcome. Bias denotes systematic or directional error arising from model structure, data selection, judgment, or institutional practice.

Error can often be quantified, propagated, and reduced using analytical and statistical methods. Bias is more difficult to detect and correct, frequently requiring theoretical analysis, validation studies, or comparison across independent methods. The management of uncertainty in civil engineering therefore depends critically on understanding how error and bias arise, how they propagate through sequential decisions, and how they can be bounded through professional controls.

===Propagation of error and uncertainty===
Engineering decisions typically involve multi-step processes: observation, interpretation, modeling, design, construction, and operation. Errors introduced early in this chain can propagate and amplify downstream, while later errors may have limited impact depending on system sensitivity and redundancy.

Professional practice therefore emphasizes identifying where uncertainty enters the decision sequence and prioritizing error and bias reduction where it has the greatest effect on outcomes. This ordering principle underlies practices such as conservative design assumptions, staged investigations, peer review, and independent verification.

===Epistemic, aleatory, and behavioral uncertainty===
For practical purposes, uncertainty in civil engineering can be partitioned into three broad components:
* '''Epistemic uncertainty''', arising from incomplete knowledge, limited data, or imperfect models;
* '''Aleatory uncertainty''', reflecting inherent variability in physical processes;
* '''Behavioral uncertainty''', associated with human judgment, decision-making, and organizational interaction.

This partitioning supports targeted mitigation strategies. Epistemic uncertainty may be reduced through investigation and analysis; aleatory uncertainty may be managed through design margins and reliability methods; behavioral uncertainty requires institutional controls, contractual clarity, and governance mechanisms.

==From uncertainty to risk and hazard==
Risk and hazard are not synonymous with uncertainty, but structured subsets of it. Risk refers to uncertainty that has been framed in terms of potential consequences and that is sufficiently bounded to support decision-making. Hazard denotes a source or condition with the potential to cause harm, independent of likelihood.

In this chapter, '''general risk''' is defined as:
:'''a bounded subset of uncertainty associated with the potential for material error or bias when acting or choosing in physical, economic, or social environments, and that can be represented using coherent analytical or probabilistic frameworks and subjected to testing or validation.'''

This definition is discipline-neutral and applies across engineering, economics, medicine, and policy. Subsequent sections introduce discipline-specific taxonomies that adapt this general concept to the particular decision contexts of civil engineering projects.

==Logical framework for specific risk taxonomies==
Once uncertainty has been reduced to bounded, decision-relevant forms, it becomes possible to develop taxonomies that support analysis, communication, and management. Taxonomies are not required to be epistemically complete; rather, they must be logically distinct, practically exhaustive for their purpose, and aligned with decision needs.

The following sections examine how scientific, legal, economic, and engineering disciplines construct uncertainty and risk taxonomies, and how these perspectives inform civil engineering practice.

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===Scientific risk taxonomies===
Scientific inquiry proceeds by observing phenomena, proposing explanatory models, and testing those models against experience. At every stage of this process, uncertainty is present. Scientific risk taxonomies arise from the need to identify, distinguish, and manage different sources of uncertainty that affect observation, modeling, inference, and prediction.

Most scientific taxonomies of uncertainty have focused on uncertainties associated with data, parameters, and models, particularly those that can be represented statistically. While multiple taxonomies are possible, a common organizing principle is that uncertainty is environment-specific, phenomenon-oriented, and model-centric.

====Environment-specific, phenomenon-oriented, model-centric taxonomy of uncertainty====
A widely used scientific taxonomy distinguishes uncertainty according to its relationship to models and data:

* '''Parameter uncertainty'''
Parameter uncertainty arises from uncertainty in the numerical values of model inputs. These parameters may be physically unobservable, indirectly inferred, or only partially constrained by available data.
** '''Experimental (observational) uncertainty''' results from variability in repeated measurements under nominally identical conditions.
** '''Parametric variability''' reflects natural or contextual variability in input variables that are treated as fixed values within a model.

* '''Structural uncertainty'''
Structural uncertainty, also referred to as model inadequacy or model discrepancy, arises when a model fails to capture relevant features of the underlying phenomenon. Because all models are simplifications of reality, structural uncertainty is unavoidable and depends on model assumptions, scope, and fitness for purpose.
** Structural uncertainty is distinct from parameter uncertainty and cannot be reduced solely by collecting additional data.
** Inadequate model structure can lead to systematically misleading conclusions even when parameter estimates appear precise.

* '''Algorithmic (numerical) uncertainty'''
Algorithmic uncertainty arises from numerical approximations used to implement models computationally. Examples include discretization error, convergence error, and truncation error associated with numerical solution methods such as finite difference or finite element approximations.
** These uncertainties depend**

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==Uncertainty taxonomies in a legal environment==
Legal decision-making operates under conditions of uncertainty that differ in purpose and structure from those of science or engineering. From a legal perspective, a taxonomy is a systematic scheme for distinguishing, ordering, and naming types within a subject field. Such taxonomies are not required to be epistemically complete; rather, they must be sufficient to organize the kinds of descriptive information commonly encountered by decision-makers and to support fair, effective, and defensible judgments.

Walker characterizes legal taxonomies of scientific and technical uncertainty as tools for cataloging uncertainty associated with descriptive assertions, particularly those involving cause-and-effect claims. These taxonomies emphasize clarity, logical distinction, and practical exhaustiveness over theoretical completeness.

===Core components of legal uncertainty===
Legal frameworks distinguish multiple, logically distinct sources of uncertainty that may affect descriptive assertions and decisions:

* '''Linguistic uncertainty'''
Linguistic uncertainty, often described as vagueness, arises from limitations of language rather than from data or models.
** '''Context dependence''' occurs when the applicability of a statement depends on unstated or shifting contextual conditions.
** '''Ambiguity''' arises when terms admit multiple meanings that cannot be resolved within the given context.
** '''Underspecification''' occurs when descriptions lack sufficient detail to support a unique interpretation.
Linguistic uncertainty underlies all other components of legal uncertainty, as it affects how evidence, concepts, and models are interpreted.

* '''Conceptual uncertainty'''
Conceptual uncertainty arises from the choice and definition of concepts used to describe phenomena. Whenever predicates are selected to characterize an object, event, or condition, alternative conceptualizations are excluded.
Conceptualization generates useful structure, but it can also constrain understanding by privileging certain interpretations over others. Poorly chosen or inadequately defined concepts can therefore introduce systematic distortion into legal and technical reasoning.

Conceptual uncertainty is closely related to issues of validity. A concept is valid to the extent that it accurately represents the phenomenon it is intended to describe. Conceptual bias, like systematic error in measurement, is difficult to detect and often recognizable only through theoretical analysis or comparison across alternative frameworks.

* '''Measurement and classification uncertainty'''
Measurement uncertainty arises when concepts are applied to specific cases through observation, assessment, or classification. Even when conceptual uncertainty has been minimized, misclassification may occur due to procedural limitations, observer judgment, or data quality.
Measurement uncertainty affects the reliability and precision of descriptive assertions. Errors at this stage can propagate through subsequent reasoning and reduce the quality of causal inference or decision outcomes.

* '''Sampling uncertainty'''
Sampling uncertainty arises when conclusions about a population are inferred from a limited subset of observations. The uncertainty reflects the extent to which the sample represents the relevant population.
In legal and regulatory contexts, sampling uncertainty affects the credibility of generalizations, forecasts, and risk characterizations based on empirical evidence.

* '''Modeling uncertainty'''
Modeling uncertainty arises when descriptive or causal claims rely on simplified representations of complex systems. This includes uncertainty associated with variable selection, functional relationships, and parameter specification.
** '''Causal uncertainty''' arises when relationships between variables are inferred rather than directly observed. Legal decision-makers often rely on causal models to explain why an outcome occurred or to predict the consequences of alternative actions.
** '''Epistemic choice''' refers to uncertainty introduced by selecting among competing interpretive frameworks or explanatory models.

===Aggregation and interaction of uncertainties===
The components of legal uncertainty do not operate independently. Linguistic, conceptual, measurement, sampling, and modeling uncertainties can combine in different ways to produce aggregate uncertainty affecting the credibility, relevance, and weight of evidence.

Legal taxonomies therefore focus not on eliminating uncertainty, but on making its sources explicit so that decision-makers can assess reliability, assign weight, and determine appropriate standards of proof or precaution.

===Relationship to scientific and engineering practice===
While legal taxonomies do not require quantitative completeness, they depend critically on validated scientific and engineering methods to support descriptive and causal claims. Scientific and engineering disciplines provide procedures and models capable of integrating qualitative and quantitative information into structured representations of uncertainty.

However, the legal use of such information remains distinct. The objective is not optimization or prediction per se, but defensible judgment under uncertainty. Validation, transparency, and clarity of assumptions are therefore central concerns at the interface between legal, scientific, and engineering domains.

These distinctions inform how uncertainty is framed, communicated, and relied upon in civil engineering projects subject to regulatory review, contractual interpretation, or dispute resolution.

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===Economics uncertainty taxonomies===
Like scientific and engineering treatments of uncertainty, economic approaches are environment-specific, phenomenon-oriented, and model-centric. In economics, the relevant environment is the market or sector of economic activity, and the phenomena of interest are human actions, organizational behavior, and manufactured constructs such as firms, contracts, and markets.

Economics does not address all forms of uncertainty. Instead, it focuses on subsets of uncertainty that possess causal connections to material outcomes, particularly those affecting prices, profits, losses, and allocation of resources. These subsets are commonly labeled as risks.

A foundational contribution to the economic treatment of uncertainty is associated with Frank Knight, who distinguished between measurable risk and unmeasurable uncertainty. Knight characterized uncertainty as arising from defects or limitations in knowledge, particularly managerial and organizational knowledge, rather than from inherent randomness alone. In this framework, risk refers to situations in which probabilities can be assigned to outcomes, while uncertainty refers to situations in which such assignment is not possible.

Knight defined risk as the class of uncertainties that can be reduced or managed through probabilistic reasoning and mechanisms such as insurance. By contrast, uncertainty in the strict sense reflects imperfect knowledge that cannot be reduced to numerical probabilities. For Knight, measuring uncertainty involved assessing both the likelihood of occurrence and the severity of economic consequences, while recognizing that some consequences remain fundamentally indeterminate.

Knight emphasized that economic decision-making is rarely conducted under conditions of complete ignorance or perfect information. Instead, action is typically based on partial knowledge, judgment, and opinion formed under uncertainty. Economic behavior therefore reflects degrees of belief and confidence rather than certainty, and the practical problem lies in managing variation in knowledge rather than eliminating uncertainty altogether.

In his analysis of insurance markets, Knight illustrated the difficulty of classifying certain uncertainties. Events such as accidental death could be treated probabilistically, while sickness or business failure resisted objective classification due to heterogeneity, judgment, and contextual dependence. Uncertainties that could not be classified or grouped were therefore not insurable and remained outside the domain of measurable risk.

===Core distinctions in economic uncertainty===
Economic treatments of uncertainty commonly rely on the following distinctions:

* Uncertainty about the economic environment, including prices, demand, and external conditions, is distinct from uncertainty about the behavior or choices of other actors.
* Risk is a subset of uncertainty in which the decision-maker can enumerate possible actions, identify possible outcomes, and assign probabilities to those outcomes.
* Uncertainty arises when such enumeration or probability assignment is not feasible due to incomplete knowledge, structural change, or reliance on judgment.

These distinctions form an idealized classification that has been influential but remains philosophically and empirically contested. Nevertheless, it provides a useful framework for distinguishing calculable exposure from judgment-dependent uncertainty in economic analysis.

===Uncertainty, knowledge, and economic models===
Like the theory of the firm, an economic theory of uncertainty seeks to explain and predict the behavior of specific subsets of uncertainty that affect economic outcomes. These theories examine how the presence, distribution, and variability of knowledge influence decision-making, pricing, and allocation.

Economic models often assume certainty or perfect information for analytical convenience. The economics of uncertainty exists as an alternative framework that explicitly incorporates imperfect information, unknown preferences, and judgment-based decisions when doing so improves explanatory or predictive power.

Unlike physical systems, economic activity produces artificial constructs such as firms, contracts, markets, and financial instruments. Uncertainty in economics therefore arises not only from external conditions but also from institutional design, incentives, and strategic interaction.

===Risk, hazard, and moral hazard===
Economic analysis also distinguishes between risk and hazard. While Knight used the term hazard in several contexts, the distinction becomes most explicit in the concept of moral hazard. Moral hazard arises when the presence of insurance or protection alters behavior, leading actors to take actions that increase exposure to loss.

In such cases, uncertainty is not merely a property of the environment but is generated endogenously through behavior and information asymmetry. This distinction is particularly relevant for engineering projects involving insurance, contracting, public–private partnerships, and regulatory oversight.

In summary, economic uncertainty taxonomies emphasize the role of partial knowledge, judgment, and incentives in shaping material outcomes. These frameworks inform how risk is identified, priced, transferred, or retained, and they provide an essential foundation for understanding cost, schedule, and financial uncertainty in civil engineering practice.

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=== Uncertainty and Risk in Policy and Regulatory Environment ===
:''"Hume's 'just reasoner', when faced with a difficult public policy decision in current times, would probably commission a risk analysis. But how could they incorporate 'a degree of doubt, caution, and modesty?"'' '''note-43 [36]'''
The problem of "decision-making in the face of uncertainty" is how to regulate based on incomplete information that has the potential to be materially inaccurate. A contextual assumption is that we need to evaluate decision rules for dealing with uncertainty. As a social enterprise, risk regulation, whatever its substantive objectives, should be as '''effective, efficient, and equitable''' as practically possible. These "three E's" form a set of "process objectives" or "meta-goals." From the uncertainty standpoint, causal information can be usefully divided into two major categories: information about groups and information about individuals.

Each category, group, and individual has its distinctive types of inherent uncertainty. These are logically distinct, generally independent, and cumulative, contributing to '''(?????)'''

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=== Engineering Uncertainty Taxonomies ===
Like scientific and economic concepts of uncertainty, engineering is environment-specific, phenomena-oriented, model-centric, external choice, and driven by decision-making in the face of uncertainty. Like economics and unlike science, engineering is not interested in all forms of uncertainty, only those with determinable and material consequences. Like economics, engineering conceives risk as a specific and unambiguous subset of uncertainty determined by measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences. Also, engineering recognizes the role information and, by extension, knowledge plays in constructing the built environment.
'''note-44 [37]]''' Like economics, engineering differs from economics and scientific uncertainty in its interest in behavioral uncertainty.
*"Behavioral or interaction uncertainty is how individuals or organizations act or interact. Behavioral uncertainty arises from four sources: design uncertainty, requirement uncertainty, volitional uncertainty, and human errors." '''note-45 [38]]'''
** A design uncertainty is a choice among alternatives over which an individual or group of individuals exercises direct control but has not yet decided upon.
** Requirement uncertainty includes parameters of interest to and determined by the stakeholder, independent of the engineer or designer.
** Volitional uncertainty is uncertainty about what the subject him/herself will decide. Other people’s future actions and conduct are not entirely predictable, particularly when dealing with other organizations.
** Human errors occur during the development of a system or project due to blunders or mistakes by an individual or individuals.

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==Practice Frameworks for Uncertainty and Risk in Civil Engineering Practice==
===PMIBoK===
PMI does not define uncertainty, but the PMI Body of Knowledge uses the term in 45 places (uncertainty) and as describing properties in 8 places (uncertainties). [See Note 8] PMI does define "risk" and uses the term over 1,300 times in the same document. In its glossary, PMI also defines terms such as "threat" and "opportunity" but not events.[391 The BoK contextually defines uncertainty when it states that project risk has its origins in the uncertainty present in all projects. (Op. Cit., pg 309) 
'''Uncertainty''' is contextually defined and taken as having the property of affecting project execution and the ability to meet stakeholder expectations. Uncertainty is more than the sum of all individual risks (known and unknown). The nature of this uncertainty is not defined, only its capacity to affect something else or its properties. 
'''Risk''' is defined as " ... an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives." (Ibid.) Beyond this formal definition, PMI adds context when, in its risk management section (Sec. 11 ), the BoK states that risk is a caused event or condition with multiple causes and impacts. The BoK even offers specific techniques for mapping cause-and-effect relationships and influence diagrams. (Sec. 11.2.2.5) Not as evident but equally important is the recognition that not only is risk caused but the impact is "triggered". The risk trigger is an event or situation that signals that it is about to occur (Glossary, pg. 566). Risk conditions are factors that affect or otherwise contribute to risk, such as project stakeholders. Risk implicitly retains some residual element of probability in it, or a value of less than 1.0 with risk that approaches a level of near certainty is termed "issues" or "realized risk" .(Op. Cit., pg 309). PMI also links risk to underlying variations in project outcomes. (Ibid.) Risk is based upon data and information which can be assessed as to its quality (Sec. 11.3.2.3). This analysis looks to determine the value of the information and examines the degree to which the risk information is understood as well as its " ... accuracy, quality, reliability and integrity ... " (Ibid.) Part of that is understanding the relationship between occurrence and impact as outlined in PMl's Figure 11-10 Risk Impact Matrix and developing a model for prioritizing risk products and setting the threshold for action.
====Commentary:====
For defining and identifying risk, PMI notes in Sec. 11.2.2.4 that " ... Every project and its plan is conceived and developed based on a set of hypotheses, scenarios, or assumptions." Part of the risk analysis process is identifying risks to the project, such as inaccuracy, instability, inconsistency, or incompleteness of assumptions. Similarly, the quality of management plans, as well as their consistency with others and the project objectives and assumptions are 11 •• .indicators of risk in the project." (Sec. 11.2.2.1) 
Similarly, the role of engineering economics in risk is embedded or implied in the PMI definition of risk when the BoK discusses risk identification as a process of assessing which risks out of the total risk exposure for the project "may affect" the project and distilling their characteristics. Embedded in this statement is the concept that not all risks can cause economic impacts (positive and negative) to the project. Also embedded is the requirement to understand risk characteristics to identify project risk (Cost and schedule). Lastly, risk identification is an iterative process of incrementing project risk documentation, much like project scope, cost, and schedule documentation. 
Lastly, PMI acknowledges the complex constitution of risk aggregates or the "sum" of all risks in its material, but is it a sum? Risk information has descriptive and explanatory content. These are knowledge and meta-knowledge components. Understanding project assumptions is project-specific knowledge, but identifying risks from assumption inaccuracy, instability, inconsistency, or incompleteness requires professional or program meta-knowledge. 
'''Uncertainty exists in all projects, but risk, a subset of uncertainty with material economic impacts, is a key meta-knowledge element for civil engineering practice.'''
===Software Development Process- Risk Focused===
The Unified Process requires the project team to focus on addressing the most critical risks early in the project life
cycle. The deliverables of each iteration, especially in the Elaboration phase, must be selected to ensure that the greatest risks are addressed first. <ref> Booch, Grady, Ivar Jacobson, and James Rumbaugh. "The unified software development process."
Reading: Addison Wesley (1999).</ref>
===American Society of Civil Engineers (ASCE)===
ASCE does not define uncertainty, but the ASCE CE Body of Knowledge (CEBoK) uses the term in 22 places (uncertainty) and describes properties in 13 places (uncertainties). 
The CEBoK does not use the phrase "uncertainty and risk" as PMI and AACE do in their publication. ASCE reverses the two (Risk and uncertainty) in twelve places regarding technical outcomes (Outcome 12). A fundamental difference between PMI and ISO is that risk is considered a subset of uncertainty. ASCE does not define "risk" but uses the term 25 times in the same document. Almost all are in the context of variation of design parameters and not in the classical context of cost, schedule uncertainty, and risk.
====Commentary:====
...
=== American Association of Cost Engineers (AACE) ===
AACE defined uncertainty, risk, and other related terms in its Risk Management Dictionary. <ref>Risk Management Committee uRisk Management Dictionary, Cost Engineering Vol. 37/No. 10, OCTOBER 1995. </ref>
AACE's overall strategy was to define risk and other terms that stem from base uncertainty. AACE first defined uncertainty as .... "(t)he total range of events that may happen and produce risks (including both threats and opportunities) affecting a project (see opportunities, events, conditions, risk, and threats" where the following sub-definitions apply:
** Biases--A lack of objectivity based on the individual's position or perspective. Systematic and predictable relationships between a person's opinion or statement and his/her underlying knowledge or circumstances. Note: there may be "system biases" as well as "individual biases."
** Condition (Uncertain Condition)-Any specific identifiable circumstance (such as the rate of inflation or the quality of labor available) that might affect the outcome of the project
** Event (Uncertain Condition)-is a specific identifiable action (such as a large government project being started in the same labor area as your project) or an act of nature that might happen and that (if it does happen) could affect the outcome of the project.
** Opportunities are Uncertain events that could improve the results or improve the probability that the desired outcome will happen
** Threats are Uncertain events that are potentially negative or reduce the probability that the desired outcome will happen.
*** AACE defines Risk as an "ambiguous term" (sic) that is synonymous with uncertainty or a negative subset such as "threats", or an overall negative impact of all possible uncertainties.
====Commentary:====
...

==Limitations of the definition==
...
==Working definition==
...
==Beneficial outcomes==
...
==See also==
Wiki articles on the project.

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==Notes==
<references group="note" />
<li> Implicit in this definition of uncertainty as a "state of' is ... </li>
: " ... a conceptualization of uncertainty as a subjective, cognitive experience of people--a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one's lack of knowledge, without which one could not feel uncertain; '''uncertainty is a form of "meta-cognition" ... ( or alternatively, its main component, meta-knowledge) ... -a knowing about knowing.'''" (Han, 2011, op. cit., Emphasis added)
</ol>
<ol start="5">
<li> Cox wanted his system to satisfy the following conditions:</li>
: Divisibility and comparability- The plausibility of a statement is a real number and depends on the information we have related to the statement.
: Common sense - Plausibilities should vary sensibly with the assessment of plausibilities in the model.
: Consistency - If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). -preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf
</ol>
<ol start="6">
<li> The utility of existing taxonomies of uncertainty in civil engineering and, by extension, "risk" has been unsatisfactory for this very reason. This is particularly true in cases where the source of the uncertainty is conflicting versus incomplete information. See Regan, et. al.,"A taxonomy and treatment of uncertainty for ecology and conservation biology" (2002)
for a survey of scientific taxonomies for uncertainty.</li>
<li> Regan et al. argue that " ... genuine examples of this kind of uncertainty are hard to find. Even classic cases of random experiments like coin tosses and the throwing of dice are deterministic; it is just that we do not have enough information about the dynamic processes and initial conditions to make any sensible estimates about the outcomes. Such processes are, for all intents and purposes, inherently random, but they are not genuinely inherently random. For similar reasons, complex systems such as ecosystems and weather patterns are unlikely to be inherently random. Similarly, chaotic systems are entirely deterministic. They are unpredictable because the deterministic processes generating them and the relevant initial conditions are hard
to fully specify (see Stewart 1989, Sugihara et al. 1990)." (Regan (2002) </li>
<li> The CEBoK uses the phrase "uncertainty and risk" twice in reference to scheduling (Section 6.5,2) and cost estimating (Section 7.2,2) as well as reversing the two (Risk and Uncertainty) also in two places in discussing project life cycle (both in Section 2.4.1) </li>
</ol>

==References==
<references />

Uncertainty and Risk in Civil Engineering Practice

2026-01-20T01:29:43Z

WikiSysop: 2026 01 19 /* Uncertainty Taxonomies in a Legal Environment */ GPT edits...

'''Life is short and the art long; the occasion instant, experiment perilous, decision difficult.'''
(Hippocrates, as quoted in Fox)

:''See also [[Project Definition]], [[Project Life Cycle and Phase Models]], [[Project Delivery Methods]]''
:''See also [[Technical Outcomes for Civil Engineering: Risk and Uncertainty]]''

==Basic considerations==
Civil engineering practice requires decision-making in environments characterized by incomplete, imperfect, and evolving knowledge. Engineers routinely act in the presence of uncertainty related to physical phenomena, economic constraints, regulatory requirements, and human behavior. The professional problem is not the elimination of uncertainty, which is impossible in practice, but its reduction, partitioning, and management to a degree sufficient to support responsible action.

In this chapter, '''risk''' is treated as a bounded and decision-relevant subset of broader uncertainty. Uncertainty that can be reduced, structured, prioritized, and acted upon using professional knowledge, models, and experience is termed risk. Related but distinct is the concept of '''hazard''', which denotes a source or condition with the potential to cause harm, independent of likelihood or consequence.

This framing aligns with interdisciplinary treatments of risk analysis, which emphasize the role of uncertainty in shaping social, technical, and governance systems over long historical periods. McDaniels and Small describe risk analysis as emerging from the need to act despite imperfect knowledge and argue that societies have progressively shifted from fatalistic acceptance of uncertainty toward its active management through structured analysis and decision frameworks.<ref name="McDaniels">McDaniels, Timothy, and Mitchell Small. ''Risk Analysis and Society: An Interdisciplinary Characterization of the Field''. Cambridge University Press, 2004, p. 1.</ref>

Within civil engineering, this evolution has produced specialized practices focused on managing uncertainty through modeling, design standards, safety factors, reliability analysis, and project controls. These practices rely not only on domain knowledge of materials, loads, and systems, but also on '''meta-knowledge''': knowledge about the limitations, reliability, and appropriate use of engineering knowledge itself.

==Semantic, epistemic, and logical frameworks==
Clear terminology is a prerequisite for coherent risk analysis. In professional practice, inconsistent or interchangeable use of terms such as uncertainty, risk, hazard, error, and bias can obscure decision responsibility and complicate communication among stakeholders.

The semantic framework adopted in this chapter proceeds in stages:
# distinction between certainty and uncertainty;
# refinement of uncertainty into epistemic, aleatory, and behavioral components;
# use of error and bias as operational proxies for uncertainty;
# transition from general uncertainty to discipline-specific risk taxonomies.

Beyond this point, the discussion shifts from semantics to logical frameworks, where uncertainty is partitioned into structured taxonomies suitable for analysis, communication, and decision-making in engineering projects.

==Semantics framework==

===Certainty and uncertainty===
Certainty is commonly described as an epistemic property of belief, implying immunity from doubt or criticism. In practical contexts, however, absolute certainty is neither attainable nor desirable. As Mill observed, assurance sufficient for the purposes of human life does not require freedom from all doubt, but rather justified confidence grounded in experience and evidence.

Uncertainty arises whenever beliefs or propositions are subject to doubt, criticism, or incomplete support. Contemporary treatments emphasize that uncertainty is not merely a feature of the external world, but also a cognitive condition reflecting awareness of incomplete knowledge. Han et al. characterize uncertainty as a meta-cognitive state: knowing that one does not know, or knowing that one’s knowledge may be incomplete or unreliable.<ref name="Han">Han, Paul K. J., William M. P. Klein, and Neeraj K. Arora. “Varieties of Uncertainty in Health Care: A Conceptual Taxonomy.” ''Medical Decision Making'' 31, no. 6 (2011): 828–838.</ref>

In professional reasoning, uncertainty manifests as the potential for error in observation, modeling, judgment, and action. Reasoning under uncertainty therefore differs fundamentally from reasoning under certainty: conclusions are provisional, contingent, and subject to revision as new information becomes available.

===Error, bias, and uncertainty===
In applied disciplines, uncertainty is rarely managed directly. Instead, it is addressed through its observable proxies: error and bias. Error refers to deviation between an estimate, measurement, or prediction and a reference value or observed outcome. Bias denotes systematic or directional error arising from model structure, data selection, judgment, or institutional practice.

Error can often be quantified, propagated, and reduced using analytical and statistical methods. Bias is more difficult to detect and correct, frequently requiring theoretical analysis, validation studies, or comparison across independent methods. The management of uncertainty in civil engineering therefore depends critically on understanding how error and bias arise, how they propagate through sequential decisions, and how they can be bounded through professional controls.

===Propagation of error and uncertainty===
Engineering decisions typically involve multi-step processes: observation, interpretation, modeling, design, construction, and operation. Errors introduced early in this chain can propagate and amplify downstream, while later errors may have limited impact depending on system sensitivity and redundancy.

Professional practice therefore emphasizes identifying where uncertainty enters the decision sequence and prioritizing error and bias reduction where it has the greatest effect on outcomes. This ordering principle underlies practices such as conservative design assumptions, staged investigations, peer review, and independent verification.

===Epistemic, aleatory, and behavioral uncertainty===
For practical purposes, uncertainty in civil engineering can be partitioned into three broad components:
* '''Epistemic uncertainty''', arising from incomplete knowledge, limited data, or imperfect models;
* '''Aleatory uncertainty''', reflecting inherent variability in physical processes;
* '''Behavioral uncertainty''', associated with human judgment, decision-making, and organizational interaction.

This partitioning supports targeted mitigation strategies. Epistemic uncertainty may be reduced through investigation and analysis; aleatory uncertainty may be managed through design margins and reliability methods; behavioral uncertainty requires institutional controls, contractual clarity, and governance mechanisms.

==From uncertainty to risk and hazard==
Risk and hazard are not synonymous with uncertainty, but structured subsets of it. Risk refers to uncertainty that has been framed in terms of potential consequences and that is sufficiently bounded to support decision-making. Hazard denotes a source or condition with the potential to cause harm, independent of likelihood.

In this chapter, '''general risk''' is defined as:
:'''a bounded subset of uncertainty associated with the potential for material error or bias when acting or choosing in physical, economic, or social environments, and that can be represented using coherent analytical or probabilistic frameworks and subjected to testing or validation.'''

This definition is discipline-neutral and applies across engineering, economics, medicine, and policy. Subsequent sections introduce discipline-specific taxonomies that adapt this general concept to the particular decision contexts of civil engineering projects.

==Logical framework for specific risk taxonomies==
Once uncertainty has been reduced to bounded, decision-relevant forms, it becomes possible to develop taxonomies that support analysis, communication, and management. Taxonomies are not required to be epistemically complete; rather, they must be logically distinct, practically exhaustive for their purpose, and aligned with decision needs.

The following sections examine how scientific, legal, economic, and engineering disciplines construct uncertainty and risk taxonomies, and how these perspectives inform civil engineering practice.

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===Scientific risk taxonomies===
Scientific inquiry proceeds by observing phenomena, proposing explanatory models, and testing those models against experience. At every stage of this process, uncertainty is present. Scientific risk taxonomies arise from the need to identify, distinguish, and manage different sources of uncertainty that affect observation, modeling, inference, and prediction.

Most scientific taxonomies of uncertainty have focused on uncertainties associated with data, parameters, and models, particularly those that can be represented statistically. While multiple taxonomies are possible, a common organizing principle is that uncertainty is environment-specific, phenomenon-oriented, and model-centric.

====Environment-specific, phenomenon-oriented, model-centric taxonomy of uncertainty====
A widely used scientific taxonomy distinguishes uncertainty according to its relationship to models and data:

* '''Parameter uncertainty'''
Parameter uncertainty arises from uncertainty in the numerical values of model inputs. These parameters may be physically unobservable, indirectly inferred, or only partially constrained by available data.
** '''Experimental (observational) uncertainty''' results from variability in repeated measurements under nominally identical conditions.
** '''Parametric variability''' reflects natural or contextual variability in input variables that are treated as fixed values within a model.

* '''Structural uncertainty'''
Structural uncertainty, also referred to as model inadequacy or model discrepancy, arises when a model fails to capture relevant features of the underlying phenomenon. Because all models are simplifications of reality, structural uncertainty is unavoidable and depends on model assumptions, scope, and fitness for purpose.
** Structural uncertainty is distinct from parameter uncertainty and cannot be reduced solely by collecting additional data.
** Inadequate model structure can lead to systematically misleading conclusions even when parameter estimates appear precise.

* '''Algorithmic (numerical) uncertainty'''
Algorithmic uncertainty arises from numerical approximations used to implement models computationally. Examples include discretization error, convergence error, and truncation error associated with numerical solution methods such as finite difference or finite element approximations.
** These uncertainties depend**

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==Uncertainty taxonomies in a legal environment==
Legal decision-making operates under conditions of uncertainty that differ in purpose and structure from those of science or engineering. From a legal perspective, a taxonomy is a systematic scheme for distinguishing, ordering, and naming types within a subject field. Such taxonomies are not required to be epistemically complete; rather, they must be sufficient to organize the kinds of descriptive information commonly encountered by decision-makers and to support fair, effective, and defensible judgments.

Walker characterizes legal taxonomies of scientific and technical uncertainty as tools for cataloging uncertainty associated with descriptive assertions, particularly those involving cause-and-effect claims. These taxonomies emphasize clarity, logical distinction, and practical exhaustiveness over theoretical completeness.

===Core components of legal uncertainty===
Legal frameworks distinguish multiple, logically distinct sources of uncertainty that may affect descriptive assertions and decisions:

* '''Linguistic uncertainty'''
Linguistic uncertainty, often described as vagueness, arises from limitations of language rather than from data or models.
** '''Context dependence''' occurs when the applicability of a statement depends on unstated or shifting contextual conditions.
** '''Ambiguity''' arises when terms admit multiple meanings that cannot be resolved within the given context.
** '''Underspecification''' occurs when descriptions lack sufficient detail to support a unique interpretation.
Linguistic uncertainty underlies all other components of legal uncertainty, as it affects how evidence, concepts, and models are interpreted.

* '''Conceptual uncertainty'''
Conceptual uncertainty arises from the choice and definition of concepts used to describe phenomena. Whenever predicates are selected to characterize an object, event, or condition, alternative conceptualizations are excluded.
Conceptualization generates useful structure, but it can also constrain understanding by privileging certain interpretations over others. Poorly chosen or inadequately defined concepts can therefore introduce systematic distortion into legal and technical reasoning.

Conceptual uncertainty is closely related to issues of validity. A concept is valid to the extent that it accurately represents the phenomenon it is intended to describe. Conceptual bias, like systematic error in measurement, is difficult to detect and often recognizable only through theoretical analysis or comparison across alternative frameworks.

* '''Measurement and classification uncertainty'''
Measurement uncertainty arises when concepts are applied to specific cases through observation, assessment, or classification. Even when conceptual uncertainty has been minimized, misclassification may occur due to procedural limitations, observer judgment, or data quality.
Measurement uncertainty affects the reliability and precision of descriptive assertions. Errors at this stage can propagate through subsequent reasoning and reduce the quality of causal inference or decision outcomes.

* '''Sampling uncertainty'''
Sampling uncertainty arises when conclusions about a population are inferred from a limited subset of observations. The uncertainty reflects the extent to which the sample represents the relevant population.
In legal and regulatory contexts, sampling uncertainty affects the credibility of generalizations, forecasts, and risk characterizations based on empirical evidence.

* '''Modeling uncertainty'''
Modeling uncertainty arises when descriptive or causal claims rely on simplified representations of complex systems. This includes uncertainty associated with variable selection, functional relationships, and parameter specification.
** '''Causal uncertainty''' arises when relationships between variables are inferred rather than directly observed. Legal decision-makers often rely on causal models to explain why an outcome occurred or to predict the consequences of alternative actions.
** '''Epistemic choice''' refers to uncertainty introduced by selecting among competing interpretive frameworks or explanatory models.

===Aggregation and interaction of uncertainties===
The components of legal uncertainty do not operate independently. Linguistic, conceptual, measurement, sampling, and modeling uncertainties can combine in different ways to produce aggregate uncertainty affecting the credibility, relevance, and weight of evidence.

Legal taxonomies therefore focus not on eliminating uncertainty, but on making its sources explicit so that decision-makers can assess reliability, assign weight, and determine appropriate standards of proof or precaution.

===Relationship to scientific and engineering practice===
While legal taxonomies do not require quantitative completeness, they depend critically on validated scientific and engineering methods to support descriptive and causal claims. Scientific and engineering disciplines provide procedures and models capable of integrating qualitative and quantitative information into structured representations of uncertainty.

However, the legal use of such information remains distinct. The objective is not optimization or prediction per se, but defensible judgment under uncertainty. Validation, transparency, and clarity of assumptions are therefore central concerns at the interface between legal, scientific, and engineering domains.

These distinctions inform how uncertainty is framed, communicated, and relied upon in civil engineering projects subject to regulatory review, contractual interpretation, or dispute resolution.

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=== Economics Uncertainty Taxonomies ===
Like scientific concepts of uncertainty, economics is environment-specific (in this case, read sector of economy or markets), phenomenon-oriented (in this case, read human activity and manufactured phenomena such as firms and markets guided by human judgment and decision), and model-centric.
:''' Economics is not interested in all forms of uncertainty, only specific, meaningful subsets possessing casual connections with material impacts, often labeled as 'risks'.'''
One of its earlier writers ([https://en.wikipedia.org/wiki/Frank_Knight#Life_and_career Knight], 1921, 1948, 1957) attempted to define "uncertainty" as the presence of "defects of managerial knowledge" (or [https://en.wikipedia.org/wiki/Knightian_uncertainty knightian uncertainty] and thus risk centric) as risk that is immeasurable, not possible to calculate.(Wikipedia) Risk in this sense was defined as "the ordinary risks of business activity which can ... be reduced... by applying the insurance principle." Knight does acknowledge that risk is a specific and unambiguous subset of uncertainty. (Risk, Uncertainty and Profit, 1957, pg. 19) For Knight, measuring uncertainty meant measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences, which Knight referred to as "the imperfection of knowledge". (Risk, Uncertainty and Profit, 1957, pg. 197) One of Knight's many contributions to this analysis was recognizing information and knowledge's role in economic activity.
:"''(W)e are concerned only to emphasize the fact that '''knowledge is in a sense variable in degree and that the practical problem may relate to the degree of knowledge rather than to its presence or absence in toto.''' We live only by knowing something about the future, while the problems of life, or conduct at least, arise from knowing so little. This is as true of business as of other spheres of activity. The situation's essence is action according to opinion, of greater or less foundation and value, neither entire ignorance nor complete and perfect information, but partial knowledge. If we are to understand the workings of the economic system, we must examine the meaning and significance of uncertainty; and to this end, some inquiry into the nature and function of knowledge itself is necessary.''" (Knight, pg. 199) Emphasis added
Knight also reinforced the difficulties in classifying uncertainties when he analyzed life insurance. In this case, accidental death was an uncertain phenomenon compared to sickness and accident, where an "...objective description and classification of cases was impossible..." (Knight, pg. 248) Another factor that made uncertainties ineligible as risks and, therefore, uninsurable because they were unclassifiable was the exercise of judgment in making decisions by the businessman. (Knight, pg. 251)
This is a simple classification of economic uncertainty and, as an idealization, is philosophically controversial. '''note-39 [32]'''
* Uncertainty and information about the economic environment are distinct from uncertainty about others’ behavior or choices.
* Risk as a specific subset of uncertainty implies that "...all possible acts are known, all possible outcomes arising from each act are known, and it is possible to assign probabilities to each act." '''note-40 [33]'''
Like the economic [https://en.wikipedia.org/wiki/Theory_of_the_firm theory of the firm], an economic theory of uncertainty seeks to explain and predict the nature of a specific subset of uncertainty (in this case, risk relating to physical phenomena and human activity), including its behavior, structure, and relationship to the universal uncertainty of physical phenomena. In this sense, uncertainty, like firms and markets, can establish price equilibriums under the right circumstances different from those that might have occurred in either of the other two. Such a theory of uncertainty offers a partitioning scheme that decomposes uncertainty into distinct and meaningful subsets, of which risk is a significant one. It also explains how the presence and variability of knowledge cause or " drive" economic consequences. Unlike physical phenomena, economic activity creates artificial objects such as products, firms, mediums of exchange, markets, economies, and knowledge about those activities. The economics of uncertainty exists as an alternative to "certainty" models that assume away imperfect knowledge and unknown choice/judgment preferences when it is more efficient for economic decision-making. This also allows the introduction of the logical concept of "hazard" versus "risk". Frank used hazard extensively in Risk, Uncertainty, and Profit, but not to the extent that one could say it was interchangeable. An example of this is in the term "[https://en.wikipedia.org/wiki/Moral_hazard Moral hazard]," where a firm could engage in "riskier" and potentially "uninsurable" behavior if the information were transparent, and, therefore, insurance coverage of losses is limited. note-41 [34]], note-42 [35]]

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=== Uncertainty and Risk in Policy and Regulatory Environment ===
:''"Hume's 'just reasoner', when faced with a difficult public policy decision in current times, would probably commission a risk analysis. But how could they incorporate 'a degree of doubt, caution, and modesty?"'' '''note-43 [36]'''
The problem of "decision-making in the face of uncertainty" is how to regulate based on incomplete information that has the potential to be materially inaccurate. A contextual assumption is that we need to evaluate decision rules for dealing with uncertainty. As a social enterprise, risk regulation, whatever its substantive objectives, should be as '''effective, efficient, and equitable''' as practically possible. These "three E's" form a set of "process objectives" or "meta-goals." From the uncertainty standpoint, causal information can be usefully divided into two major categories: information about groups and information about individuals.

Each category, group, and individual has its distinctive types of inherent uncertainty. These are logically distinct, generally independent, and cumulative, contributing to '''(?????)'''

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=== Engineering Uncertainty Taxonomies ===
Like scientific and economic concepts of uncertainty, engineering is environment-specific, phenomena-oriented, model-centric, external choice, and driven by decision-making in the face of uncertainty. Like economics and unlike science, engineering is not interested in all forms of uncertainty, only those with determinable and material consequences. Like economics, engineering conceives risk as a specific and unambiguous subset of uncertainty determined by measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences. Also, engineering recognizes the role information and, by extension, knowledge plays in constructing the built environment.
'''note-44 [37]]''' Like economics, engineering differs from economics and scientific uncertainty in its interest in behavioral uncertainty.
*"Behavioral or interaction uncertainty is how individuals or organizations act or interact. Behavioral uncertainty arises from four sources: design uncertainty, requirement uncertainty, volitional uncertainty, and human errors." '''note-45 [38]]'''
** A design uncertainty is a choice among alternatives over which an individual or group of individuals exercises direct control but has not yet decided upon.
** Requirement uncertainty includes parameters of interest to and determined by the stakeholder, independent of the engineer or designer.
** Volitional uncertainty is uncertainty about what the subject him/herself will decide. Other people’s future actions and conduct are not entirely predictable, particularly when dealing with other organizations.
** Human errors occur during the development of a system or project due to blunders or mistakes by an individual or individuals.

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==Practice Frameworks for Uncertainty and Risk in Civil Engineering Practice==
===PMIBoK===
PMI does not define uncertainty, but the PMI Body of Knowledge uses the term in 45 places (uncertainty) and as describing properties in 8 places (uncertainties). [See Note 8] PMI does define "risk" and uses the term over 1,300 times in the same document. In its glossary, PMI also defines terms such as "threat" and "opportunity" but not events.[391 The BoK contextually defines uncertainty when it states that project risk has its origins in the uncertainty present in all projects. (Op. Cit., pg 309) 
'''Uncertainty''' is contextually defined and taken as having the property of affecting project execution and the ability to meet stakeholder expectations. Uncertainty is more than the sum of all individual risks (known and unknown). The nature of this uncertainty is not defined, only its capacity to affect something else or its properties. 
'''Risk''' is defined as " ... an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives." (Ibid.) Beyond this formal definition, PMI adds context when, in its risk management section (Sec. 11 ), the BoK states that risk is a caused event or condition with multiple causes and impacts. The BoK even offers specific techniques for mapping cause-and-effect relationships and influence diagrams. (Sec. 11.2.2.5) Not as evident but equally important is the recognition that not only is risk caused but the impact is "triggered". The risk trigger is an event or situation that signals that it is about to occur (Glossary, pg. 566). Risk conditions are factors that affect or otherwise contribute to risk, such as project stakeholders. Risk implicitly retains some residual element of probability in it, or a value of less than 1.0 with risk that approaches a level of near certainty is termed "issues" or "realized risk" .(Op. Cit., pg 309). PMI also links risk to underlying variations in project outcomes. (Ibid.) Risk is based upon data and information which can be assessed as to its quality (Sec. 11.3.2.3). This analysis looks to determine the value of the information and examines the degree to which the risk information is understood as well as its " ... accuracy, quality, reliability and integrity ... " (Ibid.) Part of that is understanding the relationship between occurrence and impact as outlined in PMl's Figure 11-10 Risk Impact Matrix and developing a model for prioritizing risk products and setting the threshold for action.
====Commentary:====
For defining and identifying risk, PMI notes in Sec. 11.2.2.4 that " ... Every project and its plan is conceived and developed based on a set of hypotheses, scenarios, or assumptions." Part of the risk analysis process is identifying risks to the project, such as inaccuracy, instability, inconsistency, or incompleteness of assumptions. Similarly, the quality of management plans, as well as their consistency with others and the project objectives and assumptions are 11 •• .indicators of risk in the project." (Sec. 11.2.2.1) 
Similarly, the role of engineering economics in risk is embedded or implied in the PMI definition of risk when the BoK discusses risk identification as a process of assessing which risks out of the total risk exposure for the project "may affect" the project and distilling their characteristics. Embedded in this statement is the concept that not all risks can cause economic impacts (positive and negative) to the project. Also embedded is the requirement to understand risk characteristics to identify project risk (Cost and schedule). Lastly, risk identification is an iterative process of incrementing project risk documentation, much like project scope, cost, and schedule documentation. 
Lastly, PMI acknowledges the complex constitution of risk aggregates or the "sum" of all risks in its material, but is it a sum? Risk information has descriptive and explanatory content. These are knowledge and meta-knowledge components. Understanding project assumptions is project-specific knowledge, but identifying risks from assumption inaccuracy, instability, inconsistency, or incompleteness requires professional or program meta-knowledge. 
'''Uncertainty exists in all projects, but risk, a subset of uncertainty with material economic impacts, is a key meta-knowledge element for civil engineering practice.'''
===Software Development Process- Risk Focused===
The Unified Process requires the project team to focus on addressing the most critical risks early in the project life
cycle. The deliverables of each iteration, especially in the Elaboration phase, must be selected to ensure that the greatest risks are addressed first. <ref> Booch, Grady, Ivar Jacobson, and James Rumbaugh. "The unified software development process."
Reading: Addison Wesley (1999).</ref>
===American Society of Civil Engineers (ASCE)===
ASCE does not define uncertainty, but the ASCE CE Body of Knowledge (CEBoK) uses the term in 22 places (uncertainty) and describes properties in 13 places (uncertainties). 
The CEBoK does not use the phrase "uncertainty and risk" as PMI and AACE do in their publication. ASCE reverses the two (Risk and uncertainty) in twelve places regarding technical outcomes (Outcome 12). A fundamental difference between PMI and ISO is that risk is considered a subset of uncertainty. ASCE does not define "risk" but uses the term 25 times in the same document. Almost all are in the context of variation of design parameters and not in the classical context of cost, schedule uncertainty, and risk.
====Commentary:====
...
=== American Association of Cost Engineers (AACE) ===
AACE defined uncertainty, risk, and other related terms in its Risk Management Dictionary. <ref>Risk Management Committee uRisk Management Dictionary, Cost Engineering Vol. 37/No. 10, OCTOBER 1995. </ref>
AACE's overall strategy was to define risk and other terms that stem from base uncertainty. AACE first defined uncertainty as .... "(t)he total range of events that may happen and produce risks (including both threats and opportunities) affecting a project (see opportunities, events, conditions, risk, and threats" where the following sub-definitions apply:
** Biases--A lack of objectivity based on the individual's position or perspective. Systematic and predictable relationships between a person's opinion or statement and his/her underlying knowledge or circumstances. Note: there may be "system biases" as well as "individual biases."
** Condition (Uncertain Condition)-Any specific identifiable circumstance (such as the rate of inflation or the quality of labor available) that might affect the outcome of the project
** Event (Uncertain Condition)-is a specific identifiable action (such as a large government project being started in the same labor area as your project) or an act of nature that might happen and that (if it does happen) could affect the outcome of the project.
** Opportunities are Uncertain events that could improve the results or improve the probability that the desired outcome will happen
** Threats are Uncertain events that are potentially negative or reduce the probability that the desired outcome will happen.
*** AACE defines Risk as an "ambiguous term" (sic) that is synonymous with uncertainty or a negative subset such as "threats", or an overall negative impact of all possible uncertainties.
====Commentary:====
...

==Limitations of the definition==
...
==Working definition==
...
==Beneficial outcomes==
...
==See also==
Wiki articles on the project.

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==Notes==
<references group="note" />
<li> Implicit in this definition of uncertainty as a "state of' is ... </li>
: " ... a conceptualization of uncertainty as a subjective, cognitive experience of people--a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one's lack of knowledge, without which one could not feel uncertain; '''uncertainty is a form of "meta-cognition" ... ( or alternatively, its main component, meta-knowledge) ... -a knowing about knowing.'''" (Han, 2011, op. cit., Emphasis added)
</ol>
<ol start="5">
<li> Cox wanted his system to satisfy the following conditions:</li>
: Divisibility and comparability- The plausibility of a statement is a real number and depends on the information we have related to the statement.
: Common sense - Plausibilities should vary sensibly with the assessment of plausibilities in the model.
: Consistency - If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). -preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf
</ol>
<ol start="6">
<li> The utility of existing taxonomies of uncertainty in civil engineering and, by extension, "risk" has been unsatisfactory for this very reason. This is particularly true in cases where the source of the uncertainty is conflicting versus incomplete information. See Regan, et. al.,"A taxonomy and treatment of uncertainty for ecology and conservation biology" (2002)
for a survey of scientific taxonomies for uncertainty.</li>
<li> Regan et al. argue that " ... genuine examples of this kind of uncertainty are hard to find. Even classic cases of random experiments like coin tosses and the throwing of dice are deterministic; it is just that we do not have enough information about the dynamic processes and initial conditions to make any sensible estimates about the outcomes. Such processes are, for all intents and purposes, inherently random, but they are not genuinely inherently random. For similar reasons, complex systems such as ecosystems and weather patterns are unlikely to be inherently random. Similarly, chaotic systems are entirely deterministic. They are unpredictable because the deterministic processes generating them and the relevant initial conditions are hard
to fully specify (see Stewart 1989, Sugihara et al. 1990)." (Regan (2002) </li>
<li> The CEBoK uses the phrase "uncertainty and risk" twice in reference to scheduling (Section 6.5,2) and cost estimating (Section 7.2,2) as well as reversing the two (Risk and Uncertainty) also in two places in discussing project life cycle (both in Section 2.4.1) </li>
</ol>

==References==
<references />

Uncertainty and Risk in Civil Engineering Practice

2026-01-20T01:27:58Z

WikiSysop: 2026 01 19 /* Scientific Risk Taxonomies */ GPT edit

'''Life is short and the art long; the occasion instant, experiment perilous, decision difficult.'''
(Hippocrates, as quoted in Fox)

:''See also [[Project Definition]], [[Project Life Cycle and Phase Models]], [[Project Delivery Methods]]''
:''See also [[Technical Outcomes for Civil Engineering: Risk and Uncertainty]]''

==Basic considerations==
Civil engineering practice requires decision-making in environments characterized by incomplete, imperfect, and evolving knowledge. Engineers routinely act in the presence of uncertainty related to physical phenomena, economic constraints, regulatory requirements, and human behavior. The professional problem is not the elimination of uncertainty, which is impossible in practice, but its reduction, partitioning, and management to a degree sufficient to support responsible action.

In this chapter, '''risk''' is treated as a bounded and decision-relevant subset of broader uncertainty. Uncertainty that can be reduced, structured, prioritized, and acted upon using professional knowledge, models, and experience is termed risk. Related but distinct is the concept of '''hazard''', which denotes a source or condition with the potential to cause harm, independent of likelihood or consequence.

This framing aligns with interdisciplinary treatments of risk analysis, which emphasize the role of uncertainty in shaping social, technical, and governance systems over long historical periods. McDaniels and Small describe risk analysis as emerging from the need to act despite imperfect knowledge and argue that societies have progressively shifted from fatalistic acceptance of uncertainty toward its active management through structured analysis and decision frameworks.<ref name="McDaniels">McDaniels, Timothy, and Mitchell Small. ''Risk Analysis and Society: An Interdisciplinary Characterization of the Field''. Cambridge University Press, 2004, p. 1.</ref>

Within civil engineering, this evolution has produced specialized practices focused on managing uncertainty through modeling, design standards, safety factors, reliability analysis, and project controls. These practices rely not only on domain knowledge of materials, loads, and systems, but also on '''meta-knowledge''': knowledge about the limitations, reliability, and appropriate use of engineering knowledge itself.

==Semantic, epistemic, and logical frameworks==
Clear terminology is a prerequisite for coherent risk analysis. In professional practice, inconsistent or interchangeable use of terms such as uncertainty, risk, hazard, error, and bias can obscure decision responsibility and complicate communication among stakeholders.

The semantic framework adopted in this chapter proceeds in stages:
# distinction between certainty and uncertainty;
# refinement of uncertainty into epistemic, aleatory, and behavioral components;
# use of error and bias as operational proxies for uncertainty;
# transition from general uncertainty to discipline-specific risk taxonomies.

Beyond this point, the discussion shifts from semantics to logical frameworks, where uncertainty is partitioned into structured taxonomies suitable for analysis, communication, and decision-making in engineering projects.

==Semantics framework==

===Certainty and uncertainty===
Certainty is commonly described as an epistemic property of belief, implying immunity from doubt or criticism. In practical contexts, however, absolute certainty is neither attainable nor desirable. As Mill observed, assurance sufficient for the purposes of human life does not require freedom from all doubt, but rather justified confidence grounded in experience and evidence.

Uncertainty arises whenever beliefs or propositions are subject to doubt, criticism, or incomplete support. Contemporary treatments emphasize that uncertainty is not merely a feature of the external world, but also a cognitive condition reflecting awareness of incomplete knowledge. Han et al. characterize uncertainty as a meta-cognitive state: knowing that one does not know, or knowing that one’s knowledge may be incomplete or unreliable.<ref name="Han">Han, Paul K. J., William M. P. Klein, and Neeraj K. Arora. “Varieties of Uncertainty in Health Care: A Conceptual Taxonomy.” ''Medical Decision Making'' 31, no. 6 (2011): 828–838.</ref>

In professional reasoning, uncertainty manifests as the potential for error in observation, modeling, judgment, and action. Reasoning under uncertainty therefore differs fundamentally from reasoning under certainty: conclusions are provisional, contingent, and subject to revision as new information becomes available.

===Error, bias, and uncertainty===
In applied disciplines, uncertainty is rarely managed directly. Instead, it is addressed through its observable proxies: error and bias. Error refers to deviation between an estimate, measurement, or prediction and a reference value or observed outcome. Bias denotes systematic or directional error arising from model structure, data selection, judgment, or institutional practice.

Error can often be quantified, propagated, and reduced using analytical and statistical methods. Bias is more difficult to detect and correct, frequently requiring theoretical analysis, validation studies, or comparison across independent methods. The management of uncertainty in civil engineering therefore depends critically on understanding how error and bias arise, how they propagate through sequential decisions, and how they can be bounded through professional controls.

===Propagation of error and uncertainty===
Engineering decisions typically involve multi-step processes: observation, interpretation, modeling, design, construction, and operation. Errors introduced early in this chain can propagate and amplify downstream, while later errors may have limited impact depending on system sensitivity and redundancy.

Professional practice therefore emphasizes identifying where uncertainty enters the decision sequence and prioritizing error and bias reduction where it has the greatest effect on outcomes. This ordering principle underlies practices such as conservative design assumptions, staged investigations, peer review, and independent verification.

===Epistemic, aleatory, and behavioral uncertainty===
For practical purposes, uncertainty in civil engineering can be partitioned into three broad components:
* '''Epistemic uncertainty''', arising from incomplete knowledge, limited data, or imperfect models;
* '''Aleatory uncertainty''', reflecting inherent variability in physical processes;
* '''Behavioral uncertainty''', associated with human judgment, decision-making, and organizational interaction.

This partitioning supports targeted mitigation strategies. Epistemic uncertainty may be reduced through investigation and analysis; aleatory uncertainty may be managed through design margins and reliability methods; behavioral uncertainty requires institutional controls, contractual clarity, and governance mechanisms.

==From uncertainty to risk and hazard==
Risk and hazard are not synonymous with uncertainty, but structured subsets of it. Risk refers to uncertainty that has been framed in terms of potential consequences and that is sufficiently bounded to support decision-making. Hazard denotes a source or condition with the potential to cause harm, independent of likelihood.

In this chapter, '''general risk''' is defined as:
:'''a bounded subset of uncertainty associated with the potential for material error or bias when acting or choosing in physical, economic, or social environments, and that can be represented using coherent analytical or probabilistic frameworks and subjected to testing or validation.'''

This definition is discipline-neutral and applies across engineering, economics, medicine, and policy. Subsequent sections introduce discipline-specific taxonomies that adapt this general concept to the particular decision contexts of civil engineering projects.

==Logical framework for specific risk taxonomies==
Once uncertainty has been reduced to bounded, decision-relevant forms, it becomes possible to develop taxonomies that support analysis, communication, and management. Taxonomies are not required to be epistemically complete; rather, they must be logically distinct, practically exhaustive for their purpose, and aligned with decision needs.

The following sections examine how scientific, legal, economic, and engineering disciplines construct uncertainty and risk taxonomies, and how these perspectives inform civil engineering practice.

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===Scientific risk taxonomies===
Scientific inquiry proceeds by observing phenomena, proposing explanatory models, and testing those models against experience. At every stage of this process, uncertainty is present. Scientific risk taxonomies arise from the need to identify, distinguish, and manage different sources of uncertainty that affect observation, modeling, inference, and prediction.

Most scientific taxonomies of uncertainty have focused on uncertainties associated with data, parameters, and models, particularly those that can be represented statistically. While multiple taxonomies are possible, a common organizing principle is that uncertainty is environment-specific, phenomenon-oriented, and model-centric.

====Environment-specific, phenomenon-oriented, model-centric taxonomy of uncertainty====
A widely used scientific taxonomy distinguishes uncertainty according to its relationship to models and data:

* '''Parameter uncertainty'''
Parameter uncertainty arises from uncertainty in the numerical values of model inputs. These parameters may be physically unobservable, indirectly inferred, or only partially constrained by available data.
** '''Experimental (observational) uncertainty''' results from variability in repeated measurements under nominally identical conditions.
** '''Parametric variability''' reflects natural or contextual variability in input variables that are treated as fixed values within a model.

* '''Structural uncertainty'''
Structural uncertainty, also referred to as model inadequacy or model discrepancy, arises when a model fails to capture relevant features of the underlying phenomenon. Because all models are simplifications of reality, structural uncertainty is unavoidable and depends on model assumptions, scope, and fitness for purpose.
** Structural uncertainty is distinct from parameter uncertainty and cannot be reduced solely by collecting additional data.
** Inadequate model structure can lead to systematically misleading conclusions even when parameter estimates appear precise.

* '''Algorithmic (numerical) uncertainty'''
Algorithmic uncertainty arises from numerical approximations used to implement models computationally. Examples include discretization error, convergence error, and truncation error associated with numerical solution methods such as finite difference or finite element approximations.
** These uncertainties depend**

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== Uncertainty Taxonomies in a Legal Environment ==
From a legal perspective, a definition of taxonomy is dictionary-based, such as "the systematic distinguishing, ordering, and naming of type groups within a subject field." given by Walker (op. cit.). Such legal concepts of scientific or engineering taxonomies are not required to be epistemically complete. Still, they must cover "the most significant aspects of ...(current, good discipline practices) ... and of the descriptive information commonly encountered by decision-makers" in a logically distinct manner. (Walker, 1991, p. 571) These taxonomies must be sufficient to catalog the types of uncertainty associated with descriptive assertion, including the critical subset of cause and effect assertions. This legal/decision-maker framework has presented descriptive uncertainty as having six components (Walker, 1991). Although not part of the Walker taxonomy, underlying the scheme is linguistic uncertainty, or "vagueness," as a source of uncertainty, including uncertainties due to context dependence, ambiguity, and under-specificity. '''note-33 [26]]''' Arguably, these uncertainties underlie the most basic element in the taxonomy.
* '''Linguistic '''- or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. (Regan, 2002)
* '''Conceptual'''- the definition by choice and design of descriptive concepts or variables to be used as [https://en.wikipedia.org/wiki/Predicate_(grammar) predicates] where the subject of an assertion identifies what is being discussed and the predicate provides information about the topic or characterizations such as what the subject is, what the subject is doing, or what the subject is like (Wikipedia).
* "Conceptual uncertainty, or the potential for conceptual error, arises whenever predication occurs. Whenever a concept is used to describe something, using certain concepts instead of others begins to structure how we understand the object, event, or instance under discussion. Predication or conceptualization generates useful information about things, but it also can inhibit our ability to think about those things with concepts other than those selected. The concepts used may not be the most fruitful or the best designed- either for scientific purposes or for making wise, fair, effective, and efficient decisions." (Edited for reading, Walker, 1991)
* Similarly, choice and design of descriptive variables enhance and restrict data set membership, creating potentially incomplete, incompatible, or inconsistent data sets, "...the classification categories employed, and the relationships among those categories (nominal, ordinal or scalar)." '''note-34 [27]]'''
* Concept uncertainty '''note-35 [28]''' like systematic error discussed above is notoriously difficult to recognize, except on theoretical grounds, and the magnitude and direction of the bias are known.
Both terms could be analogized to that of [https://en.wikipedia.org/wiki/Accuracy_and_precision#Terminology_of_ISO_5725 accuracy] in [https://www.bipm.org/en/publications/guides/ ISO 5725] which looks at the ability of the concept or system's ability to produce results that are proximate to the "true" value or state of the phenomenon. Conceptual uncertainty and systemic bias are related to another measure of uncertainty: validity. Walker has defined validity as the ability of a concept to quantitatively describe and explain what the phenomenon objective "truly" is. Walker also ties the concepts together when he states, "Validity concerns the "accuracy" of the measurement data, not its precision." (Walker, 1998) 
Additionally, conceptual uncertainty and associated validity issues could be propagated throughout this taxonomy. Every component of action and decision in the face of uncertainty raises validity issues. Walker recognized this when the author defined epistemic choice for concepts used throughout the other uncertainty components or layers.
* '''Measurement''' uncertainty or Misclassification error- the application of the underlying concepts or variables to specific, individual cases and the uncertainty of the reliability (Walker, 1998) of the resultant data. (It is important to remember that assessment observation and measurement are used interchangeably in this analysis.) Thus, an assessment method or procedure is considered "reliable" or "precise" in the scientific terms of ISO 5715 if it repeatedly produces consistent results.
* Measurement uncertainty is logically distinct from conceptual uncertainty, which can only occur after any conceptual uncertainty has been established. Still, misclassification can occur even when the conceptual uncertainty has been minimized. Conversely, underlying errors or errors made in measurement or classification propagate thru the reasoning chain and reduce the quality or value of the final result.
* While uncertainties associated with quantitative measurements or assessments can be statistically evaluated and described, uncertainties related to qualitative assessments are more problematic.
**Working through the taxonomic chain from base concept to causal explanation requires amassing and integrating qualitative information and quantitative data into input parameters for mathematical models. Developing procedures for producing consistent, repeatable sets of information from assessments that minimize conceptual error or systemic bias requires professional efforts to "validate" such methods, models, and judgments at all layers in the process. Validation as a process is something that civil engineering as a practice needs to embrace more frequently.
* '''[https://en.wikipedia.org/wiki/Sampling Sampling]'''- in the classical statistics sense, is the selection of a limited subset of a larger population to use as a basis for making estimates about the population in the form of attributes, proprieties, or parameters. The resulting information would be determinative in making forecasts about future population sampling or possessing a desired amount of predictive power. The uncertainty about the ability of this information to correctly predict future samples is termed sampling error.
* '''Modeling'''- Modeling uncertainty arises whenever a claim is made that out of a class of candidate relationships and variable pairings, one variable pairing inclusive of constants is chosen that possesses a particular or persistent mathematical relationship to another variable X. Modeling errors arise in selecting the wrong variable pair and constants or by incorrectly specifying its constants. (Walker, 1991, pg. 599)
** '''Causal'''- In causal modeling and analysis, the relationship between causal variables is not strictly a mathematical function. Still, it makes testable predictions and explains "...how a system of variables works, why a system works the way it does, or why it makes sense to think of certain variables as a system" at all." (Walker, 1991, pg. 609)
** '''Epistemic'''- the choice of interpretations for fundamental, logical concepts used throughout the other components or layers.
The components of this uncertainty taxonomy can combine in different ways to "...produce different aggregate uncertainties." '''note-36 [29]'''
:''' Only the scientific and engineering professions possess the capacity to develop and validate procedures and models that combine the different kinds of qualitative '''note-37 [30]''' and quantitative information and their associated potential for error into an aggregate measure or "scalar variables" of uncertainty.'''note-38 [31]]
Scalar variables are quantitative variables whose categories are related by some measure of the relevant property's incremental frequency, degree, or amount. (Walker, 1991) Examples are Modulus of Elasticity, Compression stress in elastic materials, etc.

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=== Economics Uncertainty Taxonomies ===
Like scientific concepts of uncertainty, economics is environment-specific (in this case, read sector of economy or markets), phenomenon-oriented (in this case, read human activity and manufactured phenomena such as firms and markets guided by human judgment and decision), and model-centric.
:''' Economics is not interested in all forms of uncertainty, only specific, meaningful subsets possessing casual connections with material impacts, often labeled as 'risks'.'''
One of its earlier writers ([https://en.wikipedia.org/wiki/Frank_Knight#Life_and_career Knight], 1921, 1948, 1957) attempted to define "uncertainty" as the presence of "defects of managerial knowledge" (or [https://en.wikipedia.org/wiki/Knightian_uncertainty knightian uncertainty] and thus risk centric) as risk that is immeasurable, not possible to calculate.(Wikipedia) Risk in this sense was defined as "the ordinary risks of business activity which can ... be reduced... by applying the insurance principle." Knight does acknowledge that risk is a specific and unambiguous subset of uncertainty. (Risk, Uncertainty and Profit, 1957, pg. 19) For Knight, measuring uncertainty meant measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences, which Knight referred to as "the imperfection of knowledge". (Risk, Uncertainty and Profit, 1957, pg. 197) One of Knight's many contributions to this analysis was recognizing information and knowledge's role in economic activity.
:"''(W)e are concerned only to emphasize the fact that '''knowledge is in a sense variable in degree and that the practical problem may relate to the degree of knowledge rather than to its presence or absence in toto.''' We live only by knowing something about the future, while the problems of life, or conduct at least, arise from knowing so little. This is as true of business as of other spheres of activity. The situation's essence is action according to opinion, of greater or less foundation and value, neither entire ignorance nor complete and perfect information, but partial knowledge. If we are to understand the workings of the economic system, we must examine the meaning and significance of uncertainty; and to this end, some inquiry into the nature and function of knowledge itself is necessary.''" (Knight, pg. 199) Emphasis added
Knight also reinforced the difficulties in classifying uncertainties when he analyzed life insurance. In this case, accidental death was an uncertain phenomenon compared to sickness and accident, where an "...objective description and classification of cases was impossible..." (Knight, pg. 248) Another factor that made uncertainties ineligible as risks and, therefore, uninsurable because they were unclassifiable was the exercise of judgment in making decisions by the businessman. (Knight, pg. 251)
This is a simple classification of economic uncertainty and, as an idealization, is philosophically controversial. '''note-39 [32]'''
* Uncertainty and information about the economic environment are distinct from uncertainty about others’ behavior or choices.
* Risk as a specific subset of uncertainty implies that "...all possible acts are known, all possible outcomes arising from each act are known, and it is possible to assign probabilities to each act." '''note-40 [33]'''
Like the economic [https://en.wikipedia.org/wiki/Theory_of_the_firm theory of the firm], an economic theory of uncertainty seeks to explain and predict the nature of a specific subset of uncertainty (in this case, risk relating to physical phenomena and human activity), including its behavior, structure, and relationship to the universal uncertainty of physical phenomena. In this sense, uncertainty, like firms and markets, can establish price equilibriums under the right circumstances different from those that might have occurred in either of the other two. Such a theory of uncertainty offers a partitioning scheme that decomposes uncertainty into distinct and meaningful subsets, of which risk is a significant one. It also explains how the presence and variability of knowledge cause or " drive" economic consequences. Unlike physical phenomena, economic activity creates artificial objects such as products, firms, mediums of exchange, markets, economies, and knowledge about those activities. The economics of uncertainty exists as an alternative to "certainty" models that assume away imperfect knowledge and unknown choice/judgment preferences when it is more efficient for economic decision-making. This also allows the introduction of the logical concept of "hazard" versus "risk". Frank used hazard extensively in Risk, Uncertainty, and Profit, but not to the extent that one could say it was interchangeable. An example of this is in the term "[https://en.wikipedia.org/wiki/Moral_hazard Moral hazard]," where a firm could engage in "riskier" and potentially "uninsurable" behavior if the information were transparent, and, therefore, insurance coverage of losses is limited. note-41 [34]], note-42 [35]]

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=== Uncertainty and Risk in Policy and Regulatory Environment ===
:''"Hume's 'just reasoner', when faced with a difficult public policy decision in current times, would probably commission a risk analysis. But how could they incorporate 'a degree of doubt, caution, and modesty?"'' '''note-43 [36]'''
The problem of "decision-making in the face of uncertainty" is how to regulate based on incomplete information that has the potential to be materially inaccurate. A contextual assumption is that we need to evaluate decision rules for dealing with uncertainty. As a social enterprise, risk regulation, whatever its substantive objectives, should be as '''effective, efficient, and equitable''' as practically possible. These "three E's" form a set of "process objectives" or "meta-goals." From the uncertainty standpoint, causal information can be usefully divided into two major categories: information about groups and information about individuals.

Each category, group, and individual has its distinctive types of inherent uncertainty. These are logically distinct, generally independent, and cumulative, contributing to '''(?????)'''

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=== Engineering Uncertainty Taxonomies ===
Like scientific and economic concepts of uncertainty, engineering is environment-specific, phenomena-oriented, model-centric, external choice, and driven by decision-making in the face of uncertainty. Like economics and unlike science, engineering is not interested in all forms of uncertainty, only those with determinable and material consequences. Like economics, engineering conceives risk as a specific and unambiguous subset of uncertainty determined by measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences. Also, engineering recognizes the role information and, by extension, knowledge plays in constructing the built environment.
'''note-44 [37]]''' Like economics, engineering differs from economics and scientific uncertainty in its interest in behavioral uncertainty.
*"Behavioral or interaction uncertainty is how individuals or organizations act or interact. Behavioral uncertainty arises from four sources: design uncertainty, requirement uncertainty, volitional uncertainty, and human errors." '''note-45 [38]]'''
** A design uncertainty is a choice among alternatives over which an individual or group of individuals exercises direct control but has not yet decided upon.
** Requirement uncertainty includes parameters of interest to and determined by the stakeholder, independent of the engineer or designer.
** Volitional uncertainty is uncertainty about what the subject him/herself will decide. Other people’s future actions and conduct are not entirely predictable, particularly when dealing with other organizations.
** Human errors occur during the development of a system or project due to blunders or mistakes by an individual or individuals.

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==Practice Frameworks for Uncertainty and Risk in Civil Engineering Practice==
===PMIBoK===
PMI does not define uncertainty, but the PMI Body of Knowledge uses the term in 45 places (uncertainty) and as describing properties in 8 places (uncertainties). [See Note 8] PMI does define "risk" and uses the term over 1,300 times in the same document. In its glossary, PMI also defines terms such as "threat" and "opportunity" but not events.[391 The BoK contextually defines uncertainty when it states that project risk has its origins in the uncertainty present in all projects. (Op. Cit., pg 309) 
'''Uncertainty''' is contextually defined and taken as having the property of affecting project execution and the ability to meet stakeholder expectations. Uncertainty is more than the sum of all individual risks (known and unknown). The nature of this uncertainty is not defined, only its capacity to affect something else or its properties. 
'''Risk''' is defined as " ... an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives." (Ibid.) Beyond this formal definition, PMI adds context when, in its risk management section (Sec. 11 ), the BoK states that risk is a caused event or condition with multiple causes and impacts. The BoK even offers specific techniques for mapping cause-and-effect relationships and influence diagrams. (Sec. 11.2.2.5) Not as evident but equally important is the recognition that not only is risk caused but the impact is "triggered". The risk trigger is an event or situation that signals that it is about to occur (Glossary, pg. 566). Risk conditions are factors that affect or otherwise contribute to risk, such as project stakeholders. Risk implicitly retains some residual element of probability in it, or a value of less than 1.0 with risk that approaches a level of near certainty is termed "issues" or "realized risk" .(Op. Cit., pg 309). PMI also links risk to underlying variations in project outcomes. (Ibid.) Risk is based upon data and information which can be assessed as to its quality (Sec. 11.3.2.3). This analysis looks to determine the value of the information and examines the degree to which the risk information is understood as well as its " ... accuracy, quality, reliability and integrity ... " (Ibid.) Part of that is understanding the relationship between occurrence and impact as outlined in PMl's Figure 11-10 Risk Impact Matrix and developing a model for prioritizing risk products and setting the threshold for action.
====Commentary:====
For defining and identifying risk, PMI notes in Sec. 11.2.2.4 that " ... Every project and its plan is conceived and developed based on a set of hypotheses, scenarios, or assumptions." Part of the risk analysis process is identifying risks to the project, such as inaccuracy, instability, inconsistency, or incompleteness of assumptions. Similarly, the quality of management plans, as well as their consistency with others and the project objectives and assumptions are 11 •• .indicators of risk in the project." (Sec. 11.2.2.1) 
Similarly, the role of engineering economics in risk is embedded or implied in the PMI definition of risk when the BoK discusses risk identification as a process of assessing which risks out of the total risk exposure for the project "may affect" the project and distilling their characteristics. Embedded in this statement is the concept that not all risks can cause economic impacts (positive and negative) to the project. Also embedded is the requirement to understand risk characteristics to identify project risk (Cost and schedule). Lastly, risk identification is an iterative process of incrementing project risk documentation, much like project scope, cost, and schedule documentation. 
Lastly, PMI acknowledges the complex constitution of risk aggregates or the "sum" of all risks in its material, but is it a sum? Risk information has descriptive and explanatory content. These are knowledge and meta-knowledge components. Understanding project assumptions is project-specific knowledge, but identifying risks from assumption inaccuracy, instability, inconsistency, or incompleteness requires professional or program meta-knowledge. 
'''Uncertainty exists in all projects, but risk, a subset of uncertainty with material economic impacts, is a key meta-knowledge element for civil engineering practice.'''
===Software Development Process- Risk Focused===
The Unified Process requires the project team to focus on addressing the most critical risks early in the project life
cycle. The deliverables of each iteration, especially in the Elaboration phase, must be selected to ensure that the greatest risks are addressed first. <ref> Booch, Grady, Ivar Jacobson, and James Rumbaugh. "The unified software development process."
Reading: Addison Wesley (1999).</ref>
===American Society of Civil Engineers (ASCE)===
ASCE does not define uncertainty, but the ASCE CE Body of Knowledge (CEBoK) uses the term in 22 places (uncertainty) and describes properties in 13 places (uncertainties). 
The CEBoK does not use the phrase "uncertainty and risk" as PMI and AACE do in their publication. ASCE reverses the two (Risk and uncertainty) in twelve places regarding technical outcomes (Outcome 12). A fundamental difference between PMI and ISO is that risk is considered a subset of uncertainty. ASCE does not define "risk" but uses the term 25 times in the same document. Almost all are in the context of variation of design parameters and not in the classical context of cost, schedule uncertainty, and risk.
====Commentary:====
...
=== American Association of Cost Engineers (AACE) ===
AACE defined uncertainty, risk, and other related terms in its Risk Management Dictionary. <ref>Risk Management Committee uRisk Management Dictionary, Cost Engineering Vol. 37/No. 10, OCTOBER 1995. </ref>
AACE's overall strategy was to define risk and other terms that stem from base uncertainty. AACE first defined uncertainty as .... "(t)he total range of events that may happen and produce risks (including both threats and opportunities) affecting a project (see opportunities, events, conditions, risk, and threats" where the following sub-definitions apply:
** Biases--A lack of objectivity based on the individual's position or perspective. Systematic and predictable relationships between a person's opinion or statement and his/her underlying knowledge or circumstances. Note: there may be "system biases" as well as "individual biases."
** Condition (Uncertain Condition)-Any specific identifiable circumstance (such as the rate of inflation or the quality of labor available) that might affect the outcome of the project
** Event (Uncertain Condition)-is a specific identifiable action (such as a large government project being started in the same labor area as your project) or an act of nature that might happen and that (if it does happen) could affect the outcome of the project.
** Opportunities are Uncertain events that could improve the results or improve the probability that the desired outcome will happen
** Threats are Uncertain events that are potentially negative or reduce the probability that the desired outcome will happen.
*** AACE defines Risk as an "ambiguous term" (sic) that is synonymous with uncertainty or a negative subset such as "threats", or an overall negative impact of all possible uncertainties.
====Commentary:====
...

==Limitations of the definition==
...
==Working definition==
...
==Beneficial outcomes==
...
==See also==
Wiki articles on the project.

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==Notes==
<references group="note" />
<li> Implicit in this definition of uncertainty as a "state of' is ... </li>
: " ... a conceptualization of uncertainty as a subjective, cognitive experience of people--a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one's lack of knowledge, without which one could not feel uncertain; '''uncertainty is a form of "meta-cognition" ... ( or alternatively, its main component, meta-knowledge) ... -a knowing about knowing.'''" (Han, 2011, op. cit., Emphasis added)
</ol>
<ol start="5">
<li> Cox wanted his system to satisfy the following conditions:</li>
: Divisibility and comparability- The plausibility of a statement is a real number and depends on the information we have related to the statement.
: Common sense - Plausibilities should vary sensibly with the assessment of plausibilities in the model.
: Consistency - If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). -preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf
</ol>
<ol start="6">
<li> The utility of existing taxonomies of uncertainty in civil engineering and, by extension, "risk" has been unsatisfactory for this very reason. This is particularly true in cases where the source of the uncertainty is conflicting versus incomplete information. See Regan, et. al.,"A taxonomy and treatment of uncertainty for ecology and conservation biology" (2002)
for a survey of scientific taxonomies for uncertainty.</li>
<li> Regan et al. argue that " ... genuine examples of this kind of uncertainty are hard to find. Even classic cases of random experiments like coin tosses and the throwing of dice are deterministic; it is just that we do not have enough information about the dynamic processes and initial conditions to make any sensible estimates about the outcomes. Such processes are, for all intents and purposes, inherently random, but they are not genuinely inherently random. For similar reasons, complex systems such as ecosystems and weather patterns are unlikely to be inherently random. Similarly, chaotic systems are entirely deterministic. They are unpredictable because the deterministic processes generating them and the relevant initial conditions are hard
to fully specify (see Stewart 1989, Sugihara et al. 1990)." (Regan (2002) </li>
<li> The CEBoK uses the phrase "uncertainty and risk" twice in reference to scheduling (Section 6.5,2) and cost estimating (Section 7.2,2) as well as reversing the two (Risk and Uncertainty) also in two places in discussing project life cycle (both in Section 2.4.1) </li>
</ol>

==References==
<references />

Uncertainty and Risk in Civil Engineering Practice

2026-01-20T01:25:24Z

WikiSysop: 2026 01 19 GPT recommended edits for first section.

'''Life is short and the art long; the occasion instant, experiment perilous, decision difficult.'''
(Hippocrates, as quoted in Fox)

:''See also [[Project Definition]], [[Project Life Cycle and Phase Models]], [[Project Delivery Methods]]''
:''See also [[Technical Outcomes for Civil Engineering: Risk and Uncertainty]]''

==Basic considerations==
Civil engineering practice requires decision-making in environments characterized by incomplete, imperfect, and evolving knowledge. Engineers routinely act in the presence of uncertainty related to physical phenomena, economic constraints, regulatory requirements, and human behavior. The professional problem is not the elimination of uncertainty, which is impossible in practice, but its reduction, partitioning, and management to a degree sufficient to support responsible action.

In this chapter, '''risk''' is treated as a bounded and decision-relevant subset of broader uncertainty. Uncertainty that can be reduced, structured, prioritized, and acted upon using professional knowledge, models, and experience is termed risk. Related but distinct is the concept of '''hazard''', which denotes a source or condition with the potential to cause harm, independent of likelihood or consequence.

This framing aligns with interdisciplinary treatments of risk analysis, which emphasize the role of uncertainty in shaping social, technical, and governance systems over long historical periods. McDaniels and Small describe risk analysis as emerging from the need to act despite imperfect knowledge and argue that societies have progressively shifted from fatalistic acceptance of uncertainty toward its active management through structured analysis and decision frameworks.<ref name="McDaniels">McDaniels, Timothy, and Mitchell Small. ''Risk Analysis and Society: An Interdisciplinary Characterization of the Field''. Cambridge University Press, 2004, p. 1.</ref>

Within civil engineering, this evolution has produced specialized practices focused on managing uncertainty through modeling, design standards, safety factors, reliability analysis, and project controls. These practices rely not only on domain knowledge of materials, loads, and systems, but also on '''meta-knowledge''': knowledge about the limitations, reliability, and appropriate use of engineering knowledge itself.

==Semantic, epistemic, and logical frameworks==
Clear terminology is a prerequisite for coherent risk analysis. In professional practice, inconsistent or interchangeable use of terms such as uncertainty, risk, hazard, error, and bias can obscure decision responsibility and complicate communication among stakeholders.

The semantic framework adopted in this chapter proceeds in stages:
# distinction between certainty and uncertainty;
# refinement of uncertainty into epistemic, aleatory, and behavioral components;
# use of error and bias as operational proxies for uncertainty;
# transition from general uncertainty to discipline-specific risk taxonomies.

Beyond this point, the discussion shifts from semantics to logical frameworks, where uncertainty is partitioned into structured taxonomies suitable for analysis, communication, and decision-making in engineering projects.

==Semantics framework==

===Certainty and uncertainty===
Certainty is commonly described as an epistemic property of belief, implying immunity from doubt or criticism. In practical contexts, however, absolute certainty is neither attainable nor desirable. As Mill observed, assurance sufficient for the purposes of human life does not require freedom from all doubt, but rather justified confidence grounded in experience and evidence.

Uncertainty arises whenever beliefs or propositions are subject to doubt, criticism, or incomplete support. Contemporary treatments emphasize that uncertainty is not merely a feature of the external world, but also a cognitive condition reflecting awareness of incomplete knowledge. Han et al. characterize uncertainty as a meta-cognitive state: knowing that one does not know, or knowing that one’s knowledge may be incomplete or unreliable.<ref name="Han">Han, Paul K. J., William M. P. Klein, and Neeraj K. Arora. “Varieties of Uncertainty in Health Care: A Conceptual Taxonomy.” ''Medical Decision Making'' 31, no. 6 (2011): 828–838.</ref>

In professional reasoning, uncertainty manifests as the potential for error in observation, modeling, judgment, and action. Reasoning under uncertainty therefore differs fundamentally from reasoning under certainty: conclusions are provisional, contingent, and subject to revision as new information becomes available.

===Error, bias, and uncertainty===
In applied disciplines, uncertainty is rarely managed directly. Instead, it is addressed through its observable proxies: error and bias. Error refers to deviation between an estimate, measurement, or prediction and a reference value or observed outcome. Bias denotes systematic or directional error arising from model structure, data selection, judgment, or institutional practice.

Error can often be quantified, propagated, and reduced using analytical and statistical methods. Bias is more difficult to detect and correct, frequently requiring theoretical analysis, validation studies, or comparison across independent methods. The management of uncertainty in civil engineering therefore depends critically on understanding how error and bias arise, how they propagate through sequential decisions, and how they can be bounded through professional controls.

===Propagation of error and uncertainty===
Engineering decisions typically involve multi-step processes: observation, interpretation, modeling, design, construction, and operation. Errors introduced early in this chain can propagate and amplify downstream, while later errors may have limited impact depending on system sensitivity and redundancy.

Professional practice therefore emphasizes identifying where uncertainty enters the decision sequence and prioritizing error and bias reduction where it has the greatest effect on outcomes. This ordering principle underlies practices such as conservative design assumptions, staged investigations, peer review, and independent verification.

===Epistemic, aleatory, and behavioral uncertainty===
For practical purposes, uncertainty in civil engineering can be partitioned into three broad components:
* '''Epistemic uncertainty''', arising from incomplete knowledge, limited data, or imperfect models;
* '''Aleatory uncertainty''', reflecting inherent variability in physical processes;
* '''Behavioral uncertainty''', associated with human judgment, decision-making, and organizational interaction.

This partitioning supports targeted mitigation strategies. Epistemic uncertainty may be reduced through investigation and analysis; aleatory uncertainty may be managed through design margins and reliability methods; behavioral uncertainty requires institutional controls, contractual clarity, and governance mechanisms.

==From uncertainty to risk and hazard==
Risk and hazard are not synonymous with uncertainty, but structured subsets of it. Risk refers to uncertainty that has been framed in terms of potential consequences and that is sufficiently bounded to support decision-making. Hazard denotes a source or condition with the potential to cause harm, independent of likelihood.

In this chapter, '''general risk''' is defined as:
:'''a bounded subset of uncertainty associated with the potential for material error or bias when acting or choosing in physical, economic, or social environments, and that can be represented using coherent analytical or probabilistic frameworks and subjected to testing or validation.'''

This definition is discipline-neutral and applies across engineering, economics, medicine, and policy. Subsequent sections introduce discipline-specific taxonomies that adapt this general concept to the particular decision contexts of civil engineering projects.

==Logical framework for specific risk taxonomies==
Once uncertainty has been reduced to bounded, decision-relevant forms, it becomes possible to develop taxonomies that support analysis, communication, and management. Taxonomies are not required to be epistemically complete; rather, they must be logically distinct, practically exhaustive for their purpose, and aligned with decision needs.

The following sections examine how scientific, legal, economic, and engineering disciplines construct uncertainty and risk taxonomies, and how these perspectives inform civil engineering practice.

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=== Scientific Risk Taxonomies ===
The [https://en.wikipedia.org/wiki/Scientific_method scientific method] is a body of techniques for investigating phenomena in its environment, acquiring new knowledge, or correcting and integrating previous knowledge. Inherent in that process are uncertainties of many forms. Any one taxonomy of [https://en.wikipedia.org/wiki/Uncertainty scientific uncertainty] has largely been focused on statistical models used to assess and quantify sampling, errors, and parameters, although several different taxonomies are conceivable: note-24 [18]
====Environment-specific, Phenomena oriented, model-centric taxonomy of uncertainties====
** [https://en.wikipedia.org/wiki/Uncertainty_quantification#Sources_of_uncertainty '''Parameter uncertainty'''] the source of which is model parameters that are inputs to the computer model (mathematical model) but whose exact values are unknown and cannot be controlled, or whose values cannot be exactly inferred by statistical methods. Subsets of this type of uncertainty include but are not limited to:
***'''Experimental uncertainty''' is also known as observation error, which comes from the variability of experimental measurements. An example is repeating an experiment measurement several times using exactly the same settings for all inputs/variables and recording the variability. It is a subset of the larger uncertainty component, parameter uncertainty.
*** '''Parametric variability''' comes from the variability of the input variables of the phenomena model and is also a subset of the larger uncertainty component, parameter uncertainty.
** '''Structural uncertainty''', or model inadequacy, model [https://en.wikipedia.org/wiki/bias bias] or "[https://en.wikipedia.org/wiki/Systemic_bias systemic bias]", or model discrepancy, which comes from the lack of knowledge of the underlying true state of the phenomenon or its environment. It depends on how accurately a mathematical model describes the true state of the phenomenon or what is termed "model fitness". Due to the inherent nature of uncertainty in any knowledge body (scientific or engineering), models are only an approximation to reality. Therefore, any conclusions drawn from the model can be very misleading when the underlying basis is not plausible or lacks validity. '''note-25 [19]]'''
*** What is missing in this context is any concept of output variability due to the model itself. Part of this is due to the lack of recognition of "process" in scientific investigation. The concept of the scientific method started with a single investigator, such as Galileo or Michael Faraday, working in their laboratories. It has grown into planet and solar system scale experiments such as data gathered on planetary flybys such as Mars, Pluto, and Ceres to the hunt for the Higgs Boson. Ultimately, science has become more like engineering in the scale and complexity of its investigations and theory aggregates, such as the "Unified theory" of particle physics. Subsets of this type of uncertainty include but are not limited to:
*** '''Algorithmic uncertainty''', or numerical uncertainty, comes from numerical errors and numerical approximations per implementation of the computer model. Most models are too complicated to solve exactly. For example, the [https://en.wikipedia.org/wiki/Finite_element_method finite element method] or [https://en.wikipedia.org/wiki/Finite_difference_method finite difference method] [may be used to approximate a solution [https://en.wikipedia.org/wiki/Partial_differential_equation partial differential equation], which introduces numerical errors. Other examples are numerical integration and infinite sum truncation, which are necessary approximations in numerical implementation.
*** '''Interpolation uncertainty''' comes from a lack of available data collected from computer model simulations or experimental measurements. For other input settings that don't have simulation data or experimental measurements, one must interpolate or extrapolate to predict the corresponding responses.
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====Commentary====
A more comprehensive taxonomy of uncertainties that acknowledges knowledge, data, and linguistic uncertainties is:
*'''Epistemic uncertainty''' -[https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Epistemic uncertainty] or systematic uncertainty, in contrast, reflects limitations in the current “state of knowledge” underlying models themselves, originates from competing theories or models, is not readily quantifiable, and is manifest by subjective confusion or indecision. It includes uncertainty due to measurement limitations, insufficient data, extrapolations and interpolations, and variability over time and space. Epistemic uncertainty is uncertainty about "...some determinate fact ... because of a lack of complete information. '''cite_note-26 [20]]''' Epistemic uncertainty can be classified into six main types:'''cite_note-27 [21]]'''
*''' Measurement error''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Observational_error Measurement error] is uncertainty that manifests itself as (apparently) random variation in the measurement of a quantity. Repeated measurements will vary and demonstrate classic statistical behavior.
* '''Systematic error''' - [https://en.wikipedia.org/wiki/Observational_error Systematic error] occurs due to bias in the measuring equipment, model, analysis, observation, or sampling procedure. It is formally defined as the difference between the true value of the quantity of interest and the value to which the mean of the results converges as sample or data sizes increase. Unlike measurement error, it is not (apparently) random, and therefore, results subject to systematic error alone do not vary about a true value. Systematic error can result from deliberate judgment to exclude (or include) data, parameters, or models that ought not to be excluded (or included).
** "The only way to deal with systematic error is to recognize a bias in the process, model, or procedure and remove it. Systematic error, however, is notoriously difficult to recognize except on theoretical grounds. Corrections may only be applied when the magnitude and direction of the bias are known. Such corrections underlie the application of double-sampling methods in environmental science." (Regan, et. al., op. cit.)
*'''Natural variation''' - [https://en.wikipedia.org/wiki/Natural_process_variation Natural variation] or underlying process variation occurs in systems that "...change (concerning time, space, or other variables) in ways that are difficult to predict...across the full range of temporal and spatial values (or other related variables)." '''note-28 [22]'''
** This form of uncertainty is reduceable utilizing tools such as [https://en.wikipedia.org/wiki/Statistical_process_control statistical process control] and replication of experiments or observations.
*'''Inherent randomness''' - [https://en.wikipedia.org/wiki/Randomness randomness] or [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty aleatory uncertainty] exists because the system is"...in principle, irreducible to a deterministic one (the most well-known case is described by [https://en.wikipedia.org/wiki/Uncertainty_principle Heisenberg’s uncertainty principle] in quantum mechanics)." '''note-29 [23], note-30 [Note 7]'''
** Even if the argument is accepted that it is unlikely that any given physical system at some level is inherently random, it is important for any taxonomy to distinguish between systems that appear random due to incomplete or inconsistent information and those that are intrinsically random. (Again, see Regan (2002)).
*'''Model uncertainty''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling Model uncertainty] occurs in the process of generating a model as a conceptual representation of a particular phenomenon to create a [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling#Overview simplified reflection of reality]. This selectivity of factors and variables creates uncertainty in at least three ways. (Regan (2002))
** '''Variables and processes''' that are regarded as relevant often represent a trade-off between system knowledge and assumed states and model objectives;
** '''Models''' depict observed processes using logical or mathematical constructs based on underlying theories about system states or dynamics using continuous equations to describe discrete processes.
** '''Curve fitting''' (including interpolation and extrapolation) with mathematical expressions using model variables where inputs are discrete data points.
** Regan (2002) argues that '''model uncertainty''' is "...notoriously difficult to quantify and impossible to eliminate ...(and)...(t)he only reliable way of determining how appropriate a model is for prediction is to perform validation studies."
** Subjective judgment occurs due to the interpretation of scarce or error-prone data.
*** The only way to address this type of uncertainty is to "...assign a degree of belief about an event in the form of a subjective probability." (Regan (2002))
*Linguistic uncertainty - Linguistic uncertainty, or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. '''note-31 [24]], note-32 [25]]'''
** '''Context dependence''' is uncertainty concerning the context in which a statement is to be understood;
** '''Ambiguity''' occurs when words have multiple meanings, and further reduction to a single meaning is not logically possible in a given context.
** '''Underspecificity''' occurs in the presence of "unwanted generality" or multiple interpretations, and reduction to a smaller set or even a single interpretation is not logically possible.
*Stochastic or statistical uncertainty - Stochastic or statistical uncertainty, which is sometimes known as [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Aleatoric uncertainty] as well as the subject of [https://en.wikipedia.org/wiki/Stochastic_control stochastic control] pertains to the parameters of a risk model, originates from sampling or measurement error, and can be quantified and mathematically expressed (e.g., using confidence intervals).
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== Uncertainty Taxonomies in a Legal Environment ==
From a legal perspective, a definition of taxonomy is dictionary-based, such as "the systematic distinguishing, ordering, and naming of type groups within a subject field." given by Walker (op. cit.). Such legal concepts of scientific or engineering taxonomies are not required to be epistemically complete. Still, they must cover "the most significant aspects of ...(current, good discipline practices) ... and of the descriptive information commonly encountered by decision-makers" in a logically distinct manner. (Walker, 1991, p. 571) These taxonomies must be sufficient to catalog the types of uncertainty associated with descriptive assertion, including the critical subset of cause and effect assertions. This legal/decision-maker framework has presented descriptive uncertainty as having six components (Walker, 1991). Although not part of the Walker taxonomy, underlying the scheme is linguistic uncertainty, or "vagueness," as a source of uncertainty, including uncertainties due to context dependence, ambiguity, and under-specificity. '''note-33 [26]]''' Arguably, these uncertainties underlie the most basic element in the taxonomy.
* '''Linguistic '''- or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. (Regan, 2002)
* '''Conceptual'''- the definition by choice and design of descriptive concepts or variables to be used as [https://en.wikipedia.org/wiki/Predicate_(grammar) predicates] where the subject of an assertion identifies what is being discussed and the predicate provides information about the topic or characterizations such as what the subject is, what the subject is doing, or what the subject is like (Wikipedia).
* "Conceptual uncertainty, or the potential for conceptual error, arises whenever predication occurs. Whenever a concept is used to describe something, using certain concepts instead of others begins to structure how we understand the object, event, or instance under discussion. Predication or conceptualization generates useful information about things, but it also can inhibit our ability to think about those things with concepts other than those selected. The concepts used may not be the most fruitful or the best designed- either for scientific purposes or for making wise, fair, effective, and efficient decisions." (Edited for reading, Walker, 1991)
* Similarly, choice and design of descriptive variables enhance and restrict data set membership, creating potentially incomplete, incompatible, or inconsistent data sets, "...the classification categories employed, and the relationships among those categories (nominal, ordinal or scalar)." '''note-34 [27]]'''
* Concept uncertainty '''note-35 [28]''' like systematic error discussed above is notoriously difficult to recognize, except on theoretical grounds, and the magnitude and direction of the bias are known.
Both terms could be analogized to that of [https://en.wikipedia.org/wiki/Accuracy_and_precision#Terminology_of_ISO_5725 accuracy] in [https://www.bipm.org/en/publications/guides/ ISO 5725] which looks at the ability of the concept or system's ability to produce results that are proximate to the "true" value or state of the phenomenon. Conceptual uncertainty and systemic bias are related to another measure of uncertainty: validity. Walker has defined validity as the ability of a concept to quantitatively describe and explain what the phenomenon objective "truly" is. Walker also ties the concepts together when he states, "Validity concerns the "accuracy" of the measurement data, not its precision." (Walker, 1998) 
Additionally, conceptual uncertainty and associated validity issues could be propagated throughout this taxonomy. Every component of action and decision in the face of uncertainty raises validity issues. Walker recognized this when the author defined epistemic choice for concepts used throughout the other uncertainty components or layers.
* '''Measurement''' uncertainty or Misclassification error- the application of the underlying concepts or variables to specific, individual cases and the uncertainty of the reliability (Walker, 1998) of the resultant data. (It is important to remember that assessment observation and measurement are used interchangeably in this analysis.) Thus, an assessment method or procedure is considered "reliable" or "precise" in the scientific terms of ISO 5715 if it repeatedly produces consistent results.
* Measurement uncertainty is logically distinct from conceptual uncertainty, which can only occur after any conceptual uncertainty has been established. Still, misclassification can occur even when the conceptual uncertainty has been minimized. Conversely, underlying errors or errors made in measurement or classification propagate thru the reasoning chain and reduce the quality or value of the final result.
* While uncertainties associated with quantitative measurements or assessments can be statistically evaluated and described, uncertainties related to qualitative assessments are more problematic.
**Working through the taxonomic chain from base concept to causal explanation requires amassing and integrating qualitative information and quantitative data into input parameters for mathematical models. Developing procedures for producing consistent, repeatable sets of information from assessments that minimize conceptual error or systemic bias requires professional efforts to "validate" such methods, models, and judgments at all layers in the process. Validation as a process is something that civil engineering as a practice needs to embrace more frequently.
* '''[https://en.wikipedia.org/wiki/Sampling Sampling]'''- in the classical statistics sense, is the selection of a limited subset of a larger population to use as a basis for making estimates about the population in the form of attributes, proprieties, or parameters. The resulting information would be determinative in making forecasts about future population sampling or possessing a desired amount of predictive power. The uncertainty about the ability of this information to correctly predict future samples is termed sampling error.
* '''Modeling'''- Modeling uncertainty arises whenever a claim is made that out of a class of candidate relationships and variable pairings, one variable pairing inclusive of constants is chosen that possesses a particular or persistent mathematical relationship to another variable X. Modeling errors arise in selecting the wrong variable pair and constants or by incorrectly specifying its constants. (Walker, 1991, pg. 599)
** '''Causal'''- In causal modeling and analysis, the relationship between causal variables is not strictly a mathematical function. Still, it makes testable predictions and explains "...how a system of variables works, why a system works the way it does, or why it makes sense to think of certain variables as a system" at all." (Walker, 1991, pg. 609)
** '''Epistemic'''- the choice of interpretations for fundamental, logical concepts used throughout the other components or layers.
The components of this uncertainty taxonomy can combine in different ways to "...produce different aggregate uncertainties." '''note-36 [29]'''
:''' Only the scientific and engineering professions possess the capacity to develop and validate procedures and models that combine the different kinds of qualitative '''note-37 [30]''' and quantitative information and their associated potential for error into an aggregate measure or "scalar variables" of uncertainty.'''note-38 [31]]
Scalar variables are quantitative variables whose categories are related by some measure of the relevant property's incremental frequency, degree, or amount. (Walker, 1991) Examples are Modulus of Elasticity, Compression stress in elastic materials, etc.

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=== Economics Uncertainty Taxonomies ===
Like scientific concepts of uncertainty, economics is environment-specific (in this case, read sector of economy or markets), phenomenon-oriented (in this case, read human activity and manufactured phenomena such as firms and markets guided by human judgment and decision), and model-centric.
:''' Economics is not interested in all forms of uncertainty, only specific, meaningful subsets possessing casual connections with material impacts, often labeled as 'risks'.'''
One of its earlier writers ([https://en.wikipedia.org/wiki/Frank_Knight#Life_and_career Knight], 1921, 1948, 1957) attempted to define "uncertainty" as the presence of "defects of managerial knowledge" (or [https://en.wikipedia.org/wiki/Knightian_uncertainty knightian uncertainty] and thus risk centric) as risk that is immeasurable, not possible to calculate.(Wikipedia) Risk in this sense was defined as "the ordinary risks of business activity which can ... be reduced... by applying the insurance principle." Knight does acknowledge that risk is a specific and unambiguous subset of uncertainty. (Risk, Uncertainty and Profit, 1957, pg. 19) For Knight, measuring uncertainty meant measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences, which Knight referred to as "the imperfection of knowledge". (Risk, Uncertainty and Profit, 1957, pg. 197) One of Knight's many contributions to this analysis was recognizing information and knowledge's role in economic activity.
:"''(W)e are concerned only to emphasize the fact that '''knowledge is in a sense variable in degree and that the practical problem may relate to the degree of knowledge rather than to its presence or absence in toto.''' We live only by knowing something about the future, while the problems of life, or conduct at least, arise from knowing so little. This is as true of business as of other spheres of activity. The situation's essence is action according to opinion, of greater or less foundation and value, neither entire ignorance nor complete and perfect information, but partial knowledge. If we are to understand the workings of the economic system, we must examine the meaning and significance of uncertainty; and to this end, some inquiry into the nature and function of knowledge itself is necessary.''" (Knight, pg. 199) Emphasis added
Knight also reinforced the difficulties in classifying uncertainties when he analyzed life insurance. In this case, accidental death was an uncertain phenomenon compared to sickness and accident, where an "...objective description and classification of cases was impossible..." (Knight, pg. 248) Another factor that made uncertainties ineligible as risks and, therefore, uninsurable because they were unclassifiable was the exercise of judgment in making decisions by the businessman. (Knight, pg. 251)
This is a simple classification of economic uncertainty and, as an idealization, is philosophically controversial. '''note-39 [32]'''
* Uncertainty and information about the economic environment are distinct from uncertainty about others’ behavior or choices.
* Risk as a specific subset of uncertainty implies that "...all possible acts are known, all possible outcomes arising from each act are known, and it is possible to assign probabilities to each act." '''note-40 [33]'''
Like the economic [https://en.wikipedia.org/wiki/Theory_of_the_firm theory of the firm], an economic theory of uncertainty seeks to explain and predict the nature of a specific subset of uncertainty (in this case, risk relating to physical phenomena and human activity), including its behavior, structure, and relationship to the universal uncertainty of physical phenomena. In this sense, uncertainty, like firms and markets, can establish price equilibriums under the right circumstances different from those that might have occurred in either of the other two. Such a theory of uncertainty offers a partitioning scheme that decomposes uncertainty into distinct and meaningful subsets, of which risk is a significant one. It also explains how the presence and variability of knowledge cause or " drive" economic consequences. Unlike physical phenomena, economic activity creates artificial objects such as products, firms, mediums of exchange, markets, economies, and knowledge about those activities. The economics of uncertainty exists as an alternative to "certainty" models that assume away imperfect knowledge and unknown choice/judgment preferences when it is more efficient for economic decision-making. This also allows the introduction of the logical concept of "hazard" versus "risk". Frank used hazard extensively in Risk, Uncertainty, and Profit, but not to the extent that one could say it was interchangeable. An example of this is in the term "[https://en.wikipedia.org/wiki/Moral_hazard Moral hazard]," where a firm could engage in "riskier" and potentially "uninsurable" behavior if the information were transparent, and, therefore, insurance coverage of losses is limited. note-41 [34]], note-42 [35]]

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=== Uncertainty and Risk in Policy and Regulatory Environment ===
:''"Hume's 'just reasoner', when faced with a difficult public policy decision in current times, would probably commission a risk analysis. But how could they incorporate 'a degree of doubt, caution, and modesty?"'' '''note-43 [36]'''
The problem of "decision-making in the face of uncertainty" is how to regulate based on incomplete information that has the potential to be materially inaccurate. A contextual assumption is that we need to evaluate decision rules for dealing with uncertainty. As a social enterprise, risk regulation, whatever its substantive objectives, should be as '''effective, efficient, and equitable''' as practically possible. These "three E's" form a set of "process objectives" or "meta-goals." From the uncertainty standpoint, causal information can be usefully divided into two major categories: information about groups and information about individuals.

Each category, group, and individual has its distinctive types of inherent uncertainty. These are logically distinct, generally independent, and cumulative, contributing to '''(?????)'''

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=== Engineering Uncertainty Taxonomies ===
Like scientific and economic concepts of uncertainty, engineering is environment-specific, phenomena-oriented, model-centric, external choice, and driven by decision-making in the face of uncertainty. Like economics and unlike science, engineering is not interested in all forms of uncertainty, only those with determinable and material consequences. Like economics, engineering conceives risk as a specific and unambiguous subset of uncertainty determined by measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences. Also, engineering recognizes the role information and, by extension, knowledge plays in constructing the built environment.
'''note-44 [37]]''' Like economics, engineering differs from economics and scientific uncertainty in its interest in behavioral uncertainty.
*"Behavioral or interaction uncertainty is how individuals or organizations act or interact. Behavioral uncertainty arises from four sources: design uncertainty, requirement uncertainty, volitional uncertainty, and human errors." '''note-45 [38]]'''
** A design uncertainty is a choice among alternatives over which an individual or group of individuals exercises direct control but has not yet decided upon.
** Requirement uncertainty includes parameters of interest to and determined by the stakeholder, independent of the engineer or designer.
** Volitional uncertainty is uncertainty about what the subject him/herself will decide. Other people’s future actions and conduct are not entirely predictable, particularly when dealing with other organizations.
** Human errors occur during the development of a system or project due to blunders or mistakes by an individual or individuals.

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==Practice Frameworks for Uncertainty and Risk in Civil Engineering Practice==
===PMIBoK===
PMI does not define uncertainty, but the PMI Body of Knowledge uses the term in 45 places (uncertainty) and as describing properties in 8 places (uncertainties). [See Note 8] PMI does define "risk" and uses the term over 1,300 times in the same document. In its glossary, PMI also defines terms such as "threat" and "opportunity" but not events.[391 The BoK contextually defines uncertainty when it states that project risk has its origins in the uncertainty present in all projects. (Op. Cit., pg 309) 
'''Uncertainty''' is contextually defined and taken as having the property of affecting project execution and the ability to meet stakeholder expectations. Uncertainty is more than the sum of all individual risks (known and unknown). The nature of this uncertainty is not defined, only its capacity to affect something else or its properties. 
'''Risk''' is defined as " ... an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives." (Ibid.) Beyond this formal definition, PMI adds context when, in its risk management section (Sec. 11 ), the BoK states that risk is a caused event or condition with multiple causes and impacts. The BoK even offers specific techniques for mapping cause-and-effect relationships and influence diagrams. (Sec. 11.2.2.5) Not as evident but equally important is the recognition that not only is risk caused but the impact is "triggered". The risk trigger is an event or situation that signals that it is about to occur (Glossary, pg. 566). Risk conditions are factors that affect or otherwise contribute to risk, such as project stakeholders. Risk implicitly retains some residual element of probability in it, or a value of less than 1.0 with risk that approaches a level of near certainty is termed "issues" or "realized risk" .(Op. Cit., pg 309). PMI also links risk to underlying variations in project outcomes. (Ibid.) Risk is based upon data and information which can be assessed as to its quality (Sec. 11.3.2.3). This analysis looks to determine the value of the information and examines the degree to which the risk information is understood as well as its " ... accuracy, quality, reliability and integrity ... " (Ibid.) Part of that is understanding the relationship between occurrence and impact as outlined in PMl's Figure 11-10 Risk Impact Matrix and developing a model for prioritizing risk products and setting the threshold for action.
====Commentary:====
For defining and identifying risk, PMI notes in Sec. 11.2.2.4 that " ... Every project and its plan is conceived and developed based on a set of hypotheses, scenarios, or assumptions." Part of the risk analysis process is identifying risks to the project, such as inaccuracy, instability, inconsistency, or incompleteness of assumptions. Similarly, the quality of management plans, as well as their consistency with others and the project objectives and assumptions are 11 •• .indicators of risk in the project." (Sec. 11.2.2.1) 
Similarly, the role of engineering economics in risk is embedded or implied in the PMI definition of risk when the BoK discusses risk identification as a process of assessing which risks out of the total risk exposure for the project "may affect" the project and distilling their characteristics. Embedded in this statement is the concept that not all risks can cause economic impacts (positive and negative) to the project. Also embedded is the requirement to understand risk characteristics to identify project risk (Cost and schedule). Lastly, risk identification is an iterative process of incrementing project risk documentation, much like project scope, cost, and schedule documentation. 
Lastly, PMI acknowledges the complex constitution of risk aggregates or the "sum" of all risks in its material, but is it a sum? Risk information has descriptive and explanatory content. These are knowledge and meta-knowledge components. Understanding project assumptions is project-specific knowledge, but identifying risks from assumption inaccuracy, instability, inconsistency, or incompleteness requires professional or program meta-knowledge. 
'''Uncertainty exists in all projects, but risk, a subset of uncertainty with material economic impacts, is a key meta-knowledge element for civil engineering practice.'''
===Software Development Process- Risk Focused===
The Unified Process requires the project team to focus on addressing the most critical risks early in the project life
cycle. The deliverables of each iteration, especially in the Elaboration phase, must be selected to ensure that the greatest risks are addressed first. <ref> Booch, Grady, Ivar Jacobson, and James Rumbaugh. "The unified software development process."
Reading: Addison Wesley (1999).</ref>
===American Society of Civil Engineers (ASCE)===
ASCE does not define uncertainty, but the ASCE CE Body of Knowledge (CEBoK) uses the term in 22 places (uncertainty) and describes properties in 13 places (uncertainties). 
The CEBoK does not use the phrase "uncertainty and risk" as PMI and AACE do in their publication. ASCE reverses the two (Risk and uncertainty) in twelve places regarding technical outcomes (Outcome 12). A fundamental difference between PMI and ISO is that risk is considered a subset of uncertainty. ASCE does not define "risk" but uses the term 25 times in the same document. Almost all are in the context of variation of design parameters and not in the classical context of cost, schedule uncertainty, and risk.
====Commentary:====
...
=== American Association of Cost Engineers (AACE) ===
AACE defined uncertainty, risk, and other related terms in its Risk Management Dictionary. <ref>Risk Management Committee uRisk Management Dictionary, Cost Engineering Vol. 37/No. 10, OCTOBER 1995. </ref>
AACE's overall strategy was to define risk and other terms that stem from base uncertainty. AACE first defined uncertainty as .... "(t)he total range of events that may happen and produce risks (including both threats and opportunities) affecting a project (see opportunities, events, conditions, risk, and threats" where the following sub-definitions apply:
** Biases--A lack of objectivity based on the individual's position or perspective. Systematic and predictable relationships between a person's opinion or statement and his/her underlying knowledge or circumstances. Note: there may be "system biases" as well as "individual biases."
** Condition (Uncertain Condition)-Any specific identifiable circumstance (such as the rate of inflation or the quality of labor available) that might affect the outcome of the project
** Event (Uncertain Condition)-is a specific identifiable action (such as a large government project being started in the same labor area as your project) or an act of nature that might happen and that (if it does happen) could affect the outcome of the project.
** Opportunities are Uncertain events that could improve the results or improve the probability that the desired outcome will happen
** Threats are Uncertain events that are potentially negative or reduce the probability that the desired outcome will happen.
*** AACE defines Risk as an "ambiguous term" (sic) that is synonymous with uncertainty or a negative subset such as "threats", or an overall negative impact of all possible uncertainties.
====Commentary:====
...

==Limitations of the definition==
...
==Working definition==
...
==Beneficial outcomes==
...
==See also==
Wiki articles on the project.

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==Notes==
<references group="note" />
<li> Implicit in this definition of uncertainty as a "state of' is ... </li>
: " ... a conceptualization of uncertainty as a subjective, cognitive experience of people--a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one's lack of knowledge, without which one could not feel uncertain; '''uncertainty is a form of "meta-cognition" ... ( or alternatively, its main component, meta-knowledge) ... -a knowing about knowing.'''" (Han, 2011, op. cit., Emphasis added)
</ol>
<ol start="5">
<li> Cox wanted his system to satisfy the following conditions:</li>
: Divisibility and comparability- The plausibility of a statement is a real number and depends on the information we have related to the statement.
: Common sense - Plausibilities should vary sensibly with the assessment of plausibilities in the model.
: Consistency - If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). -preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf
</ol>
<ol start="6">
<li> The utility of existing taxonomies of uncertainty in civil engineering and, by extension, "risk" has been unsatisfactory for this very reason. This is particularly true in cases where the source of the uncertainty is conflicting versus incomplete information. See Regan, et. al.,"A taxonomy and treatment of uncertainty for ecology and conservation biology" (2002)
for a survey of scientific taxonomies for uncertainty.</li>
<li> Regan et al. argue that " ... genuine examples of this kind of uncertainty are hard to find. Even classic cases of random experiments like coin tosses and the throwing of dice are deterministic; it is just that we do not have enough information about the dynamic processes and initial conditions to make any sensible estimates about the outcomes. Such processes are, for all intents and purposes, inherently random, but they are not genuinely inherently random. For similar reasons, complex systems such as ecosystems and weather patterns are unlikely to be inherently random. Similarly, chaotic systems are entirely deterministic. They are unpredictable because the deterministic processes generating them and the relevant initial conditions are hard
to fully specify (see Stewart 1989, Sugihara et al. 1990)." (Regan (2002) </li>
<li> The CEBoK uses the phrase "uncertainty and risk" twice in reference to scheduling (Section 6.5,2) and cost estimating (Section 7.2,2) as well as reversing the two (Risk and Uncertainty) also in two places in discussing project life cycle (both in Section 2.4.1) </li>
</ol>

==References==
<references />

Bridging the Gaps: Better Infrastructure Project Management and Civil Engineering

2025-02-11T16:24:09Z

WikiSysop: Expanding article

'''Bridging the Gaps: Better Infrastructure Project Management and Civil Engineering

Practices Based on Lessons from the Second Avenue Subway Project'''

== Abstract==
The importance of thorough geotechnical investigations, effective communication, and adaptability to ensure the completion of complex transit projects on time and under budget cannot be emphasized enough. The Second Avenue Subway (SAS) project in New York City has come under intense scrutiny owing to numerous delays and massive cost overruns. This paper highlights the need for a transformation in civil engineering graduate programs to better equip students and practitioners for the complexities of transit project deliveries. Due to an overemphasis on theoretical computational practices, civil engineers have neglected the development and interpretation of policy knowledge, leading to critical decisions being made by those with limited sustainability understanding. This trend, combined with the ineffective communication of technical aspects, has contributed to escalating costs and misaligned sustainability outcomes in transit projects. Herein, a holistic learning approach that encompasses project management, public policy, and real-world case studies is presented to bridge the current knowledge gap and prepare future professionals for successfully delivering sustainable, on-time, within-budget transit projects.
Herein, the management, engineering, and administrative practices employed in the SAS project are comprehensively analyzed. Key assumptions made by the project management and design teams are highlighted that led to unexpected costs and delays. The role of the Metropolitan Transportation Authority (MTA) as an administrative agency is examined and gaps in engineering knowledge and practices within the MTA are identified.
The analysis results demonstrate the need to address the financial sustainability of transit projects and obtain a funding equilibrium. In addition, the MTA needs to prioritize hiring and promoting individuals with engineering credentials, increase transparency and accountability, and foster stronger partnerships with external stakeholders.
The lessons learned from the SAS project can be used to inform future infrastructure projects and guide the transformation of the MTA into a more efficient, cost-effective, and responsive organization.
Practical Applications

This paper addresses the challenges faced by civil engineering professionals in delivering transit projects on time and within budget. Modern civil engineers must have a diverse set of skills, encompassing engineering, planning, economics, environmental science, management, finance, and law. Additionally, they must understand public policy and sociology while being updated on the challenges of translating public policies into engineering outcomes.
The paper emphasizes the need for meaningful change in civil engineering graduate school programs to include project management, public policy, and professional practices using real-world cases and public projects. This will equip students and practitioners with necessary tools to plan and manage transit projects effectively.
Because of the increased complexity of US public administration since WWII, learning transit fundamentals and problem-solving techniques within the context of delivering sustainable projects by graduate students is urgently required. Additionally, this paper underscores the importance of modifying civil engineering education to better prepare professionals for the challenges and opportunities in the realm of transit project delivery.

==Introduction==

Unlike other engineering disciplines, civil engineering is inextricably intertwined with public projects, which differ dramatically from other engineering products because they do not follow a single pattern or protocol (Harris and McCaffer 2013). Public projects develop in response to particular societal and governmental needs, and thus the broader economic, environmental, and political contexts in which they are embedded must be considered. The successful completion of these projects requires civil engineers who are not just technically competent but also able to understand public policy as well as political, environmental, and sociological concerns (Labuschagne and Brent 2004). Relatedly, civil engineers do not develop or work on products that can be sold to generate revenue but on projects that must secure governmental funding and approval as well as satisfy not only individual users but also public stakeholders (Koppenjan and Enserink 2009).
Kaufman (1956) outlined three distinct phases in the evolution of administrative leadership in the United States to address the core value of representativeness in a democracy. The first phase focused on representativeness in the nascent government with “No taxation without representation” as the principal interest of independence. The second phase was the quest for “neutral competence” that began after the Civil War and continues to this day. The aim was for the government to work based on explicit and objective standards rather than personal, party, or other obligations and loyalties. The slogan “Take administration out of politics” gave rise to the Civil Service Act of 1883, which aimed to control the selection of government workers by establishing a merit-based hiring system and offering formal training to civil servants at universities (Kaufman 1956). Although states and localities were slow to adopt these practices, they have made significant strides in recent years. The third phase focuses on the need for “executive leadership” to bridge the gap between the unresponsiveness of neutral competence and the public’s demand for clear communication. Civil engineers can play a crucial role in this process by negotiating effectively with bureaucratic entities and forming alliances with legislative committees and groups, which will allow them to gain greater autonomy in decision-making if they can establish clear expectations for knowledge and outcomes.
However, civil engineering has not yet established best practices in three areas: public policy, project management, and professional practice. This has posed challenges for individual engineers within powerful organizations to influence policy decisions for the public’s benefit.
In the United States, transit projects have long been a source of frustration for the public because of their high costs and delays. Media headlines have highlighted these issues and have painted a picture of inefficiency and exorbitance in the public sector (Gordon and Schleicher 2015, Rosenthal and Fitzsimmons 2017). Scholars and policy analysts have proposed various explanations for these problems, including the cost of land, regulations, and community opposition (Glaeser and Poterba 2020). However, the evolution of civil engineering, particularly the narrowing of the knowledge base and skill set associated with the profession, may be a significant but overlooked factor. Of course, this narrowing is not a development exclusive to civil engineering alone. Abbott (1986) noted that professions often become more specialized over time, which leads to a reduction in the knowledge and skill set of individuals. However, while such specialization may make sense in other professions, the narrowing of civil engineering has contributed to a failure to fully integrate the technical requirements of transit projects with political, social, environmental, and economic needs. This has resulted in a disconnect between the technical aspects of transit projects, which civil engineers are trained to address, and the non-technical aspects, which they are not. This has led to increased costs and public frustration. Civil engineers can play a significant role in overcoming this disconnect and contributing to the development and execution of successful and sustainable transit projects, but only if their own education and training shifts to (re)emphasizing sustainability. A clearer understanding of how the capacity and role played by civil engineers has changed over time is needed to help address these problems.
The Second Avenue Subway (SAS) project in New York City (NYC) has garnered attention because of design issues, construction challenges, and decisions that led to massive cost overruns. This project faced numerous design challenges including building a new subway line in a densely populated urban area and accounting for varying geological conditions along the route. Ongoing criticism of the SAS project’s cost and delays in completion have resulted in scrutiny of the project management and decision-making processes. In this paper, the SAS project is used as a case study to highlight the need for a more detailed study on engineering alternatives and the establishment of best practices in project management to prevent cost overruns and delays in future projects.

== Civil Engineering Knowledge==
===From 1800 to 1950===
In the early days of civil engineering in the United States, ad hoc public commissions or chartered companies managed public construction projects (Kaufman 1956). With the evolution of the field, civil engineering became known as “land development” (Williams 1922). The primary concern of such commissions was feasibility, which led to the first wave of public projects consisting of canal construction (Poor 1860). The Concord Canal Report (1825) emphasized assessing a project’s economic and technical value before execution, which necessitated consulting experts in mechanics, hydraulics, geology, and soils to estimate the costs, benefits, and results. When debating whether Massachusetts should promote canals or railroads, Governor Levi Lincoln consulted experts in various fields to resolve questions related to labor, expenses, and project benefits and results (Poor 1860).
The Civil War represented a turning point for civil engineering as railroads were constructed to facilitate transportation of Union armies, which laid the groundwork for modern civil engineering. In the post-Civil War era, civil engineers collaborated with investors to finance and build railroads, which significantly contributed to the country’s economic growth (Civil War Railroad Report 1865). Railway engineers needed knowledge in finance and business management because private financiers were the primary investors in railroads while the government granted rights of way. Gotshall (1903) highlighted the importance of these skills by demonstrating the practicability of high-speed electric railways from both engineering and commercial perspectives. In the late 19th century, Thurston argued for a dedicated method of publication to provide engineers with detailed and specific knowledge in their field. He called for an institution devoted to providing individuals with technical training and practical laboratory experience (Durand 1929), which aligned with the trend for various fields at the time of specialization in knowledge and skills. The emergence of professional societies such as the American Society of Civil Engineers (ASCE) facilitated this trend by providing a platform for knowledge exchange and expertise. Thurston’s vision and the emergence of professional societies laid the foundation for the growth and development of civil engineering as a full-fledged profession in the United States. However, this also led to the emergence of new administrative challenges and changes that significantly affected its ability to expand its intellectual dominance among engineering disciplines (Durand 1929).
At the beginning of the 20th century, civil engineering encompassed a combination of factual, conceptual, and procedural knowledge. Unlike law and medicine, engineering schools were founded by college-trained professors who designed the curricula. Although the apprenticeship method was used to train engineers, it never developed into significant engineering schools. Teaching theory before practice led to many first- and second-year students feeling disoriented and disheartened because they continued to engage in the same routine of reading books and studying abstract symbols as they did in high school (Durand 1929). This disconnection between academia and engineering practice became evident during the construction of massive projects that required unprecedented coordination, planning, and management. Parsons (1902), who was the Chief Engineer of the NYC Rapid Transit Commission at the time, warned that the engineer of the future would need to deal not only with calculations but also with human needs, industrial demand, finance, and legislation. Engineers would need to combine technical and economic expertise to conceive, plan, design, execute, and manage projects.

===From 1950 to the Present===
The post-World War II era brought several challenges to civil engineering, including the bankruptcy of the railroad industry, massive administrative changes, and the widening gap between academia and professional practice. The United States underwent a major administrative shift by transitioning from ad hoc decision-making when funding transit projects to a more structured and centralized system emphasizing executive leadership, efficiency, and data-driven decisions (Barzelay 2001). This approach influenced agencies such as the Federal Transit Administration (FTA), where urban planners began to outnumber civil engineers (Lewis 2004). Despite improvements in transit planning, a disconnect persisted between professionals and politicians, which raised concerns about the effectiveness of decision-making and implementation (Levinson 2003). Urban planning evolved as a profession in response to the challenges of urbanization in the late 19th century (Hall 2002). Initially, city planning focused on the City Beautiful concept. However, as cities expanded, land economics became increasingly important (Filion and Hammond 2003). Lawyers, civil engineers, and urban planners all began playing key roles in shaping American infrastructure (Peterson 2003).
The ASCE did not heed Parsons’ warning from the early 20th century, and they neglected to advance their knowledge in project management, public policy, and professional practice (Fitch 1999). When the transit industry went bankrupt, the National Environmental Policy Act (NEPA, Department of Energy 1970) was published, and the FTA issued several policy documents on funding strategies for transit projects. Civil engineers struggled to add engineering interpretations of these policy documents with regard to recommending best practices and ensuring that new engineers are educated accordingly in universities (Barzelay 2001). In addition, a governmental shift toward a business-oriented approach led to a decline in practical professors and licensed engineers, which caused a loss of influence by civil engineers (Dzur 2008). Meanwhile, organizations such as the Project Management Institute have enabled managers without an engineering background to assume high decision-making positions within governments and engineering firms offering construction management services (Kerzner 2009). Abbott (1986) argued that business-oriented practices are more applicable to professions that lack technical roots and thus prioritize business strategies. However, professions such as civil engineering and medicine derive their legitimacy not just from efficiency and quality management but also from the technical skill and experience inherent to their fields.
Following the Civil Service Act and the rise of neutral competence, civil engineers should have developed a series of management practices tailored to enhance the overall functioning of transit organizations, which would have helped them to effectively manage and maintain the developed infrastructure as well as provide informed engineering interpretations of policy documents such as the NEPA (Perry and Rainey 1988). Unfortunately, civil engineers did not work on developing and interpreting policy knowledge, but rather they became focused on a narrow scope of computational practices in design, which was driven by an excessive theoretical focus in academia (Petroski 2011). This knowledge gap, combined with the lack of participation by civil engineers in interpreting the NEPA, led to major planning decisions being made by planners and economists with a limited understanding of sustainability (Council on Environmental Quality 2014). Consequently, NEPA is now perceived as a permitting tool rather than an engineering document, which has caused public participation to be associated with Not in My Backyard (NIMBY) attitudes and resulted in misaligned sustainability outcomes for transit projects (Kates et al. 2005; Real Estate Record and Builders’ Guide 1868–1884). Urban planners and civil engineers both play critical roles in evaluating transit project options. While urban planners focus on the socioeconomic impacts, civil engineers concentrate on the technical and operational aspects. However, the failure of civil engineers to effectively communicate technical issues to stakeholders has led to the NEPA being regarded as a bureaucratic process, which has caused transit project costs to soar (Petroski 2011).
The inability of civil engineering to adapt to the evolving landscape of public administration and the growing influence of policy documents on engineering practice may have weakened its standing. Furthermore, practitioners are unable to effectively shape their academic foundations, unlike their counterparts in medicine and law (Petroski 2011). This has led to disjointed and ad hoc academic solutions, particularly in graduate programs, which fail to provide civil engineering students with the necessary knowledge and skills to understand a project’s life cycle in the context of project management as well as policy knowledge for funding and executing transit projects on time and within budget (Perry and Rainey 1988).
In an attempt to address these shortcomings, construction management has been introduced as a hybrid program between business and engineering schools (Petroski 2011). Unfortunately, these programs often lack an engineering-focused approach to expanding undergraduate curricula to develop students’ understanding of public policies, project management, and professional practice. Instead, they primarily emphasize the memorization of terminology and the use of ad hoc computational software (Katz 2012). In professional practice, civil engineers have struggled to adapt to changing demands, including new technologies and increased regulations. The profession must reassess its approach and incorporate the necessary skills and knowledge to effectively contribute to the construction of sustainable infrastructure projects (Petroski 2011). The lack of engineering interpretation of public regulations such as the NEPA and the FTA’s Capital Investment Grants Program requirements has significantly affected the planning and execution of infrastructure projects (Council on Environmental Quality 2014). The ASCE has acknowledged the issue of practical experience within the profession, but it has struggled to intervene in graduate education (ASCE 2008). Moreover, the lack of input by civil engineers in interpreting the NEPA has led to major planning decisions being made by planners and economists with a limited understanding of sustainability (Council on Environmental Quality 2014).
To tackle the challenges faced by civil engineers, a holistic approach to project delivery is needed that combines technical expertise, policy understanding, and quality management practices. Although the Accreditation Board for Engineering and Technology (ABET) ensures the quality of undergraduate education, graduate programs often lack these three essential components. ABET was established in 1932 as the Engineers’ Council for Professional Development and was initially focused on the technical content taught in courses. In 1997, ABET shifted its emphasis to learning outcomes with the introduction of Engineering Criteria 2000 (EC2000) (ABET 2021). This transition led to a more flexible approach, which is a key quality needed for professionals to succeed in fields of vital importance to society (Kates et al. 2005). By investing in academic knowledge and prioritizing technical expertise over business-oriented practices, civil engineering can restore its value and effectively contribute to the construction of sustainable infrastructure projects in the future (Petroski 2011). Civil engineers must also play an active role in interpreting and complying with important policy documents such as the NEPA to ensure that the intended sustainability outcomes are achieved (Council on Environmental Quality 2014). By doing so, civil engineers can avoid the mistakes of the past and build a better future for our planet. The need for a more comprehensive approach to project delivery and a greater understanding of public policy is essential for the continued growth and success of civil engineering.
==Second Avenue Subway Project==
===Historical Context and the Need for Best Practices===

In this study, we have employed a research methodology centered on the SAS project. We used publicly available data from three main sources. First, we conducted an in-depth analysis of engineering reports and documentation from the SAS project found in the FTA’s oversight monitoring reports, published by its project management oversight contractor (PMOC reports 2013). Second, we examined the MTA’s publicly available quarterly comprehensive reports to stakeholders. Lastly, we reviewed relevant reports and literature on NYC subway construction during 1880–1920. These reports contained Rapid Transit Commission reports and best practices in the transportation infrastructure domain.
The data-analysis component of our methodology included qualitative and quantitative methods. Initially, we reviewed the engineering reports and the PMOC and MTA reports on the SAS project. Furthermore, this examination facilitated the extraction of pertinent data and identification of key themes and patterns concerning project performance, challenges encountered, and lessons learned. We have utilized our professional experience and expertise in the field to offer context and nuance to data analysis. This has allowed for a comprehensive understanding of the project’s complexities and highlighted the significant aspects that may otherwise have been overlooked. Moreover, we have compared the SAS project’s performance, challenges, and outcomes with those documented in the literature on the NYC subway project of 1900. This comparative analysis was aimed to pinpoint any unique aspects of the SAS project and evaluate its overall success in relation to the Rapid Transit Commission’s NYC subway in the early 20th century.
We devised evaluation criteria based on findings from the literature review to estimate the project’s performance. Furthermore, we considered the specific goals and objectives proposed in the SAS draft environmental impact statement (EIS) and final EIS. We employed these criteria to evaluate various dimensions of the SAS project, such as cost, schedule, quality, and stakeholder satisfaction (PMOC reports 2013).
The construction of the NYC subway system in the early 20th century exemplifies the technical and financial expertise required of civil engineers. The system cost approximately 350 million USD to construct in 1920 (New York Times 1920), which is equivalent to 4.48 billion USD in 2018. This is similar to the construction cost of the Panama Canal, which was approximately 375 million USD at the time, and it exceeded the cost of similar systems in all other great cities in the world combined. However, the NYC subway system has since become a vital part of the city’s transit infrastructure and has contributed to its economic growth and development. It serves as a testament to the importance of civil engineering in society (Kirkwood 2012). Although the term “sustainability” was not explicitly used during the planning and design of the NYC subway system, all three pillars of sustainability (i.e., economic viability, environmental protection, and social equity) were integral to its construction (Walker 1917). The importance of effective technical communication can be seen in the annual reports of the Rapid Transit Commission. Notably, the challenges faced by the commission at the time were not that different from those faced in the 21st century, such as the population density of Manhattan, subway routes, funding issues, concerns of the public, and tunnel depth (Hood 2004). Documents from the early 20th century (Gotshall 1903, Walker 1917) describe the cost and economic viability of the NYC subway system considering construction challenges and an 1896 court order while also highlighting the social, environmental, and economic impacts.
For new transit systems, the ASCE assesses how well they align with current engineering best practices and requirements. The ASCE Report Card offers insights into the necessary expenditures to maintain transit systems, but its primary objective is to ensure compliance with contemporary standards (Glaeser and Poterba 2020). This assessment can be influenced by subjective factors such as the performance of the responsible organization. It is crucial to understand that a poor evaluation does not always imply a poorly functioning subway system because the ASCE does not distinguish between management and engineering. In other words, a system could be well-engineered but poorly managed. Most subway systems were built in the 1940s, and since then agencies originally responsible for their construction have transitioned to their operation. Thus, such agencies have lacked involvement in engineering projects to build new subways for nearly six decades.
In the existing legal framework, project management contracts are neither considered professional service contracts nor backed by surety, and the ASCE has not yet formulated best practices for project management similar to those developed for design and temporary construction, which have clear guidelines and codes to which the design team must adhere. In the absence of clear guidelines, project management teams may not be held accountable for any issues, which can potentially jeopardize their reputation and credibility. To address this lack of knowledge, the ASCE can publish best practices for transit project management, design, and construction with a focus on consistent engineering principles and legal clarity. The ASCE should establish best practices for an acceptable level of performance concerning project management services. These can include constructability review documents, project budgets, contingency plans, and schedules on par with the approximately 70 design standards currently maintained by the organization.

===Design and Construction Decisions===
The SAS project has garnered attention due to its design, construction challenges, and decisions that led to massive cost overruns. The absence of best practices for project management contributed to the project’s setbacks.
The SAS project faced numerous design challenges, including building a new subway line in a densely populated urban area and accounting for varying geological conditions along the route. Table 1 presents the depths of stations for the SAS project. The draft environmental impact statement (DEIS) for the SAS did not discuss the three options of shallow, mid-depth, and deep tunnels to determine the most feasible path forward. The number of ventilation shafts and access shafts required is related to the depth of the tunnel. The Rapid Transit Subway Construction report provided guidelines for balancing the cost, soil type, construction method, and location to determine the optimal depth for subway tunnels (Associated Engineers-A Joint Venture 1975, New York Urban Transportation Group 1972).
During the preliminary engineering phase between 2000 and 2004, the project management team created budget and contingency plans before entering the final design phase. Despite these efforts, the budget at the Record of Decision had grown to 3.68 billion USD, which was an increase of 920 million USD over the original projection from the 1999 DEIS. By the time the SAS project secured federal government funds, the budget had reached 3.90 billion USD, and it eventually increased to 4.45 billion USD by the time of completion. The ongoing criticism of the cost overruns and delays has resulted in scrutiny of the project management and decision-making processes. The publication of the Final Environmental Impact Study (FEIS) in 2004 elaborated solely on different means of constructing a transit system on Second Avenue, which is primarily a planning practice. This highlights the need for a more detailed study of engineering alternatives and a stronger emphasis on establishing best practices in project management to prevent cost overruns and delays in future projects.
===Geotechnical Design and Construction Issues===
===Tunnel-Boring Machine===
The project management team faced several geotechnical design and construction issues. One of the challenges was related to the tunnel-boring machine (TBM). The team initially justified the use of a TBM because of its ability to dig underground without causing surface disruptions. However, they did not properly plan for the removal of soil excavated by the TBM, which led to the contractor having to install large muck removal and caused obstructions and disruptions at the street level. Furthermore, the cost of using the TBM was triple the amount needed for the more traditional method of cut and cover, which raised questions about the effectiveness of constructability review documents and project management practices (Urban Engineers 2012).

Table 1. SAS Station Depth
Station Location Type Transfer Routes Preliminary Entrance Locations Approximate Station Depth
SECOND AVENUE LINE
96th St Second Ave/96th to south of 94th St 2 track Second Ave/southwest corner of 96th St and northeast and southwest corners of 94th St 40–45 ft
86th St Second Ave/87th to south of 82nd St 2 track Second Ave/northeast and southeast corners of 86th St and eastern side of Second Ave between 83rd and 84th Sts 85 ft
72nd St Second Ave/72nd to 69th St 3 track Bway/Second Ave Lines Second Ave/northeast and southwest corners of 72nd St; and northeast corner of 69th St 85 ft
63RD STREET LINE
Lexington Ave 63rd Street/Lexington to Third Ave 4 track F Existing: Lexington Ave/63rd St; New: Third Ave/63rd St (northwest, southeast, and possible northeast corners) 105–135 ft

Notes:
1. Average depth of Manhattan tunnels constructed in 1900 was 30 feet.
2. 72nd Street station became two tracks.
3. All four tracks at Lexington Avenue (63rd Street station) exist today; only two are used for passenger services.

===Ground Support Challenges===
The 72nd and 85th Street stations faced constructability challenges related to ground support during the detailed design stage. Engineers discovered that the quality and depth of rock above the stations were not adequate to support the tunnel (Fulcher et al. 2013, Urban Engineers 2008). The original design of the 72nd Street station included three tracks, but it was at such a depth that the design team was forced to change the configuration to the current two tracks. This highlights the need for conducting thorough geotechnical studies and developing engineering alternatives rather than relying solely on a shallow FEIS analysis. This can prevent costly redesigns and reconstructions during the excavation process and ensure that the design aligns with the project’s goals.

===Utility Work and Contracting Challenges===
In the preliminary design phase, the project and design team initially planned to use six construction contracts, but they later encountered problems with utility work and the assumption that general contractors would handle all permits, engineering, and logistics. This resulted in the team having to break the contract into separate utility and station contracts as had been recommended by an engineering report on the construction of the original subway system in 1919 (NYC Engineering Report on Subway Construction). During construction, the project management team discovered that the buried foundations of elevated tracks on Second Avenue that had been demolished in the mid-1940s still existed, which presented a significant cleaning task. This was yet another instance where the project deviated from the best engineering practices in terms of a thorough geotechnical investigation prior to detailed design and risk management (Urban Engineers 2010).
Real Estate Acquisition Challenges
The design and project management team assumed that the acquisition of real estate for shaftways would be straightforward. However, accessing the ground beneath the surface was not without issue (Real Estate Record and Builders' Guide 1904). The team assumed that only one building would be necessary for real estate negotiations. However, in reality, access was needed through multiple interconnected buildings when the design team started the detailed design. Manhattan block buildings leaned on each other, and there was no gap between them (Eschenasy et al. 2017). Thus, an individual building provided support to the entire block. The design team had to construct a frame that supported the entire block to pull it out in one piece (Urban Engineers 2008).
In cases where the access shaft was going into a building, the SAS project needed an easement by law. However, the project needed to enter multiple buildings because the buildings were interconnected. Manhattan’s structures were designed so that someone living two or three buildings away from the access building had to walk through a hallway in the second or third building before arriving at the original access building. A deeded easement was required for this setup (Urban Engineers 2008).

A comprehensive approach to civil engineering best practices in project management is essential for understanding the intricacies of large-scale transit projects such as the SAS. Relying solely on individual perspectives and interpretations is insufficient for a profession that has built subway systems since the late 19th century. Successful projects necessitate a systemic understanding of project management, which includes thorough geotechnical studies, effective technical communication between stakeholders, and a detailed analysis of real estate acquisition and utility work. In addition, it is crucial to emphasize the importance of translating these efforts into the education of graduate students. They should be taught to understand construction deliverables, project delivery methods, and construction packaging in a practical manner, akin to the hands-on approach expected of law or medical students. This goes beyond mere memorization and equips future professionals with the skills necessary to navigate complex projects effectively. By learning from the experience of the SAS project, future project management teams can address challenges related to tunnel-boring machines, ground support, and utility work while also navigating the complexities of real estate acquisition and easement. Implementing these lessons will help minimize delays and cost overruns, which will ultimately lead to more efficient and cost-effective infrastructure development.
Moreover, it is vital for project management teams to remain adaptable and open to changes in their initial plans. This flexibility will enable them to respond effectively to unexpected challenges that may arise during the design and construction phases. It is equally important to invest time and resources in conducting comprehensive analyses, including geotechnical investigations and engineering alternative analyses, to ensure that projects are built on a solid foundation.

Table 2. SAS Budget History
Categories 1999 DEIS (Phase I & II) 1999 DEIS (Adjusted for Phase I) FEIS (2004 Record of Decision) FFGA (November 2007) Actual (2018)
Total Design $410.00M $410.00M
Construction Management Services $86.00M $80.94M
Construction $4.82B $2.17B $2.81B $2.69B $2.78B
Contract 1: Tunneling $375.88M
Contract 2–6 & Others $2,430.12M
Rolling Stock * $610.10M $610.10M $157.00M $153.00M $0.00M
Real Estate $131.40M $59.13M $191.00M $240.96M $281.50M
OCIP $180.00M $160.00M
Project Reserve $8.00M $173.10M
NYCT Labor, Utility Reimbursement, etc. $145.30M
Subtotal Without Financing $5.56B $2.76B $3.68B $3.90B $4.45B
Financing Cost $796.31M
Total $4.85B
Notes:
1. All estimates are for the Year of Expenditure (2018).
2. SAS project Phase I never paid for rolling stocks; therefore, the estimate is crossed out.

==Metropolitan Transportation Authority==
Public authorities acquired many of their transit systems from private companies that were in financial trouble after World War II. Because of the decline in profits and ridership, transit companies spent less money on maintenance and renovation, which resulted in their systems largely falling into disrepair by the time they were acquired by public authorities. The new transit authorities have had limited success in addressing the problem of station neglect, with a lack of funds being a major challenge. The NYC Transit Authority was established in 1953 (Homburger and Kennedy 1961), and the Metropolitan Transportation Authority (MTA) was created later in 1968 (New York Urban Transportation Group 1972). Since then, the MTA has consistently struggled with budget deficits and has been unable to establish a pricing equilibrium. It is not considered financially sustainable because of its reliance on government subsidies to balance its budget. It also lacks transparency in its financial system, and it is not held to the same standards of competency as private institutions. However, as a public institution, its value in terms of economic growth and employment through infrastructure investment must be recognized (Glaeser and Poterba 2020).
To operate sustainably and be responsive to taxpayers, the MTA needs to establish an equilibrium in funding to cover operating, maintenance, and capital improvement costs while recognizing that it may not conform to a normal pricing equilibrium (Mankiw 2018). It is crucial for project stakeholders to have confidence in the MTA’s management, whether practices are internal or external (Mayor's Committee on Management Survey 1953). The MTA has two primary roles in managing its transit projects: planning and economics, and engineering. To be competent in these practices, the MTA staff must possess the necessary knowledge and skills. Several reasons justify subsidizing public mass transit systems with taxes. First, taxation is the only practical way to collect from people who indirectly benefit from the transit system. Without taxes, the community would receive a benefit without contributing in return. Second, fares paid by riders do not fully cover the operating costs, depreciation, and capital asset investments. Therefore, it is necessary to collect part of the cost from both riders and the community through taxes (Litman 2021). Opponents may argue that fares should be high enough to cover the total costs of the transit system to eliminate the need for government subsidies (Coase 1960). This approach would discourage some people from using the system’s services and result in fewer riders. Subsidies help the MTA maintain affordable fares and allow the NYC transit system to operate at its intended capacity, as it has for over 100 years (Habib et al. 1978).
Notably, NYC’s transit system has always been subsidized, dating back to the first streets and docks built at public expense in 1684 and city-operated ferries crossing the East River. Even private investors were not allowed to increase fares to the level of pricing equilibrium. Transit facilities are generally more economical when heavily used, as reduced traffic leads to wasted capacity (Parry and Small 2009).

To achieve a funding equilibrium, the MTA must make an effort to itemize its costs and develop a plan to control them effectively. The complexity of its budget can be understood by examining three main categories: operating costs, maintenance costs, and capital investments in expanding the system (Engineering-Contracting 1910). Therefore, the MTA is responsible for managing taxpayer money in a way that balances its budget while also investing in capital growth to deliver sustainable transit projects. However, the MTA has struggled with effectively managing construction projects due to a lack of advancement in civil engineering practices and knowledge.

Theoretically, the MTA maintains a high level of accountability, but this is not consistently evident in its organizational practices (Hood 1992). As a result, while a design team may generate technical documents, the project management team of a consulting firm will not review them because it falls outside the scope of an engineering contract. In essence, the project management team satisfies the MTA’s staffing needs without requiring members to hold necessary engineering qualifications, understand engineering practices, or carry engineering liability insurance. Despite this, the MTA is the highest authority in the decision-making process and integrates the three variables of scope, cost, and schedule for capital projects. Most of the members of a project management team will not possess the necessary credentials to review engineering practices or deliverables because being a licensed engineer is not a key requirement. Rather, a certification in management is needed. This is a significant departure from reports from the early 20th century (Building the New Rapid Transit System of NYC 1915), which emphasized that the design and construction of the NYC subway system was primarily an engineering task with military-like organizational principles.
It is vital for the MTA to address this gap and prioritize engineering knowledge and practices in its project management. Currently, the project management team may move in one direction, the design team produce construction documents in another direction, and the construction team move in yet another direction independent of the other two. This lack of coordination can be seen in the SAS project, where the geotechnical design team produced design memos for the tunnels and stations that were signed off by the project management team and construction team. However, the construction documents did not consider the shallow depth of rock on top of the tunnels or the width of the tunnels in their original three-track design, which caused problems in the construction of the stations. The schedule and budget approved by the project management team confirmed that it reviewed the design, but no changes were made, which is likely because the FTA would not pay for a project redesign.

To overcome these challenges and improve overall performance, the MTA must make significant changes in its organizational structure and management practices. One critical step is to require project management staff to possess engineering credentials and knowledge. By doing so, the MTA can better align its various teams and ensure that they are working collaboratively toward a common goal. Furthermore, the MTA should invest in the professional development of its staff by providing ongoing training and resources related to civil engineering practices. This will help it keep up with advancements in the field and ensure that its projects are executed using the most up-to-date techniques and methods.

Another essential component of improving management practices is to increase transparency and accountability within the MTA. By implementing performance metrics and regularly tracking progress, it can ensure that it is effectively managing taxpayer money and delivering results in line with its objectives. Increased transparency can help build public trust and demonstrate that the MTA is committed to providing quality service while responsibly managing its resources. Finally, the MTA should focus on forging stronger partnerships with external stakeholders such as local governments, businesses, and community organizations.
Conclusion

The early 20th century witnessed the evolution of civil engineering into an established profession with a comprehensive knowledge base. Civil engineers contributed to the construction of massive projects such as the NYC subway system, which required technical and economic expertise. However, civil engineering faces vulnerabilities because of the absence of a well-rounded education system that can prepare engineering students for the complexities of future projects and the disconnect between academia and engineering practices. As civil engineering knowledge continues to develop, the profession must address these vulnerabilities and adapt to the changing demands of the industry.

The SAS project faced multiple design challenges and construction decisions that resulted in considerable cost overruns. The assumptions made by the project management and design teams regarding geotechnical issues, construction contract packaging, real estate acquisition, and easement were unable to handle the complexity of the SAS project. This resulted in unexpected costs and delays, as evidenced by the significant budget increases. To avoid similar issues, it is crucial to examine the logic and lessons learned from the project and apply these insights to future endeavors. The lack of well-defined best practices for project management played a role in these issues. To avoid similar problems in future transit projects, the ASCE should establish comprehensive best practices for project management, design, and construction encompassing consistent engineering principles, legal clarity, and acceptable performance levels for interpretation under the NEPA. By adopting these best practices, transit agencies and engineering firms can collaborate more effectively to complete crucial infrastructure projects on schedule and within budget. The SAS project serves as a case study for the importance of thorough planning, effective communication, and adaptability in the face of complex challenges. The lessons learned from this project can be applied to the better planning, design, and execution of future infrastructure projects, which will ultimately benefit both the industry and the public.
The MTA is a vital public institution responsible for providing essential transit services to NYC citizens. However, it faces significant challenges related to financial sustainability and management practices. By prioritizing engineering knowledge and practices, increasing transparency and accountability, and fostering stronger partnerships with external stakeholders, the MTA can transform itself into a more efficient, cost-effective, and responsive organization. These changes will ultimately lead to better transit services for the public and a more sustainable future. As the MTA evolves and adapts to these new management practices, it can serve as a model for other public institutions looking to improve their own operations and better serve their constituents.

==Data Availability Statement==

All data used in this study is publicly accessible and can be obtained through the provided references. For the PMOC reports, readers can access the FTA’s website at https://www.transit.dot.gov/foia/metropolitan-transportation-authority-second-avenue-subway. The 2013 “The Politics of Large Infrastructure Investment Decision-Making: The Case of the Second Avenue Subway Case Study” report can be found at https://rosap.ntl.bts.gov/view/dot/27119.

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If you would like to contribute to this Wiki by adding new pages or adding to existing pages, you may do so by becoming a registered user: [mailto:riskengineer20895@gmail.com email Administrator].
==Why is Civil Infrastructure important?==
:<big>'''''Infrastructure is a platform for governance, commerce, and economic growth and is a lifeline for modern societies'''''</big>. <ref name="Esmaeili2016">Esmaeili, Behzad, et al. "Inclusion of an Introduction to Infrastructure Course in a Civil and Environmental Engineering Curriculum." Journal of Professional Issues in Engineering Education and Practice (2016): 04016020</ref>
:<big>'''''It is the platform upon which the world's economies function and our prosperity depends.'''''</big> <ref name="USACE2014">Hart, Steven D., et al. "Infrastructure and the Operational Art:A Handbook for Understanding, Visualizing, and Describing Infrastructure Systems" US Army Engineer Research and Development Center, (2014)</ref>
:'''''<big>By virtue of the way we live modern life, almost everyone in societies ... is familiar with infrastructure and relies on its benefits. We turn on lights. We flush toilets. We eat food that we did not grow.</big>'''''<ref name="USACE2014" />
Infrastructure has been characterized as the mechanism that delivers the "..fundamental needs of society: food, water, energy, shelter, governance ... without infrastructure, societies disintegrate and people die." <ref name="USACE2014" /> As a society, Americans enjoy the use of "...highway, waterway, air, and rail systems that have allowed the unparalleled mobility of people and goods. Water-borne diseases are virtually nonexistent because of water and wastewater treatment, distribution, and collection systems. In addition, telecommunications and power systems have enabled our economic growth."
<ref name="ASCEinfrastructure">ASCE Critical Infrastructure Guidance Task Committee. "Guiding principles for the nation's critical infrastructure." American Society of Civil Engineers,2009.</ref> 
This development happened over a period of several centuries and represents a number of successes and failures in what is currently termed infrastructure
<ref>Hart, Steven D., J. Ledlie Klosky, and Scott Katalenich. "Conceptual models for infrastructure leadership." Journal of Management in Engineering 30.3 (2013): 04014003.</ref>
but also in the past was termed "public works" and even before that "internal improvements". 
Indeed in the United States, we have a long history of federal support for building great infrastructures such as the National Road, Erie Canal, transcontinental railways, inland navigation coupled with flood mitigation and control, the Panama Canal, and the interstate highway system and efforts in improving seismic design and construction over the last century. <ref name="USACE2014" /> This has also stimulated discussion among world leaders on the ...
:"''broad and urgent issues of energy, infrastructure (particularly for electricity and transportation), and climate change...(that) have become inextricably linked as societies around the world discuss, disagree, debate, and make decisions about properly balancing the production and use of energy against the quality of life and economic opportunity.'' <ref name="USACE2014" />
:'''Therefore, for any society, even the American society, its economy is "... inextricably linked with the infrastructure that supports it...(and) the social, political, and economic structure of a society can magnify or mitigate the effects of a failure in infrastructure and vice versa."''' <ref name="USACE2014" />

==Civil infrastructure and the rise of projects==
Civil infrastructure, like canals, railroads, bridges, etc., quickly became singular and substantial ventures for their sponsors, first cities and then nations. Not all succeeded, as the state bankruptcies in the 19th Canal building in the young United States demonstrate. Even at the height of the industrial revolution and the rise of modern society, less than a dozen nation-states built large-scale infrastructure. Even in the 21st century, infrastructure is still viewed as a hallmark of advanced economic development. Crucially, the unique character of these undertakings marks an "...explicit and deliberate disconnection from history and future" to create the exceptional circumstances that facilitate the accelerated delivery of this infrastructure given its heavy cost and long schedules.<ref name="Grabher2015">Grabher, Gernot, and Joachim Thiel. "Projects, people, professions: Trajectories of learning through a mega-event (the London 2012 case)." Geoforum 65 (2015): 328-337.</ref> 
This investment also required organizations to manage the "unprecedented scale and complexity of the challenges with regard to capital investments, public attention and stakeholder participation". <ref name="Grabher2015"/> Large organizations must be built, and specialized labor must be recruited and mobilized during this infrastructure effort. The sponsors must secure funding and negotiate with multiple stakeholders while contending with the daily direction of the endeavor. <ref name="Grabher2015"/> Once the endeavor has been accomplished, the organization is dissolved in what has been termed "institutionalized termination"<ref>R.A. Lundin, A. Söderholm, A theory of the temporary organization, Scand. J. Manage., 11 (4) (1995), pp. 437–455</ref>. <ref name="Grabher2015"/> These efforts to accomplish the national ambitions for infrastructure gave rise to the modern vision of projects and were a driving social factor in at least two ways: creating an environment that fostered first the rise of the civil infrastructure occupation and then the engineering professions as well as the related industry and professional communities. As such, the notion of a project allows a society to organize and manage its infrastructure endeavor as a distinct set of given tasks, separate from the “continuity of past/present/future” (Hernes et al., 2013) as well as an artificial constraint on its institutional life such as as a count down clock (Lundin et al., 2001)."<ref name="Grabher2015"/> 
:'''The constant pressure to deliver infrastructure effectively and efficiently has forced the development and evolution of the project as the primary vehicle for successfully delivering civil infrastructure.'''

==The Work of civil infrastructure and the rise of occupations==
'''''A project needs to be designed by and around people, and its success is dependent upon the will, commitment, and ability of those individuals for their creation, development, and termination.''''' <ref>Lundin, Rolf A., and Anders Söderholm. "A theory of the temporary organization." Scandinavian Journal of management 11.4 (1995): 437-455</ref>
Project organizations mobilize large numbers of people with a variety of education and experience in order to focus their energy in the form of work towards a common set of goals or objectives. A key factor in the success of these endeavors is the ability or capacity of the project to control the definition of the work tasks and the allocation of labor, more specifically, specific groups of labor to certain tasks. For civil infrastructure, this allocation is a continuum of labor "...differentiated by the [[Wikipedia:Locus_of_control|locus of control]] over the division of labor" <ref name="Lewis1971">Lewis, Bronwen A., Browen A. Lewis, and Charles K. Warriner. "The job-profession continuum." Kansas Journal of Sociology (1971): 34-44.</ref> by the project organization as a way of determining of who does what, managed by whom. This labor allocation is based upon the life experiences at any given time of the mobilized labor forces, stratified into cohorts and their subsets and constituting the basis for labor occupations. <ref name="Abbott2005"> Abbott, Andrew, Chapter Title: Sociology of Work and Occupations, Chapter Author(s): Andrew Abbott, Book Title:Smelser, Neil J., and Richard Swedberg, eds. The handbook of economic sociology. Princeton University Press, 2005.</ref> In this sense, when someone assigns a person an "occupation", it locates them in a category of workers accomplishing tasks within a division of labor. <ref name="Abbott2005"/> Abbott argues further that occupations have several other realities; namely, they are identifiable and stable groups of workers (as opposed to simple categories of transient work) with sustained participation, which may occasionally rise to the level of being formally recognized such as the crafts or professions.<ref name="Abbott2005"/> 
Project organizations manage the accomplishment of civil infrastructure within this space of project work and available labor by means of management and allocation of that mobilized labor. The fundamental locus of this process is the product of project work assigned to a specific labor group or occupation. Within this framework, one can define the term "job" as allocated work for which no control is exerted over the allocation. In this sense, an occupation is determined by the project organization, where there is consensus over the tasks to be allocated to this portion of the labor force. Classical thinking in sociology has taught that occupations were different than professions or crafts in that the consensus had not been developed through formalized training or regulatory authority. <ref name="Lewis1971"/>
:'''The constant pressure to deliver infrastructure effectively and efficiently has forced the development and evolution of the project organization's capacity and capability to control the definition of work and the allocation of labor.'''
:: In the case of the latter, it is important to allocate work to identifiable and stable groups of workers (as opposed to simple categories of transient work) with sustained participation, resulting in specialized experience and learning.
: Over time and with the emergence of technology, this allocation of labor would give rise to certain occupations achieving the level of being formally recognized, namely the crafts and professions.
==Civil Infrastructure and the rise of the engineering professions==
'''''The modern professions developed during the early eras of capitalism in Europe, but they were distinctive in societies increasingly dominated by the logic and spirit of the market... caveat emptor (“let the buyer beware”) versus credat emptor (“let the taker believe in us''”).'''<ref>Harper, D. (2007). Work and occupations. In C. D. Bryant, & D. L. Peck, 21st century sociology: a reference handbook. Thousand Oaks, CA: Sage Publications. </ref> 
'''''The aspirations of the nation for its infrastructure are often expressed ... (by) professional organizations.''''' <ref name="USACE2014" /> 
National ambitions for infrastructure are not always expressed or executed successfully. The early canal period in North America serves to make the point. The then colonies of Virginia and Pennsylvania conducted surveys to determine route locations and feasibility for water canals using existing waterways to connect the eastern Atlantic seaboard with the newly formed Northwestern territories, mainly the Ohio Valley. Europe went through this period from the 16th to early 18th century until the development of the "engineered" canal that connected two points, whether there was a waterway. Examples were the mid-18th century "Sankey" and "Bridgewater" canals. In the American case, the Pennsylvania Canal scheme of 1790 was unsuccessful and would not be revived until the completion of the Erie Canal in 1825. 
Due to the Industrial Revolution and the Enlightenment and the ambitions for accomplishing civil infrastructure, first projects evolved, and then specialized labor or occupations developed. As the complexity grew, so also did the related uncertainties associated with civil infrastructure, thus there was a social need for certainty. At first, it was scientific in nature, but then, the new concept of technology emerged. Professions and the newly formalized one known as engineering provide a special function in our society and economy where investing substantial sums of funding in public infrastructure requires "...some assurance that it will perform its intended function." This is not an absolute guarantee, but it is the engineer exercising their professional judgment that is "the practical certainty" that makes social investment in public infrastructure possible and makes the world "..a less uncertain place than it might seem..." 
The rise of modern society also required the "...unification of societies" and the development of national societies and economies. It was the compelling economic logic of this trend that provided the rationalization for the rise of the professions and their vision of occupational imperialism that closed markets. This has evolved in the late 20th and 21st centuries to become national frameworks of public and private civil infrastructure, sustained by public taxation and user fees, requiring "...a reasonably consistent means of interpreting and applying it..." thus the rise of the professions and particularly that of engineering, especially, civil engineering. 
The increasingly urgent challenge for the professions is that more and more of this 21st-century infrastructure possesses an inherent complexity and pace that may, at times, threaten to exceed the profession's ability to manage them to social expectations. The very essence of social acceptance and perceived legitimacy of the profession is the confidence that society places in it.
:"'''A large-scale society demands comparable institutions to sustain and regulate it. These are not all to be found in the state but the modern nation-state could not have developed without the co-evolution of professions such as civil engineering.'''"
==The Civil Engineering context==
:''The practice of the '''civil engineering discipline''' by '''civil engineering professionals''' under the overall direction of a professional society such as the American Society of Civil Engineers (ASCE) acting as the organized "'''civil engineering profession'''", '''impacts the health and vitality of a nation as no other profession does.''' The National Academy of Sciences has noted that the country's business competitiveness, military strength, health, and standard of living are intimately connected to engineering.''(Emphasis added) 
As noted above, modern societies require modern and complex civil infrastructure successfully delivered by project organizations that control work development and allocation of labor, especially specialized labor. The complexity of these infrastructure projects will require the use of new management forms such as civil infrastructure programs to align multiple projects on major objectives, transfer knowledge between projects, adapt strategies to subtle shifts in program layer goals/objectives, mediate conflicts between program layer sponsors and project layer stakeholders; deliver more specialized forms of management control, namely oversight. 
A significant portion of the latter of that assigned work is work allocated to engineering professionals, in this case, civil engineers. This will force changes in existing social forms and practices, continue to give rise to new labor allocations and recognize new civil infrastructure occupations. These changes and economic pressures will also challenge existing highly credentialed and regulated occupations, namely the engineering professions and civil engineering in particular.
:'''To generate value and social acceptance, the '''civil engineering profession''' must be able to identify, create, and continuously manage occupational and professional knowledge, especially technical knowledge, as knowledge may be the most strategically significant resource any profession can possess.'''
This will require the disciplined development and integrated application of empirical and theoretical knowledge, effective meta-knowledge, and meta-data in a manner that executes the project, manages risk, adds value, and meets or exceeds stakeholder expectations, which is the purpose of the '''civil engineering discipline'''. 

''See also [[Bridging the Gaps: Better Infrastructure Project Management and Civil Engineering]].''

The '''civil engineering profession''' intends to deliver products, services, and projects in a manner that meets or exceeds stakeholder expectations, thereby earning social trust and gaining recognition as environmental stewards. The primary vehicle for fulfilling this intent is thru projects. Therefore, the civil engineering profession will continue to focus on defining, selecting, and implementing projects geared toward improving our natural and built environment, with sustainability being a key consideration and opportunity. 
The role played by the '''civil engineering profession''' in the nation's well-being requires the application of '''civil engineering discipline''' knowledge to be practiced by highly credentialed and sometimes licensed or certificated '''civil engineering professionals''', paraprofessionals and other civil infrastructure occupationals who are knowledgeable and skilled at continuously maintaining the integrity and adequacy of both the design and project life cycle strategy in conformance with relevant professional responsibility and ethical standards.
This role played by the '''civil engineering profession''' in the nation's well-being also requires '''civil engineering professionals''' to increasingly practice in conformance with established/codified sets of practices in the form of [[Bodies of knowledge]], [[Regulatory agency|regulatory requirements]], [[Program Management|programmatic requirements]] and [[Agency Guidance Practices for Civil Engineering|guidance practices]]. They will also operate within a practice framework of an increasingly specialized array of [[management plans and sub-plans]] supported by [[analytical and modeling deliverables]].

<!--There will also be legislatively imposed practice codes as well as licensure requirements for [[Wikipedia:Project_management|Professional Engineering project managers]], [[Wikipedia:Project_management#Project_controlling_and_project_control_systems|project controls]] and [[Risk Management|risk management]]. Each of the latter will be recognized as a specialized civil engineering application, meriting its recognition as a professional engineer.
Such a process does not exist for project managers of [[Wikipedia:Public_infrastructure|public sector]] [[Wikipedia:Infrastructure#Engineering|infrastructure]]. However, professional recommendations are starting to emerge from professional organizations that are [[Professional Practice Extensions|extensible]], i.e., capable of being extended to support policy goals espoused by public agencies for better project management performance and project cost and schedule risk management.

This future vision of the '''civil engineering profession''' and '''discipline''' can only be accomplished by '''civil engineering professionals''' practicing within a framework of [[Performance Management Systems (PMSs)|performance management systems (PMSs)]] that are [[Management control|controlled]] throughout the life of the project. This [[Management process|process approach]] to project delivery, in turn, is only practical in an environment where processes are defined, [[Project Documentation|documented]] and [[Project Monitoring|monitored]]. This vision also requires these processes to be tailored or adapted for project-specific requirements and continuously improved in a value-added way throughout the project life-cycle or duration. Lastly, it also requires characterizing and describing "uncertainties" in terms of variations in process, gaps in project data, limits to engineering knowledge and models, and lastly, [[Project stakeholder|stakeholder]] behavior that puts the project at risk.

This practice of civil engineering ("'''civil engineering practice'''") takes place within a framework of discipline quality, professional ethics and standard of care, and stakeholders' expectations and sense of value. This stakeholder expectation is that the following occur:
*Development and implementation of [[engineering tools]], [[solution sets]], etc., that represent the best trade-offs between costs and benefits to deliver the desired strategic outcomes successfully,
*Executing at all layers ([[Work Package Layer|work package]], Contract, [[Project Layer|project]] and [[Program Layer|program]]) to meet [[Project Objective|objectives]] and fulfill [[Requirement|requirement]]s for the [[Engineering complexity|complex]] project in a [[Project predictability|predictable]] and [[Project reliability|reliable]] manner that meets or exceeds [[Acceptance|acceptance criteria]].

Only a formal, process-based approach to discipline quality in project delivery by the "civil engineering profession" will deliver this result!

=Portals=
==[[Academic Portal]]==
Contains program materials that offer advanced knowledge of an engineering specialty, technical management skills, and understanding of the design process and its relationship to project lifecycle and delivery methods that could be combined with core business, financial, and management subjects typically found in an M.B.A. program or as part of a [http://www.mempc.org/degree/ Master of Engineering Management (MEM) Degree] or other specialized educational programs.
*Existing Course offerings are described. In some cases, Riskwiki is being used as an academic resource. We would love to support your program or course!...
*''' Please contact us to add your school and courses!''' [mailto:riskengineer20895@gmail.com email Academic Coordinator].

==[[Basic Concepts Portal]]==
''(Under Construction at the moment, nothing in this section)''
==[[Professional Concepts Portal]]==
Contains materials on the profession of civil engineering, project management, and other subjects, including a detailed analysis of the outcomes with commentaries and recommendations. It offers an overview of ASCE's current outcome structure as well as an alternative set that supports the Masters in Engineering Management (MEM) program.
==[[Practice Concepts Portal]]==
Contains materials on specialized civil engineering knowledge and practices in project management and other subjects such as phase models, project delivery methods, assessment, internal controls, and stakeholder oversight.
==[[Resources Portal]]==
Contains materials on engineering frameworks for project management and project risk as well as the evolution of policy and practice on project management and risk at several US government agencies, functional risk practices, and other specialized risk topics.
==[[Whitepapers Portal]]==
It contains First Principle materials on civil engineering, project management, other subjects, and professional bodies of knowledge such as the Civil Engineering Body of Knowledge (CEBoK) and Project Management Body of Knowledge (PMIBoK).

'''Again, welcome to Risk Engineering.org!'''

=Notelist=

=References=
<references />
= Footnotes =

=See also=

=Copyright Licensing Policy=
All content on this site, unless otherwise noted, is licensed under an Attribution-NonCommercial-ShareAlike 3.0 United States (CC BY-NC-SA 3.0 US). See the website's [[RiskWiki:Copyright|Copyright Licensing Policy]]

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<div style="float:right;margin-left:1em;"> __TOC__ </div>
----
= Welcome to RiskWiki!=
'''RiskWiki is an educational website being implemented as an engineering ''wiki'' with articles about various Civil Engineering (CE) topics with civil engineering [[Uncertainty_and_Risk_in_Civil_Engineering_Practice|uncertainty and risk]] as the capstone meta-knowledge element.''' The underlying engine for RiskWiki, [https://www.mediawiki.org/wiki/MediaWiki Mediawiki], is the same [https://en.wikipedia.org/wiki/Content_Engineering content engine] used by Wikipedia but with many style and customization differences made. We also have organized [[Main_Page#Portals|Portals]] to help navigate RiskWiki as well as to find information of interest to engineering professionals, academics, and students. 
While the site focuses on risk, many other civil engineering topics must be explored and discussed as foundational concepts. It will offer articles that provide analysis, commentary, and additional reading. RiskWiki is intended to serve as a broad resource to the engineering community and civil engineers. We have added a lot of new material in this area.
*'''The primary focus of RiskWiki is complex [[Civil_Engineering_Projects|civil engineering projects]] using civil engineering [[Management_process|processes]] and [[Management_control|controls]].'''
*'''A secondary focus of RiskWiki is [[Basic Concepts of Program Management|civil engineering programs]].'''
These articles will be presented in the standardized format of an encyclopedia such as Wikipedia that allows the reader to examine the material in a logical sequence with hyperlinked terms:
* First, prefatory material or basic considerations for the article are presented.
* Engineering terminology is often beset with terms used interchangeably for several major concepts and in several different contexts, so the semantics of the term are discussed and analyzed. The [[logical framework|logical]] framework is also presented and hyperlinked to allow cross-referencing on key concepts.
* Professional knowledge will next be presented in a "practice frameworks" section that builds upon the previous semantic and logical analysis. This is often in the form of a [[Body_of_Knowledge|body of knowledge]] produced by relevant professional societies such as the American Society of Civil Engineers (ASCE), Project Management Institute PMI, or International Organization for Standardization (ISO). Each practice version will have commentary, analysis, and recommendations for the professional. Significant research will also be a part of the section.
* As noted in other places, civil engineers often practice in regulatory and programmatic contexts. Regulations do not appear without legislative guidance, so the articles proceed from professional frameworks through the prism of legislation into regulatory and programmatic frameworks.
* Every knowledge concept or object has limits on its usage and therefore sections on "Further Guidance", "Beneficial Outcomes" are given.
* Lastly, working definitions of the term and practice-specific variants are given.
* Further reading for the professional is suggested, as well as planning for academic resources.
'''The site is also intended to support learning in either a formal academic or corporate training environment as well as individual study efforts and online educational materials.'''
*If you would like us to develop any specific materials for you, please feel free to address any requests on these subjects to [mailto:riskengineer20895@gmail.com email Administrator].
If you would like to contribute to this Wiki by adding new pages or adding to existing pages, you may do so by becoming a registered user: [mailto:riskengineer20895@gmail.com email Administrator].
==Why is Civil Infrastructure important?==
:<big>'''''Infrastructure is a platform for governance, commerce, and economic growth and is a lifeline for modern societies'''''</big>. <ref name="Esmaeili2016">Esmaeili, Behzad, et al. "Inclusion of an Introduction to Infrastructure Course in a Civil and Environmental Engineering Curriculum." Journal of Professional Issues in Engineering Education and Practice (2016): 04016020</ref>
:<big>'''''It is the platform upon which the world's economies function and our prosperity depends.'''''</big> <ref name="USACE2014">Hart, Steven D., et al. "Infrastructure and the Operational Art:A Handbook for Understanding, Visualizing, and Describing Infrastructure Systems" US Army Engineer Research and Development Center, (2014)</ref>
:'''''<big>By virtue of the way we live modern life, almost everyone in societies ... is familiar with infrastructure and relies on its benefits. We turn on lights. We flush toilets. We eat food that we did not grow.</big>'''''<ref name="USACE2014" />
Infrastructure has been characterized as the mechanism that delivers the "..fundamental needs of society: food, water, energy, shelter, governance ... without infrastructure, societies disintegrate and people die." <ref name="USACE2014" /> As a society, Americans enjoy the use of "...highway, waterway, air, and rail systems that have allowed the unparalleled mobility of people and goods. Water-borne diseases are virtually nonexistent because of water and wastewater treatment, distribution, and collection systems. In addition, telecommunications and power systems have enabled our economic growth."
<ref name="ASCEinfrastructure">ASCE Critical Infrastructure Guidance Task Committee. "Guiding principles for the nation's critical infrastructure." American Society of Civil Engineers,2009.</ref> 
This development happened over a period of several centuries and represents a number of successes and failures in what is currently termed infrastructure
<ref>Hart, Steven D., J. Ledlie Klosky, and Scott Katalenich. "Conceptual models for infrastructure leadership." Journal of Management in Engineering 30.3 (2013): 04014003.</ref>
but also in the past was termed "public works" and even before that "internal improvements". 
Indeed in the United States, we have a long history of federal support for building great infrastructures such as the National Road, Erie Canal, transcontinental railways, inland navigation coupled with flood mitigation and control, the Panama Canal, and the interstate highway system and efforts in improving seismic design and construction over the last century. <ref name="USACE2014" /> This has also stimulated discussion among world leaders on the ...
:"''broad and urgent issues of energy, infrastructure (particularly for electricity and transportation), and climate change...(that) have become inextricably linked as societies around the world discuss, disagree, debate, and make decisions about properly balancing the production and use of energy against the quality of life and economic opportunity.'' <ref name="USACE2014" />
:'''Therefore, for any society, even the American society, its economy is "... inextricably linked with the infrastructure that supports it...(and) the social, political, and economic structure of a society can magnify or mitigate the effects of a failure in infrastructure and vice versa."''' <ref name="USACE2014" />

==Civil infrastructure and the rise of projects==
Civil infrastructure, like canals, railroads, bridges, etc., quickly became singular and substantial ventures for their sponsors, first cities and then nations. Not all succeeded, as the state bankruptcies in the 19th Canal building in the young United States demonstrate. Even at the height of the industrial revolution and the rise of modern society, less than a dozen nation-states built large-scale infrastructure. Even in the 21st century, infrastructure is still viewed as a hallmark of advanced economic development. Crucially, the unique character of these undertakings marks an "...explicit and deliberate disconnection from history and future" to create the exceptional circumstances that facilitate the accelerated delivery of this infrastructure given its heavy cost and long schedules.<ref name="Grabher2015">Grabher, Gernot, and Joachim Thiel. "Projects, people, professions: Trajectories of learning through a mega-event (the London 2012 case)." Geoforum 65 (2015): 328-337.</ref> 
This investment also required organizations to manage the "unprecedented scale and complexity of the challenges with regard to capital investments, public attention and stakeholder participation". <ref name="Grabher2015"/> Large organizations must be built, and specialized labor must be recruited and mobilized during this infrastructure effort. The sponsors must secure funding and negotiate with multiple stakeholders while contending with the daily direction of the endeavor. <ref name="Grabher2015"/> Once the endeavor has been accomplished, the organization is dissolved in what has been termed "institutionalized termination"<ref>R.A. Lundin, A. Söderholm, A theory of the temporary organization, Scand. J. Manage., 11 (4) (1995), pp. 437–455</ref>. <ref name="Grabher2015"/> These efforts to accomplish the national ambitions for infrastructure gave rise to the modern vision of projects and were a driving social factor in at least two ways: creating an environment that fostered first the rise of the civil infrastructure occupation and then the engineering professions as well as the related industry and professional communities. As such, the notion of a project allows a society to organize and manage its infrastructure endeavor as a distinct set of given tasks, separate from the “continuity of past/present/future” (Hernes et al., 2013) as well as an artificial constraint on its institutional life such as as a count down clock (Lundin et al., 2001)."<ref name="Grabher2015"/> 
:'''The constant pressure to deliver infrastructure effectively and efficiently has forced the development and evolution of the project as the primary vehicle for successfully delivering civil infrastructure.'''

==The Work of civil infrastructure and the rise of occupations==
'''''A project needs to be designed by and around people, and its success is dependent upon the will, commitment, and ability of those individuals for their creation, development, and termination.''''' <ref>Lundin, Rolf A., and Anders Söderholm. "A theory of the temporary organization." Scandinavian Journal of management 11.4 (1995): 437-455</ref>
Project organizations mobilize large numbers of people with a variety of education and experience in order to focus their energy in the form of work towards a common set of goals or objectives. A key factor in the success of these endeavors is the ability or capacity of the project to control the definition of the work tasks and the allocation of labor, more specifically, specific groups of labor to certain tasks. For civil infrastructure, this allocation is a continuum of labor "...differentiated by the [[Wikipedia:Locus_of_control|locus of control]] over the division of labor" <ref name="Lewis1971">Lewis, Bronwen A., Browen A. Lewis, and Charles K. Warriner. "The job-profession continuum." Kansas Journal of Sociology (1971): 34-44.</ref> by the project organization as a way of determining of who does what, managed by whom. This labor allocation is based upon the life experiences at any given time of the mobilized labor forces, stratified into cohorts and their subsets and constituting the basis for labor occupations. <ref name="Abbott2005"> Abbott, Andrew, Chapter Title: Sociology of Work and Occupations, Chapter Author(s): Andrew Abbott, Book Title:Smelser, Neil J., and Richard Swedberg, eds. The handbook of economic sociology. Princeton University Press, 2005.</ref> In this sense, when someone assigns a person an "occupation", it locates them in a category of workers accomplishing tasks within a division of labor. <ref name="Abbott2005"/> Abbott argues further that occupations have several other realities; namely, they are identifiable and stable groups of workers (as opposed to simple categories of transient work) with sustained participation, which may occasionally rise to the level of being formally recognized such as the crafts or professions.<ref name="Abbott2005"/> 
Project organizations manage the accomplishment of civil infrastructure within this space of project work and available labor by means of management and allocation of that mobilized labor. The fundamental locus of this process is the product of project work assigned to a specific labor group or occupation. Within this framework, one can define the term "job" as allocated work for which no control is exerted over the allocation. In this sense, an occupation is determined by the project organization, where there is consensus over the tasks to be allocated to this portion of the labor force. Classical thinking in sociology has taught that occupations were different than professions or crafts in that the consensus had not been developed through formalized training or regulatory authority. <ref name="Lewis1971"/>
:'''The constant pressure to deliver infrastructure effectively and efficiently has forced the development and evolution of the project organization's capacity and capability to control the definition of work and the allocation of labor.'''
:: In the case of the latter, it is important to allocate work to identifiable and stable groups of workers (as opposed to simple categories of transient work) with sustained participation, resulting in specialized experience and learning.
: Over time and with the emergence of technology, this allocation of labor would give rise to certain occupations achieving the level of being formally recognized, namely the crafts and professions.
==Civil Infrastructure and the rise of the engineering professions==
'''''The modern professions developed during the early eras of capitalism in Europe, but they were distinctive in societies increasingly dominated by the logic and spirit of the market... caveat emptor (“let the buyer beware”) versus credat emptor (“let the taker believe in us''”).'''<ref>Harper, D. (2007). Work and occupations. In C. D. Bryant, & D. L. Peck, 21st century sociology: a reference handbook. Thousand Oaks, CA: Sage Publications. </ref> 
'''''The aspirations of the nation for its infrastructure are often expressed ... (by) professional organizations.''''' <ref name="USACE2014" /> 
National ambitions for infrastructure are not always expressed or executed successfully. The early canal period in North America serves to make the point. The then colonies of Virginia and Pennsylvania conducted surveys to determine route locations and feasibility for water canals using existing waterways to connect the eastern Atlantic seaboard with the newly formed Northwestern territories, mainly the Ohio Valley. Europe went through this period from the 16th to early 18th century until the development of the "engineered" canal that connected two points, whether there was a waterway. Examples were the mid-18th century "Sankey" and "Bridgewater" canals. In the American case, the Pennsylvania Canal scheme of 1790 was unsuccessful and would not be revived until the completion of the Erie Canal in 1825. 
Due to the Industrial Revolution and the Enlightenment and the ambitions for accomplishing civil infrastructure, first projects evolved, and then specialized labor or occupations developed. As the complexity grew, so also did the related uncertainties associated with civil infrastructure, thus there was a social need for certainty. At first, it was scientific in nature, but then, the new concept of technology emerged. Professions and the newly formalized one known as engineering provide a special function in our society and economy where investing substantial sums of funding in public infrastructure requires "...some assurance that it will perform its intended function." This is not an absolute guarantee, but it is the engineer exercising their professional judgment that is "the practical certainty" that makes social investment in public infrastructure possible and makes the world "..a less uncertain place than it might seem..." 
The rise of modern society also required the "...unification of societies" and the development of national societies and economies. It was the compelling economic logic of this trend that provided the rationalization for the rise of the professions and their vision of occupational imperialism that closed markets. This has evolved in the late 20th and 21st centuries to become national frameworks of public and private civil infrastructure, sustained by public taxation and user fees, requiring "...a reasonably consistent means of interpreting and applying it..." thus the rise of the professions and particularly that of engineering, especially, civil engineering. 
The increasingly urgent challenge for the professions is that more and more of this 21st-century infrastructure possesses an inherent complexity and pace that may, at times, threaten to exceed the profession's ability to manage them to social expectations. The very essence of social acceptance and perceived legitimacy of the profession is the confidence that society places in it.
:"'''A large-scale society demands comparable institutions to sustain and regulate it. These are not all to be found in the state but the modern nation-state could not have developed without the co-evolution of professions such as civil engineering.'''"
==The Civil Engineering context==
:''The practice of the '''civil engineering discipline''' by '''civil engineering professionals''' under the overall direction of a professional society such as the American Society of Civil Engineers (ASCE) acting as the organized "'''civil engineering profession'''", '''impacts the health and vitality of a nation as no other profession does.''' The National Academy of Sciences has noted that the country's business competitiveness, military strength, health, and standard of living are intimately connected to engineering.''(Emphasis added) 
As noted above, modern societies require modern and complex civil infrastructure successfully delivered by project organizations that control work development and allocation of labor, especially specialized labor. The complexity of these infrastructure projects will require the use of new management forms such as civil infrastructure programs to align multiple projects on major objectives, transfer knowledge between projects, adapt strategies to subtle shifts in program layer goals/objectives, mediate conflicts between program layer sponsors and project layer stakeholders; deliver more specialized forms of management control, namely oversight. 
A significant portion of the latter of that assigned work is work allocated to engineering professionals, in this case, civil engineers. This will force changes in existing social forms and practices, continue to give rise to new labor allocations and recognize new civil infrastructure occupations. These changes and economic pressures will also challenge existing highly credentialed and regulated occupations, namely the engineering professions and civil engineering in particular.
:'''To generate value and social acceptance, the '''civil engineering profession''' must be able to identify, create, and continuously manage occupational and professional knowledge, especially technical knowledge, as knowledge may be the most strategically significant resource any profession can possess.'''
This will require the disciplined development and integrated application of empirical and theoretical knowledge, effective meta-knowledge, and meta-data in a manner that executes the project, manages risk, adds value, and meets or exceeds stakeholder expectations, which is the purpose of the '''civil engineering discipline'''. 

''See also [[Infrastructure Project Management and Civil Engineering Practices Based on Lessons from the Second Avenue Subway Project]].''

The '''civil engineering profession''' intends to deliver products, services, and projects in a manner that meets or exceeds stakeholder expectations, thereby earning social trust and gaining recognition as environmental stewards. The primary vehicle for fulfilling this intent is thru projects. Therefore, the civil engineering profession will continue to focus on defining, selecting, and implementing projects geared toward improving our natural and built environment, with sustainability being a key consideration and opportunity. 
The role played by the '''civil engineering profession''' in the nation's well-being requires the application of '''civil engineering discipline''' knowledge to be practiced by highly credentialed and sometimes licensed or certificated '''civil engineering professionals''', paraprofessionals and other civil infrastructure occupationals who are knowledgeable and skilled at continuously maintaining the integrity and adequacy of both the design and project life cycle strategy in conformance with relevant professional responsibility and ethical standards.
This role played by the '''civil engineering profession''' in the nation's well-being also requires '''civil engineering professionals''' to increasingly practice in conformance with established/codified sets of practices in the form of [[Bodies of knowledge]], [[Regulatory agency|regulatory requirements]], [[Program Management|programmatic requirements]] and [[Agency Guidance Practices for Civil Engineering|guidance practices]]. They will also operate within a practice framework of an increasingly specialized array of [[management plans and sub-plans]] supported by [[analytical and modeling deliverables]].

<!--There will also be legislatively imposed practice codes as well as licensure requirements for [[Wikipedia:Project_management|Professional Engineering project managers]], [[Wikipedia:Project_management#Project_controlling_and_project_control_systems|project controls]] and [[Risk Management|risk management]]. Each of the latter will be recognized as a specialized civil engineering application, meriting its recognition as a professional engineer.
Such a process does not exist for project managers of [[Wikipedia:Public_infrastructure|public sector]] [[Wikipedia:Infrastructure#Engineering|infrastructure]]. However, professional recommendations are starting to emerge from professional organizations that are [[Professional Practice Extensions|extensible]], i.e., capable of being extended to support policy goals espoused by public agencies for better project management performance and project cost and schedule risk management.

This future vision of the '''civil engineering profession''' and '''discipline''' can only be accomplished by '''civil engineering professionals''' practicing within a framework of [[Performance Management Systems (PMSs)|performance management systems (PMSs)]] that are [[Management control|controlled]] throughout the life of the project. This [[Management process|process approach]] to project delivery, in turn, is only practical in an environment where processes are defined, [[Project Documentation|documented]] and [[Project Monitoring|monitored]]. This vision also requires these processes to be tailored or adapted for project-specific requirements and continuously improved in a value-added way throughout the project life-cycle or duration. Lastly, it also requires characterizing and describing "uncertainties" in terms of variations in process, gaps in project data, limits to engineering knowledge and models, and lastly, [[Project stakeholder|stakeholder]] behavior that puts the project at risk.

This practice of civil engineering ("'''civil engineering practice'''") takes place within a framework of discipline quality, professional ethics and standard of care, and stakeholders' expectations and sense of value. This stakeholder expectation is that the following occur:
*Development and implementation of [[engineering tools]], [[solution sets]], etc., that represent the best trade-offs between costs and benefits to deliver the desired strategic outcomes successfully,
*Executing at all layers ([[Work Package Layer|work package]], Contract, [[Project Layer|project]] and [[Program Layer|program]]) to meet [[Project Objective|objectives]] and fulfill [[Requirement|requirement]]s for the [[Engineering complexity|complex]] project in a [[Project predictability|predictable]] and [[Project reliability|reliable]] manner that meets or exceeds [[Acceptance|acceptance criteria]].

Only a formal, process-based approach to discipline quality in project delivery by the "civil engineering profession" will deliver this result!

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Uncertainty and Risk in Civil Engineering Practice

2024-11-24T16:59:44Z

WikiSysop: /* Logical Framework for Specific Risk Taxonomies */

'''Life is short and the art long; the occasion instant, experiment perilous, decision difficult.''' (Hippocrates as quoted in Fox,)
:''See also [[Project Definition]] , [[Project Life Cycle and Phase Models]] , [[Project Delivery Methods]]''
:''See also [[Technical Outcomes for Civil Engineering:Risk and Uncertainty]]''
==Basic Considerations==
As human beings,'' ... being alive means seeking opportunities and taking risks.'' As knowledge professionals
living in the 21st century, this means coping with an increasingly complex number of uncertainties for humans
living in this environment. We seek to understand better how these uncertainties can be characterized and
managed. The essence of this article and the experience for engineers in general and civil engineers in particular is
that manageable uncertainty is, by definition, termed risk, and its kindred cousin is termed hazard. This causes us to experience the
:" ''... human dread of and fascination for risk and the increasingly important role of risk analysis within societies ...'' " <ref name="McDaniels"> McDaniels, Timothy, and Mitchell Small. Risk analysis and society: an interdisciplinary characterization of the field. Cambridge University Press, 2004, page 1 </ref> (Ibid.)
and, by extension, civil engineering. McDaniels et al. argue that risk management has been
fundamental to our social and governance development for the past 10,000 years. The scale and shape of the
uncertainties faced in this period shaped the societies that have developed today. The central thrust of this effort
over the centuries has been to reshape and re-frame our understanding and conception of uncertainty from one of
complete unknowing and simple acceptance as our fate in life to one of management (cf "''Against the Gods''"
concept in Bernstein's book <ref name = Bernstein">Bernstein, Peter L., and Jesse Boggs. Against the gods. Simon & Schuster 1997.</ref>.
: ''All the knowledge professions and disciplines have struggled with managing uncertainty, for it is impossible to manage unbounded uncertainty.'' <ref group="note">See McDaniels et al. 2004 for an extensive discussion and bibliography on the historical development of Risk Analysis and some key milestones in risk analysis in the 20th century.</ref>
As outlined below, professions such as civil engineering have successfully acted in the face of uncertainty. It lies in the profession's ability to reduce a wide variety of uncertainties into increasingly smaller and crucially bounded subsets that can be managed. These are called 'risks. More recently, this process has evolved, and individual disciplines such as Civil Engineering (CE) have developed their knowledge and models to perform risk analysis. Risk is also taught as a distinct discipline and specialty practice within civil engineering. Still, it has some unique features that set it apart from other more classical practices within CE. As such, it is one of the first of some very specialized CE practices that use knowledge about civil engineering knowledge, or meta-knowledge. Other examples of civil engineering metaknowledge are project controls and quality controls.
==Semantic, Epistemic and Logical frameworks==
The semantic, epistemic, and logical frameworks for uncertainty and risk have several dimensions and layers of logical frameworks. The semantics problems include interchangeable usages for risk and uncertainty and hazard, uncertain and imperfect, and a lack of definitional material or context. The semantical scheme for this article will be to proceed from the distinction between certainty and uncertainty through marginal refinements and reductions of uncertainty up to the point of causal uncertainties. Beyond this point of semantics will be the logic frameworks or the subject of taxonomies of professional knowledge. First, it is about simple, testable phenomena, and then, it moves on to complex taxonomy schemes for artificially constructed phenomena or engineering projects.
<ref group="note">TBD</ref>
==Semantics framework==
===Certainty and Uncertainty===
''''' There is no such thing as absolute certainty, but there is assurance sufficient for the purposes of human life.''''' ([https://en.wikipedia.org/wiki/John_Stuart_Mill John Stuart Mill]) 
'''''If you tried to doubt everything, you would not get as far as doubting anything. The game of doubting itself presupposes certainty.''''' ([https://en.wikipedia.org/wiki/Ludwig_Wittgenstein Wittgenstein] # 115 from On [https://en.wikipedia.org/wiki/On_Certainty Certainty]) 
It is important to note that the references to epistemic or epidemiological knowledge in this article are assumed to relate to three forms of knowledge, namely:
* Knowledge that ( [https://en.wikipedia.org/wiki/Descriptive_knowledge descriptive or declarative or propositional knowledge])
* Knowledge how ( or "[https://en.wikipedia.org/wiki/Procedural_knowledge know-how]"), and
* [https://en.wikipedia.org/wiki/Knowledge_by_acquaintance Knowledge by acquaintance].
'''Certainty''' has been defined as "an epistemic property" of knowledge in all its forms and the state of our beliefs about that knowledge.
<ref>Certainty, Stanford Encyclopedia of Philosophy, accessed on September 12, 2015, at http://plato.stanford.edu/entries/certainty/#ConCer </ref>
Certainty about any belief about knowledge implies that it is not subject to doubt or skepticism. This immunity to criticism can be dogmatically based, emphasizing the importance of a propositional-based sense of truth over experiential, sensory perceptions. <ref>Definition of [https://en.wikipedia.org/wiki/Dogmatic_theology Dogmatic theology or belief], accessed at Wikipedia</ref>
: '''The demarcation line between dogmatic and non-dogmatic beliefs lies in the presence and recognition of specific criteria and information that would make the believer change their beliefs.'''
An empirical framework of [https://en.wikipedia.org/wiki/Testability testability] and [https://en.wikipedia.org/wiki/Falsifiability falsification] is required to recognize this and limit dogmatism. For example, one could argue that people would change their minds if God asked them to. Similarly, one could construct a concept of epistemic as opposed to dogmatic belief certainty as the ability to know anything that one chooses to know and can be known or [https://en.wikipedia.org/wiki/Omniscience inherent omniscience].

A second kind of certainty is epistemic, when conviction reflects the highest possible support for a belief. In this sense, knowledge is separate from beliefs, although someone may have beliefs about a property, such as certainty of knowledge. Logically, it has been shown that there will be unprovable statements within the system for any such knowledge system. Secondly, the knowledge system cannot demonstrate its own consistency. ([https://en.wikipedia.org/wiki/Gödel%27s_incompleteness_theorems Gödel's incompleteness theorems])
: '''Certainty, in real life, is useless or often damaging (the idea is that "total security from error" is impossible in practice, and a complete "lack of doubt" is undesirable)''' ([[https://en.wikipedia.org/wiki/Certainty Physicist Carlo Rovelli]])
'''Uncertainty''', on the other hand, arises immediately in the slightest amount of doubt or criticism.
<ref group="note"> The Merriam-Webster dictionary defines uncertainty as “the quality or state of being uncertain,” which is something of a circular definition. Likewise, one could define uncertainty as the state of not being certain.

Synonyms are distrust, doubt, misgiving, mistrust, reservation, skepticism, and suspicion. Another writer added indefinite, indeterminate, not certain to occur, problematical, unreliable, untrustworthy, unknown beyond doubt, dubious, doubtful, not clearly identified or defined, not constant, variable, and fitful to the list.
:(Han, Paul KJ, William MP Klein, and Neeraj K. Arora. "Varieties of Uncertainty in Health Care: A Conceptual Taxonomy." Medical Decision Making 31.6 (2011): 828-838.)
Han et al. noted that any definition of uncertainty clearly encompasses "...numerous types, sources, and manifestations of uncertainty, and ...(any) ...useful working definition of uncertainty needs to specify the concept underlying these varied meanings of the term."</ref>
Implicit in this definition of uncertainty as a “state of” is ...
"...a conceptualization of uncertainty as a subjective, cognitive experience of people—a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one’s lack of knowledge, without which one could not feel uncertain; uncertainty is a form of “meta-cognition” ...(or its main component, meta-knowledge)... —a knowing about knowing." (Han, 2011, op. cit., Emphasis added)
Similarly, uncertainty could be defined as "...any departure from the unachievable goal of complete [https://en.wikipedia.org/wiki/Determinism determinism]."
<ref name="Walker1">Walker, W. E., Harremoës, P., Rotmans, J., Van Der Sluijs, J. P., Van Asselt, M. B., Janssen, P., & Krayer von Krauss, M. P. (2003). Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support. Integrated assessment, 4(1), 5-17.</ref>
Reasoning under uncertainty is very different than performing the same under certainty. In reasoning under certainty, one has complete knowledge and deduces without doubt and equally important, without limitation, thereby concluding free from error. Reasoning under uncertainty, one works in a state of incomplete, inconsistent, and limited knowledge; doubts cloud any statements or assertions and thereby taint any deductions/inferences resulting in the potential for error. <ref group="note">Implicit in this definition of uncertainty as a “state of” is ...
"...a conceptualization of uncertainty as a subjective, cognitive experience of people—a state of mind rather than a feature of the objective world. The defining feature of this state, furthermore, appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one’s lack of knowledge, without which one could not feel uncertain; uncertainty is a form of “meta-cognition” ...(or alternatively, its main component, meta-knowledge)... —a knowing about knowing." (Han, 2011, op. cit., Emphasis added)</ref>
:'''Knowledge professions reason in a state of incomplete, inconsistent and limited knowledge with doubts that cloud any statements or assertions and taint any deductions/inferences resulting in the potential for error.'''
:''' It is also important to note the semantical and logical correlation between 'reasoning under uncertainty' or 'acting in the face of uncertainty' with the 'potential for' or 'presence of' error. The presence of uncertainty is invariably linked to the potential presence of error.''' The [https://en.wikipedia.org/wiki/Contingency_(philosophy) contingent] nature of uncertainty logically implies the contingent nature of error.
:'''Knowledge Professions use error as a proxy for uncertainty such that within discipline knowledge frameworks, uncertainty can be managed.''' The rationale for this belief is that error can be reliably described, quantified, explained, and ultimately reduced in ways that uncertainty cannot.

'''Uncertainty''' implies a range of variation that is impossible in certainty. Concepts and definitions of uncertainty are unbounded and vast but start from the point of complete and total ignorance. In describing uncertainty, statements can range from complete ignorance to relatively high degrees of belief that there is less potential for error in our reasoning or action. The latter is based upon confidence in justified knowledge, favorable past experience, and reliable parameters/models. One can't make that statement about ranges in certainty. If certainty, by definition, precludes doubt of any type or nature, then how could that be graduated to any degree? 
Uncertainty, on the other hand, can be reduced through disciplined efforts to acquire knowledge. In short, we can reduce or even manage uncertainty (to a degree); how can certainty be improved? Are there higher degrees of perfection? The answer is no. We reason from an uncertain starting point. Yet this effort presumes, as Mill and Wittgenstein postulated, that we can develop or acquire an increasing degree of belief or conviction that there is less potential for error in our reasoning or action. This position assumes that some way exists to identify what a lesser degree of uncertainty would look like.
:'''Knowledge professions acquire an increasing belief or conviction that there is less potential for error in their reasoning or action based on justified knowledge, favorable past experience, and reliable parameters/models.'''
Uncertainty is, in some form, amenable to description and explanation through observation and analysis. It is explainable and predictable to such a degree that it interests professionals such as scientists and engineers. Professional knowledge of this type has explanatory or predictive power, albeit limited in scope and application to relevant subjects such as physical objects or phenomena and functionality.
:'''The very act of reducing the scope of uncertainty to a limited set of phenomena implies choice and, ultimately, the decision to act in the face of such uncertainty, but acting or judging in this manner adds more value than doing the same under relative ignorance.'''
'''Uncertainty''', as described above, implies a hybrid nature. Examples of this are economic or decision-theoretic applications. In economics, [https://en.wikipedia.org/wiki/Perfect_information perfect information] allows one within the limiting framework of perfect competition in economic games to make decisions with 'perfect knowledge.' The crucial difference here is the distinction between '[https://en.wikipedia.org/wiki/Perfection perfection],' broadly, a state of completeness and lawlessness, and 'perfect, which can be thought of as an analytical limit that is approachable but can never be attained. Arbitrarily delimiting the realm of knowledge into defined, finite boundaries allows the simulated production of perfect choice, assumed crucially, to be free from error. The hybrid nature of this form of 'simulated' certainty produced within boundaries defined by assumptions and, therefore, clouded by doubt is itself a form of uncertainty. The importance of this is the semantical and logical association that exists between certainty and perfection versus the hybrid concept of a perfect anything, whether knowledge, infraction, competition, etc., or in the case of engineering, elastic, permeable, conductive, etc., being contained within an admittedly imperfect matrix of uncertainty.
:'''Simply put, a finite, heavily bounded piece of uncertain knowledge can be improved through the simulated use of [https://www.merriam-webster.com/dictionary/assumed assumedly] perfect information, but the perfection of knowledge cannot be attained or simulated.'''
Engineering and Economics, for example, simulate finite elements of perfect knowledge or information, heavily bounded with assumptions and a range of applications. By doing so, these professions reduce the bounds and variability of uncertainty and thereby manage it. Using these hybrid models to acquire knowledge takes place in an economy (regulated or institutional) and environment (transparent and structured), which puts pressure on the various professions to recognize and use qualitative and quantitative knowledge better to reduce uncertainties in their activities. The challenges in this reflect the nature of the profession's or discipline's knowledge (e.g., scientific, engineering, or medical), the policies and structure of the relevant professional knowledge bodies, process objectives, constraints, and "the elusive demands of politics." <ref name="siren">Walker, Vern R. "The siren songs of science: toward a taxonomy of scientific uncertainty for decision-makers." Conn. L. Rev. 23 (1990): 567.</ref>
One common theme runs through all of these efforts to address the presence of uncertainty in professional activities. 
:"The available scientific information upon which a decision must be made is almost always a mixture of engineering knowledge and uncertainty. Regardless of the information or methodology, there is the potential for error. This is true regardless of the relevant science, whether archaeology or aeronautics, economics or engineering, pharmacology or toxicology or epidemiology. Achieving the best social decisions requires not only understanding and using what we know but also appreciating and weighing the extent of our uncertainty. In making the best use of ... information in ... decision-making, it is still true that the beginning of wisdom is knowing what it is we do not know." (Vern, Op. cit., Reformatted and engineering substituted for scientific) 
'''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty to develop explanations, calculate, and make predictions. This professional knowledge converges on but never reaches certainty, producing a "legitimacy" from the fruitfulness of its use.''' <ref>Randomness Is Unpredictability, Antony Eagle, The British Journal for the Philosophy of Science, Vol. 56, No. 4 (Dec., 2005), pp. 749-790 </ref> 
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=== Bias and Uncertainty ===
Up to this point, the concept of error, as outlined in uncertainty, could easily be simulated by a truly random variable. There should be no detectable differences around the unseen or specified statistical mean. However, several instances in experience point to a tendency towards one extreme or a pattern of error, a collective that is in itself an error of errors, namely [https://en.wikipedia.org/wiki/Bias bias].

Arguably, this could be part of the choice uncertainty discussion below, but it makes more sense to be looked at on its own. Acting in the face of uncertainty is an exercise of personal knowledge or experience. Whether using professional knowledge or individual experience, the potential is there to make a series of choices that, in some instances, make errors more likely than they would be if considered in the context of a statistical analysis.

This tendency for an individual to make the same error in a predictable or repeatable manner is termed bias. Personal experience, beliefs, knowledge, data, and models all have inherent built-in factors, errors, or defects that create predictable error patterns when applied in decision-making. Walker (1998) argues implicitly that such bias can also be considered systemic error. This type of uncertainty is difficult to identify except on theoretical grounds when the magnitude and direction of the bias are known. Most of these biases, whether systemic or knowledge-based, individual or practice-based, require disciplined efforts to "validate" such methods, models, and judgments at all layers while bounding the uncertainty.
: This is the challenge to professional knowledge that must be overcome, namely, developing and validating models and methods for managing bounded uncertainty that minimize this tendency towards repeatable groups of error or bias. Therefore, from this point forward, the discussion will refer to 'uncertainty and its proxies, error, and bias.'
===Propagating Error and Uncertainty===
Partitioning uncertainty and its proxies, error, and bias into logically distinct subsets involves or is associated with multiple-step execution or choice frameworks. Combining those measurements or choices into single parameters or choices involves, in some cases, assembling the data/information in a unique or limited range of sequences. This implies that associated errors in observing, calculating, or choosing will have unequal impacts or consequences depending on where in the chain the error occurs. Also implied in this is the possibility that subsequent errors will be causally linked to some degree. (In statistical practice, this depends upon whether the errors are independent of each other or correlated, specifically, co-variant.)
:'''Professions develop knowledge of how uncertainty and its proxies, error, and bias propagate through an ordered and normative series of observations or choices. This allows the profession to prioritize error and bias reduction to achieve an optimized reduction of uncertainty.'''
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===Cognition, Metacognition and Uncertainty===
The concept of choice and the resultant error in uncertainty can be further delimited depending on the role of [https://en.wikipedia.org/wiki/Cognition cognition], [https://en.wikipedia.org/wiki/Metacognition meta-cognition], and independent external elements. The rationale for this argument is that such choice activity in the face of uncertainty is a conscious, cognitive act.

An argument could be made that such cognition is a prerequisite for exercising choice and introducing error, as presented and discussed above. The counterfactual to this argument is the example of uninformed choice. Choices introduce as much as errors or more as informed choices could be made. (???) Choice, in this sense, is indifferent to knowledge. Purposeful choice in an environment of objectives requires knowledge, method, and experience, but uninformed, speculative choice does not.
:"''' Cognition''' is the set of all mental abilities and processes related to knowledge, attention, memory and working memory, judgment and evaluation, reasoning and "computation," problem-solving and decision making, comprehension and production of language, etc. Human cognition is conscious and unconscious, concrete or abstract, as well as intuitive (like knowledge of a language) and conceptual (like a model of a language). Cognitive processes use existing knowledge and generate new knowledge." (Wikipedia)
:"'''Metacognition''' is "cognition about cognition", or "knowing about knowing" and can take many forms. It includes knowledge about when and how to use particular strategies for learning or problem-solving. There are generally two aspects of [https://en.wikipedia.org/wiki/Metacognition metacognition]: knowledge about cognition and regulation of cognition."
Some types of metacognition knowledge are:
:'''Person knowledge''' (declarative knowledge), which is understanding one's own capabilities.
:'''Task knowledge''' (procedural knowledge), which is how one perceives the difficulty of a task, which is the content, length, and type of assignment.
:'''Strategic knowledge''' (conditional knowledge) is one's capability to use strategies to learn information. (Accessed at Wikipedia)
Like metacognitive knowledge, metacognitive regulation or "cognitive control" contains three essential skills.
:'''Planning:''' refers to the appropriate selection of strategies and the correct allocation of resources that affect task performance.
:'''Monitoring:''' refers to one's awareness of comprehension and task performance.
:'''Evaluating:''' refers to appraising the final product of a task and the efficiency at which the task was performed. This can include re-evaluating strategies that were used. (Accessed at Wikipedia)
Nothing has been said up to this point that limits this structure to an individual, or a collective, or two entities making cognitive choices that are incompatible or inconsistent to varying degrees. Interpretations of this scheme include an individual professional exercising judgment in a decision that, in turn, relies upon the collective judgment and decision of the discipline as an underlying basis versus the decision of another individual to oppose/protest or otherwise contest that decision. Thus, the framework of cognitive/meta-cognitive applies to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, economics of regulatory practice, and game theory.
:By solving useful problems in the face of inherent uncertainty, discipline-specific frameworks of cognitive/meta-cognitive activity apply to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, the economics of regulatory practice, and game theory.
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===Probability and Uncertainty===
Probability is a [https://en.wikipedia.org/wiki/Coherence_theory_of_truth coherent] approach to uncertainty in the physical and mathematics or "classical" domains such as engineering. <ref>See Colyvan for a critique of this claim, Colyvan Mark. "Is probability the only coherent approach to uncertainty?." Risk Analysis 28.3 (2008): 645-652.</ref> and is the "...is certainly the best-known and most widely used formalism for quantifying uncertainty.<ref>Morgan, Millett Granger, Max Henrion, and Mitchell Small. Uncertainty: a guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge University Press, 1992.</ref> [https://en.wikipedia.org/wiki/Cox%27s_theorem Cox’s theorem] ("Any measure of belief is [https://en.wikipedia.org/wiki/Isomorphism isomorphic] (but not necessarily equal) to a probability measure") is a well-known argument for the validity of that argument.
<ref group="note">Cox wanted his system to satisfy the following conditions: 
Divisibility and comparability – The plausibility of a statement is a real number and is dependent on the information we have related to the statement.
:Common sense – Plausibilities should vary sensibly with the assessment of plausibilities in the model.
:Consistency – If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). — preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf</ref>

Another writer, [https://en.wikipedia.org/wiki/Frank_Knight Knight] (1921,1956), presented the following taxonomy of probabilities:
<ref>As presented and discussed in Runde, Jochen. "Clarifying Frank Knight's discussion of the meaning of risk and uncertainty." Cambridge Journal of Economics 22.5 (1998): 539-546.</ref>
: '''Classical 'a priori' probability:''' As an idealized model, numerical probabilities are computed based on generalized principles of assigning equal likelihoods and mutually exhaustive possible outcomes (Runde, 1998) to all set members. Such probabilities are assigned to "...outcomes based on a judgment of indifference between those outcomes, that is, based on the absence of any evidence of real influences in play that may render any one outcome more or less probable than any other." (Runde, op. cit.) This builds upon Knights's concept of an 'absolutely homogeneous classification of instances that are completely identical except for [https://en.wikipedia.org/wiki/Indeterminate_(variable) indeterminate] factors. This judgment of probability or logical probability is on the same logical plane as the propositions of mathematics and ultimately [https://en.wikipedia.org/wiki/Inductivism inductions] from experience.'(Knight, 1956, pg.225)
: '''Bayesian 'a priori' probability:''' In contrast to interpreting probability as the "frequency" or "propensity" of some phenomenon, [https://en.wikipedia.org/wiki/Bayesian_probability Bayesian probability] is a quantity that we assign to represent a state of knowledge or a state of belief. In this view, probability is assigned to a hypothesis, often using the basis of past or prior experience. In contrast, under the frequentist view, a hypothesis is typically tested without being assigned a probability based on prior experience. The Bayesian interpretation of probability can be seen as an extension of propositional logic that enables reasoning with hypotheses, i.e., propositions whose truth or falsity is uncertain. Bayesian probability belongs to the category of evidential probabilities; to evaluate the probability of a hypothesis, the Bayesian probabilist specifies some prior probability, which is then updated in the light of new, relevant data (evidence). The Bayesian interpretation provides a standard set of procedures and formulae to perform this calculation. <ref>See also Larvor, B. "After Popper, Kuhn, and Feyerabend: Recent Issues in Theories of Scientific Method." Metascience (2002).</ref>
: '''Statistical or 'a posterior probability:''' Empirical evaluation of the frequency of association between predicates, not analyzable into varying combinations of equally probable or logical, 'a priori' alternatives. Knight argued that any "high degree of confidence" that the judgment that experience will remain valid for future predictions is "...still based on an 'a priori" judgment of indeterminateness." (Knight, Op. Cit.) Knight's argument to support this is what will be discussed further below that first, "...the impossibility of eliminating all factors not really indeterminate; and, second, the impossibility of enumerating the equally probable alternatives involved and determining their mode of combination so as to evaluate the probability by a priori calculation." (Knight, Op. Cit.) Knight noted that the main difference between this form and that of logical or inductive probability was the presence of empirical information, which allowed the analyst to identify propensity and direction in the observed phenomena. In this case, 'a priori' probability values may be derived from first principles and "statistical probabilities are determined a posteriori by the empirical method of counting instances." (Runde [1998] quoting Knight, op. cit.)
:'''Estimates:''' The distinction here is that there is no valid basis ('a priori' or 'a posteriori') for any classifying phenomena. For Knight, this form of probability presented the greatest logical difficulties of all ... but its distinction from the other types must be emphasized, and some of its complicated relations indicated..." (pp. 224-5, emphasis in the original)

Knight's concept of probability can be viewed as "..a continuum of probability situations, depending on the degree of homogeneity of the 'instances' in question" <ref>Runde, Jochen. "Clarifying Frank Knight's discussion of the meaning of risk and uncertainty." Cambridge Journal of Economics 22.5 (1998): 539-546.</ref>; i.e. going from a logically distinct but equally likely set of elements to a unique set with one member with statistical frequency in the middle. Knight's main motivation for distinguishing between a priori probability and statistical probability underscores his opinion that "(i) the 'mathematical or a priori type of probability is practically never met with in business, while the second is extremely common'; and (ii) 'the statistical treatment never gives closely accurate quantitative results' (Runde quoting Knight pp. 215-16). Runde argues that Knight couldn't conceive of (empirically tabulated) occurrences that are used in daily commerce classes of instances that we have to make do within the course of everyday economic life could be divided into subclasses of instances that are sufficiently homogeneous to permit the determination of what he calls 'real' probability (p. 217).
:'''In modeling degrees of uncertainty, any measure of belief is isomorphic but not necessarily equal to a probability or statistics measure'''.
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===Explanation, Prediction and Uncertainty===
It should be clear that the general notion of uncertainty synonymous with complete ignorance must be bounded as part of a scheme to produce a useful concept of uncertainty in professional practice such as civil engineering.
:The first principle that must be introduced is that such uncertainty must be bounded by reliable and valuable knowledge gained from past experience combined with analytical and computational capabilities to address an immediate and real problem of interest. This could be viewed as the economic interest argument. Only uncertainties associated with physical phenomena and economic scarcity will be addressed.
:The second principle is that the knowledge model is capable of producing statements or assertions in the form of hypotheses that themselves are testable. [https://en.wikipedia.org/wiki/Testability Testability], in this sense, is defined as the property applying to an empirical hypothesis and involves two components:
::The logical property that is variously described as contingency, defeasibility, or falsifiability, which means that counterexamples to the hypothesis are logically possible. Contrast this to a [https://en.wikipedia.org/wiki/Tautology_(logic) logical tautology], which is always true that is true in every possible interpretation. The practical feasibility of observing a reproducible series of such counterexamples if they do exist. In short, a hypothesis is testable if there is some real, non-zero expectation of deciding whether it is true or false of verifiable, reproducible experience. Upon this property of its constituent hypotheses rests the ability to decide whether a theory can be supported or falsified by actual experience data. (Source Wikipedia.)
'''Knowledge models must be capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria described above in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error and bias.'''
===Variability and Aleatory Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge referred to as epistemic uncertainty. Acting in the face of epistemic uncertainty introduces choice uncertainty (discussed below) and results in the production of explanations and predictions. It is implied that there will be testable hypotheses in the form of outcomes of interest to the knowledge professions. Inherent in nature is a variation of results, which may appear to uncertain but isn't. Observations of phenomena will not be the same, and part of the experimental process is to narrow those results down to within an acceptable level of tolerance. Statistical measures seek to identify underlying and unobserved measures of central tendency. These parameters are not directly observed but are derived to a degree of confidence.
In some cases, variation around a mean or within a statistical range may be perfectly acceptable to the knowledge profession. Examples of this are in the physical sciences and medicine. In Engineering and, to a lesser extent, economics, the process or phenomenon may be required to be more controlled to a narrower range than what a natural or "unmanaged" amount of variation would allow. Several courses of action present themselves.
:The first would be to ignore the aleatory phenomenon and choice, which is rejected outright.
:The second would be to assume that, in effect, such variation is itself a form of minimally managed uncertainty and treat it the same as epistemic uncertainty. This is not a desirable outcome as reducing Aleatory error and bias requires
:A third approach would be to acknowledge that process variation is, in some forms, manageable. This means making choices and acting in a way that presumes that the natural occurrence of error can be reduced, or more importantly, bias can be reduced. There is ample precedent for the third approach in civil engineering design and project management. In this sense, the argument is made that the aleatory response to managed efforts results in an elastic variation range. Presumably, an effective management effort either "shifts" the central tendency of the process results or reduces the variation range of occurrence or some other population parameter. Choosing to act and accept that narrower range rather than the broader natural variation may result in an outcome that, while well within the bounds of the expected variation, is outside the arbitrary control limits established by the choice and action. While this phenomenon mimics the broader uncertainty and its proxy error, it is not an error. The information on the variation was known, and the ability to produce narrower results was an error, albeit a pseudo-error, when compared to the broader classes of uncertainty discussed above, such as total ignorance.
::Looking at uncertainty using this third approach means that the error in choosing a new target parameter for this process may contain epistemic errors (our knowledge of the phenomenon may be erroneous, or the model flawed), aleatory uncertainties as discussed, or, more importantly, choice error or bias in thinking that we can move the process results to meet the requirements.
:'''In civil engineering practice, this may mean applying professional judgment in developing partitioning schemes or allocating uncertainty between the three dimensions (Epistemic, Aleatory, and Behavioral) of uncertainty.'''
Lastly, raising the topic like this brings the questions of effectiveness and efficiency to the fore. Advancing knowledge models that reduce error and bias at first are largely choice models resulting in justifications and favorable experiences. At some point in the process, the knowledge profession advances explanations and predictions that require reliable mathematical or logical concepts that are reduced to models and parameters. The question of efficiency becomes important; the broader uncertainty may have been reduced, but the process is economically inefficient. The profession is urged to become more frugal or, for example, reduce the variability of the outcomes. Knowledge professions often operate under conditions that require them to acquire knowledge and reduce uncertainty effectively and efficiently. Doing so requires understanding both epistemic uncertainty, as discussed above, and statistical process variation or aleatory variation.
:'''Knowledge professions manage and reduce uncertainty in the form of error and bias in an economically effective and efficient manner. This effort requires understanding both epistemic and aleatory uncertainty.'''
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===Choice and Behavioral Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge, referred to as epistemic uncertainty, as well as the inherent variations in results, or what was termed aleatory uncertainty. Acting in the face of that epistemic and aleatory uncertainty introduces yet another uncertainty into the process, namely choice uncertainty. This uncertainty reflects uncertainties associated with first-person and third-person choice. This is particularly relevant for engineering with project stakeholders.
===Summary of Semantics Framework for Uncertainty===
Professional disciplines have gradually re-framed their understanding of uncertainty and, thru experience and analysis, have arrived at the following meta-knowledge concepts of uncertainty:
* '''Uncertainty and its proxy, error, have three components: [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic epistemic uncertainty], [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic aleatory uncertainty], and behavioral uncertainty.'''
* '''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty using non-dogmatic beliefs, which recognize the existence of specific criteria and information that would make the believer change their beliefs using coherent approaches such as probability.'''
* '''Knowledge professions develop knowledge of how uncertainty and its proxy, error, propagate through an ordered and normative series of observations or choices that meet testability criteria in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error. An error can then be described, explained, and reduced in ways that uncertainty cannot. This allows the professions to prioritize error reduction to achieve an optimized reduction of uncertainty and produce a "legitimacy" from the fruitfulness of its use.'''
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===Uncertainty versus Risk/Hazard===
Up to this point, nothing has been said about risk or hazard. Now, risk can be defined. Simply put risk or hazard are finite, logically distinct subsets of the broader uncertainty. Risk and hazard are currently defined interchangeably. First and foremost, risk is a specific subset of general uncertainty. Any arbitrary subset of general uncertainty can be defined as a risk if it meets the following criteria:
* Uncertainty is associated with the potential for material error when acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena.
* Uncertainty and its proxy, error, can be partitioned into logically distinct subsets associated with multiple-step execution or choice frameworks in physical environments linked to a unique or limited range of sequences.
**Subcriteria 1:Resulting errors will have varying impacts or consequences depending on where the error occurs and the degree of causal linkage or influence in the chain.
**Subcriteria 2:Potential errors can be sequenced and prioritized to achieve an optimized reduction of error or bias and, by proxy, uncertainty over time.
* Uncertainty and its proxies, error bias, and environments are isomorphic to coherent approaches such as probability.
* Uncertainty and its proxies, error, and bias are isomorphic to coherent approaches such as knowledge models capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria in 'reasoning under uncertainty.'
This is a definition of general risk. There is no differentiation for engineering or economics, medicine or insurance. There is no difference between a 'good' risk consequence and a 'negative' one. Redefining general risk into simpler terms gives the following:
:'''General Risk is a subset of general uncertainty that is associated with the potential for material errors and biases when sequentially acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena; isomorphic to probability measures and methods and capable of testability.'''
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==Logical Framework for Specific Risk Taxonomies==
As noted above, at the most fundamental level, uncertainty is ".....the subjective perception of ignorance."
<ref name="Han">Han, Paul KJ, William MP Klein, and Neeraj K. Arora. "Varieties of Uncertainty in Health Care A Conceptual Taxonomy." Medical Decision Making 31.6 (2011): 828-838.</ref>
At this point in the discussion, the focus has moved from discussing uncertainty to its framed subset, risk.
:'''Further, the discussion moves from general risk to a professional or discipline-specific risk [https://en.wikipedia.org/wiki/Taxonomy taxonomy]. Beyond this point, risk will be used in place of uncertainty.'''
Han et al. note that, in general, "(t) taxonomies are valuable not only in their comprehensiveness but in their coherent reduction of uncertainty to conceptually discrete elements."
Uncertainty (and, by implication, risk or hazard) taxonomies offer an approach or tool to more precisely identify and define ontological uncertainty so that it can be quantified, analyzed, and communicated. Viewed this way, uncertainty is not a single, monolithic phenomenon but "...multi-dimensional with theoretically distinct domains and constructs that are potentially measurable and related to different outcomes, mechanisms of action, and management strategies." <ref name="Han"/> Taxonomic schemes for classifying the different kinds of scientific and, by extension, engineering uncertainty require identifying the various kinds of potential error associated with descriptive scientific or engineering information and knowledge. An example of this is the following quote from a legal authority:
:"I would especially stress the need for an agency to disclose the uncertainty that surrounds its determinations. ''' And by uncertainty, I mean the agency's ignorance as well as its quantitative estimates of error."''' (Emphasis added)
<ref>Walker citing Bazelon, Science, and Uncertainty: A Jurist's View, 5 HARV. ENVTLt L. REv. 209, 212 (1981).</ref>
''' Given the necessity of acting in the face of enormous uncertainties
<ref>Vern citing Ruckelshaus, Science. Risk, and Public Policy, 221 SCIENCE 1026, 1027 (1983)</ref>,
discipline taxonomies must offer a clearly coherent and probabilistic approach to uncertainty that is sufficiently general in nature, logically distinct and exhaustive in scope and "...provide decision-makers with a foundation for understanding the nature of discipline information..."''' <ref name="siren"/>

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=== Scientific Risk Taxonomies ===
The [https://en.wikipedia.org/wiki/Scientific_method scientific method] is a body of techniques for investigating phenomena in its environment, acquiring new knowledge, or correcting and integrating previous knowledge. Inherent in that process are uncertainties of many forms. Any one taxonomy of [https://en.wikipedia.org/wiki/Uncertainty scientific uncertainty] has largely been focused on statistical models used to assess and quantify sampling, errors, and parameters, although several different taxonomies are conceivable: note-24 [18]
====Environment-specific, Phenomena oriented, model-centric taxonomy of uncertainties====
** [https://en.wikipedia.org/wiki/Uncertainty_quantification#Sources_of_uncertainty '''Parameter uncertainty'''] the source of which is model parameters that are inputs to the computer model (mathematical model) but whose exact values are unknown and cannot be controlled, or whose values cannot be exactly inferred by statistical methods. Subsets of this type of uncertainty include but are not limited to:
***'''Experimental uncertainty''' is also known as observation error, which comes from the variability of experimental measurements. An example is repeating an experiment measurement several times using exactly the same settings for all inputs/variables and recording the variability. It is a subset of the larger uncertainty component, parameter uncertainty.
*** '''Parametric variability''' comes from the variability of the input variables of the phenomena model and is also a subset of the larger uncertainty component, parameter uncertainty.
** '''Structural uncertainty''', or model inadequacy, model [https://en.wikipedia.org/wiki/bias bias] or "[https://en.wikipedia.org/wiki/Systemic_bias systemic bias]", or model discrepancy, which comes from the lack of knowledge of the underlying true state of the phenomenon or its environment. It depends on how accurately a mathematical model describes the true state of the phenomenon or what is termed "model fitness". Due to the inherent nature of uncertainty in any knowledge body (scientific or engineering), models are only an approximation to reality. Therefore, any conclusions drawn from the model can be very misleading when the underlying basis is not plausible or lacks validity. '''note-25 [19]]'''
*** What is missing in this context is any concept of output variability due to the model itself. Part of this is due to the lack of recognition of "process" in scientific investigation. The concept of the scientific method started with a single investigator, such as Galileo or Michael Faraday, working in their laboratories. It has grown into planet and solar system scale experiments such as data gathered on planetary flybys such as Mars, Pluto, and Ceres to the hunt for the Higgs Boson. Ultimately, science has become more like engineering in the scale and complexity of its investigations and theory aggregates, such as the "Unified theory" of particle physics. Subsets of this type of uncertainty include but are not limited to:
*** '''Algorithmic uncertainty''', or numerical uncertainty, comes from numerical errors and numerical approximations per implementation of the computer model. Most models are too complicated to solve exactly. For example, the [https://en.wikipedia.org/wiki/Finite_element_method finite element method] or [https://en.wikipedia.org/wiki/Finite_difference_method finite difference method] [may be used to approximate a solution [https://en.wikipedia.org/wiki/Partial_differential_equation partial differential equation], which introduces numerical errors. Other examples are numerical integration and infinite sum truncation, which are necessary approximations in numerical implementation.
*** '''Interpolation uncertainty''' comes from a lack of available data collected from computer model simulations or experimental measurements. For other input settings that don't have simulation data or experimental measurements, one must interpolate or extrapolate to predict the corresponding responses.
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====Commentary====
A more comprehensive taxonomy of uncertainties that acknowledges knowledge, data, and linguistic uncertainties is:
*'''Epistemic uncertainty''' -[https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Epistemic uncertainty] or systematic uncertainty, in contrast, reflects limitations in the current “state of knowledge” underlying models themselves, originates from competing theories or models, is not readily quantifiable, and is manifest by subjective confusion or indecision. It includes uncertainty due to measurement limitations, insufficient data, extrapolations and interpolations, and variability over time and space. Epistemic uncertainty is uncertainty about "...some determinate fact ... because of a lack of complete information. '''cite_note-26 [20]]''' Epistemic uncertainty can be classified into six main types:'''cite_note-27 [21]]'''
*''' Measurement error''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Observational_error Measurement error] is uncertainty that manifests itself as (apparently) random variation in the measurement of a quantity. Repeated measurements will vary and demonstrate classic statistical behavior.
* '''Systematic error''' - [https://en.wikipedia.org/wiki/Observational_error Systematic error] occurs due to bias in the measuring equipment, model, analysis, observation, or sampling procedure. It is formally defined as the difference between the true value of the quantity of interest and the value to which the mean of the results converges as sample or data sizes increase. Unlike measurement error, it is not (apparently) random, and therefore, results subject to systematic error alone do not vary about a true value. Systematic error can result from deliberate judgment to exclude (or include) data, parameters, or models that ought not to be excluded (or included).
** "The only way to deal with systematic error is to recognize a bias in the process, model, or procedure and remove it. Systematic error, however, is notoriously difficult to recognize except on theoretical grounds. Corrections may only be applied when the magnitude and direction of the bias are known. Such corrections underlie the application of double-sampling methods in environmental science." (Regan, et. al., op. cit.)
*'''Natural variation''' - [https://en.wikipedia.org/wiki/Natural_process_variation Natural variation] or underlying process variation occurs in systems that "...change (concerning time, space, or other variables) in ways that are difficult to predict...across the full range of temporal and spatial values (or other related variables)." '''note-28 [22]'''
** This form of uncertainty is reduceable utilizing tools such as [https://en.wikipedia.org/wiki/Statistical_process_control statistical process control] and replication of experiments or observations.
*'''Inherent randomness''' - [https://en.wikipedia.org/wiki/Randomness randomness] or [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty aleatory uncertainty] exists because the system is"...in principle, irreducible to a deterministic one (the most well-known case is described by [https://en.wikipedia.org/wiki/Uncertainty_principle Heisenberg’s uncertainty principle] in quantum mechanics)." '''note-29 [23], note-30 [Note 7]'''
** Even if the argument is accepted that it is unlikely that any given physical system at some level is inherently random, it is important for any taxonomy to distinguish between systems that appear random due to incomplete or inconsistent information and those that are intrinsically random. (Again, see Regan (2002)).
*'''Model uncertainty''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling Model uncertainty] occurs in the process of generating a model as a conceptual representation of a particular phenomenon to create a [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling#Overview simplified reflection of reality]. This selectivity of factors and variables creates uncertainty in at least three ways. (Regan (2002))
** '''Variables and processes''' that are regarded as relevant often represent a trade-off between system knowledge and assumed states and model objectives;
** '''Models''' depict observed processes using logical or mathematical constructs based on underlying theories about system states or dynamics using continuous equations to describe discrete processes.
** '''Curve fitting''' (including interpolation and extrapolation) with mathematical expressions using model variables where inputs are discrete data points.
** Regan (2002) argues that '''model uncertainty''' is "...notoriously difficult to quantify and impossible to eliminate ...(and)...(t)he only reliable way of determining how appropriate a model is for prediction is to perform validation studies."
** Subjective judgment occurs due to the interpretation of scarce or error-prone data.
*** The only way to address this type of uncertainty is to "...assign a degree of belief about an event in the form of a subjective probability." (Regan (2002))
*Linguistic uncertainty - Linguistic uncertainty, or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. '''note-31 [24]], note-32 [25]]'''
** '''Context dependence''' is uncertainty concerning the context in which a statement is to be understood;
** '''Ambiguity''' occurs when words have multiple meanings, and further reduction to a single meaning is not logically possible in a given context.
** '''Underspecificity''' occurs in the presence of "unwanted generality" or multiple interpretations, and reduction to a smaller set or even a single interpretation is not logically possible.
*Stochastic or statistical uncertainty - Stochastic or statistical uncertainty, which is sometimes known as [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Aleatoric uncertainty] as well as the subject of [https://en.wikipedia.org/wiki/Stochastic_control stochastic control] pertains to the parameters of a risk model, originates from sampling or measurement error, and can be quantified and mathematically expressed (e.g., using confidence intervals).
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== Uncertainty Taxonomies in a Legal Environment ==
From a legal perspective, a definition of taxonomy is dictionary-based, such as "the systematic distinguishing, ordering, and naming of type groups within a subject field." given by Walker (op. cit.). Such legal concepts of scientific or engineering taxonomies are not required to be epistemically complete. Still, they must cover "the most significant aspects of ...(current, good discipline practices) ... and of the descriptive information commonly encountered by decision-makers" in a logically distinct manner. (Walker, 1991, p. 571) These taxonomies must be sufficient to catalog the types of uncertainty associated with descriptive assertion, including the critical subset of cause and effect assertions. This legal/decision-maker framework has presented descriptive uncertainty as having six components (Walker, 1991). Although not part of the Walker taxonomy, underlying the scheme is linguistic uncertainty, or "vagueness," as a source of uncertainty, including uncertainties due to context dependence, ambiguity, and under-specificity. '''note-33 [26]]''' Arguably, these uncertainties underlie the most basic element in the taxonomy.
* '''Linguistic '''- or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. (Regan, 2002)
* '''Conceptual'''- the definition by choice and design of descriptive concepts or variables to be used as [https://en.wikipedia.org/wiki/Predicate_(grammar) predicates] where the subject of an assertion identifies what is being discussed and the predicate provides information about the topic or characterizations such as what the subject is, what the subject is doing, or what the subject is like (Wikipedia).
* "Conceptual uncertainty, or the potential for conceptual error, arises whenever predication occurs. Whenever a concept is used to describe something, using certain concepts instead of others begins to structure how we understand the object, event, or instance under discussion. Predication or conceptualization generates useful information about things, but it also can inhibit our ability to think about those things with concepts other than those selected. The concepts used may not be the most fruitful or the best designed- either for scientific purposes or for making wise, fair, effective, and efficient decisions." (Edited for reading, Walker, 1991)
* Similarly, choice and design of descriptive variables enhance and restrict data set membership, creating potentially incomplete, incompatible, or inconsistent data sets, "...the classification categories employed, and the relationships among those categories (nominal, ordinal or scalar)." '''note-34 [27]]'''
* Concept uncertainty '''note-35 [28]''' like systematic error discussed above is notoriously difficult to recognize, except on theoretical grounds, and the magnitude and direction of the bias are known.
Both terms could be analogized to that of [https://en.wikipedia.org/wiki/Accuracy_and_precision#Terminology_of_ISO_5725 accuracy] in [https://www.bipm.org/en/publications/guides/ ISO 5725] which looks at the ability of the concept or system's ability to produce results that are proximate to the "true" value or state of the phenomenon. Conceptual uncertainty and systemic bias are related to another measure of uncertainty: validity. Walker has defined validity as the ability of a concept to quantitatively describe and explain what the phenomenon objective "truly" is. Walker also ties the concepts together when he states, "Validity concerns the "accuracy" of the measurement data, not its precision." (Walker, 1998) 
Additionally, conceptual uncertainty and associated validity issues could be propagated throughout this taxonomy. Every component of action and decision in the face of uncertainty raises validity issues. Walker recognized this when the author defined epistemic choice for concepts used throughout the other uncertainty components or layers.
* '''Measurement''' uncertainty or Misclassification error- the application of the underlying concepts or variables to specific, individual cases and the uncertainty of the reliability (Walker, 1998) of the resultant data. (It is important to remember that assessment observation and measurement are used interchangeably in this analysis.) Thus, an assessment method or procedure is considered "reliable" or "precise" in the scientific terms of ISO 5715 if it repeatedly produces consistent results.
* Measurement uncertainty is logically distinct from conceptual uncertainty, which can only occur after any conceptual uncertainty has been established. Still, misclassification can occur even when the conceptual uncertainty has been minimized. Conversely, underlying errors or errors made in measurement or classification propagate thru the reasoning chain and reduce the quality or value of the final result.
* While uncertainties associated with quantitative measurements or assessments can be statistically evaluated and described, uncertainties related to qualitative assessments are more problematic.
**Working through the taxonomic chain from base concept to causal explanation requires amassing and integrating qualitative information and quantitative data into input parameters for mathematical models. Developing procedures for producing consistent, repeatable sets of information from assessments that minimize conceptual error or systemic bias requires professional efforts to "validate" such methods, models, and judgments at all layers in the process. Validation as a process is something that civil engineering as a practice needs to embrace more frequently.
* '''[https://en.wikipedia.org/wiki/Sampling Sampling]'''- in the classical statistics sense, is the selection of a limited subset of a larger population to use as a basis for making estimates about the population in the form of attributes, proprieties, or parameters. The resulting information would be determinative in making forecasts about future population sampling or possessing a desired amount of predictive power. The uncertainty about the ability of this information to correctly predict future samples is termed sampling error.
* '''Modeling'''- Modeling uncertainty arises whenever a claim is made that out of a class of candidate relationships and variable pairings, one variable pairing inclusive of constants is chosen that possesses a particular or persistent mathematical relationship to another variable X. Modeling errors arise in selecting the wrong variable pair and constants or by incorrectly specifying its constants. (Walker, 1991, pg. 599)
** '''Causal'''- In causal modeling and analysis, the relationship between causal variables is not strictly a mathematical function. Still, it makes testable predictions and explains "...how a system of variables works, why a system works the way it does, or why it makes sense to think of certain variables as a system" at all." (Walker, 1991, pg. 609)
** '''Epistemic'''- the choice of interpretations for fundamental, logical concepts used throughout the other components or layers.
The components of this uncertainty taxonomy can combine in different ways to "...produce different aggregate uncertainties." '''note-36 [29]'''
:''' Only the scientific and engineering professions possess the capacity to develop and validate procedures and models that combine the different kinds of qualitative '''note-37 [30]''' and quantitative information and their associated potential for error into an aggregate measure or "scalar variables" of uncertainty.'''note-38 [31]]
Scalar variables are quantitative variables whose categories are related by some measure of the relevant property's incremental frequency, degree, or amount. (Walker, 1991) Examples are Modulus of Elasticity, Compression stress in elastic materials, etc.

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=== Economics Uncertainty Taxonomies ===
Like scientific concepts of uncertainty, economics is environment-specific (in this case, read sector of economy or markets), phenomenon-oriented (in this case, read human activity and manufactured phenomena such as firms and markets guided by human judgment and decision), and model-centric.
:''' Economics is not interested in all forms of uncertainty, only specific, meaningful subsets possessing casual connections with material impacts, often labeled as 'risks'.'''
One of its earlier writers ([https://en.wikipedia.org/wiki/Frank_Knight#Life_and_career Knight], 1921, 1948, 1957) attempted to define "uncertainty" as the presence of "defects of managerial knowledge" (or [https://en.wikipedia.org/wiki/Knightian_uncertainty knightian uncertainty] and thus risk centric) as risk that is immeasurable, not possible to calculate.(Wikipedia) Risk in this sense was defined as "the ordinary risks of business activity which can ... be reduced... by applying the insurance principle." Knight does acknowledge that risk is a specific and unambiguous subset of uncertainty. (Risk, Uncertainty and Profit, 1957, pg. 19) For Knight, measuring uncertainty meant measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences, which Knight referred to as "the imperfection of knowledge". (Risk, Uncertainty and Profit, 1957, pg. 197) One of Knight's many contributions to this analysis was recognizing information and knowledge's role in economic activity.
:"''(W)e are concerned only to emphasize the fact that '''knowledge is in a sense variable in degree and that the practical problem may relate to the degree of knowledge rather than to its presence or absence in toto.''' We live only by knowing something about the future, while the problems of life, or conduct at least, arise from knowing so little. This is as true of business as of other spheres of activity. The situation's essence is action according to opinion, of greater or less foundation and value, neither entire ignorance nor complete and perfect information, but partial knowledge. If we are to understand the workings of the economic system, we must examine the meaning and significance of uncertainty; and to this end, some inquiry into the nature and function of knowledge itself is necessary.''" (Knight, pg. 199) Emphasis added
Knight also reinforced the difficulties in classifying uncertainties when he analyzed life insurance. In this case, accidental death was an uncertain phenomenon compared to sickness and accident, where an "...objective description and classification of cases was impossible..." (Knight, pg. 248) Another factor that made uncertainties ineligible as risks and, therefore, uninsurable because they were unclassifiable was the exercise of judgment in making decisions by the businessman. (Knight, pg. 251)
This is a simple classification of economic uncertainty and, as an idealization, is philosophically controversial. '''note-39 [32]'''
* Uncertainty and information about the economic environment are distinct from uncertainty about others’ behavior or choices.
* Risk as a specific subset of uncertainty implies that "...all possible acts are known, all possible outcomes arising from each act are known, and it is possible to assign probabilities to each act." '''note-40 [33]'''
Like the economic [https://en.wikipedia.org/wiki/Theory_of_the_firm theory of the firm], an economic theory of uncertainty seeks to explain and predict the nature of a specific subset of uncertainty (in this case, risk relating to physical phenomena and human activity), including its behavior, structure, and relationship to the universal uncertainty of physical phenomena. In this sense, uncertainty, like firms and markets, can establish price equilibriums under the right circumstances different from those that might have occurred in either of the other two. Such a theory of uncertainty offers a partitioning scheme that decomposes uncertainty into distinct and meaningful subsets, of which risk is a significant one. It also explains how the presence and variability of knowledge cause or " drive" economic consequences. Unlike physical phenomena, economic activity creates artificial objects such as products, firms, mediums of exchange, markets, economies, and knowledge about those activities. The economics of uncertainty exists as an alternative to "certainty" models that assume away imperfect knowledge and unknown choice/judgment preferences when it is more efficient for economic decision-making. This also allows the introduction of the logical concept of "hazard" versus "risk". Frank used hazard extensively in Risk, Uncertainty, and Profit, but not to the extent that one could say it was interchangeable. An example of this is in the term "[https://en.wikipedia.org/wiki/Moral_hazard Moral hazard]," where a firm could engage in "riskier" and potentially "uninsurable" behavior if the information were transparent, and, therefore, insurance coverage of losses is limited. note-41 [34]], note-42 [35]]

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=== Uncertainty and Risk in Policy and Regulatory Environment ===
:''"Hume's 'just reasoner', when faced with a difficult public policy decision in current times, would probably commission a risk analysis. But how could they incorporate 'a degree of doubt, caution, and modesty?"'' '''note-43 [36]'''
The problem of "decision-making in the face of uncertainty" is how to regulate based on incomplete information that has the potential to be materially inaccurate. A contextual assumption is that we need to evaluate decision rules for dealing with uncertainty. As a social enterprise, risk regulation, whatever its substantive objectives, should be as '''effective, efficient, and equitable''' as practically possible. These "three E's" form a set of "process objectives" or "meta-goals." From the uncertainty standpoint, causal information can be usefully divided into two major categories: information about groups and information about individuals.

Each category, group, and individual has its distinctive types of inherent uncertainty. These are logically distinct, generally independent, and cumulative, contributing to '''(?????)'''

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=== Engineering Uncertainty Taxonomies ===
Like scientific and economic concepts of uncertainty, engineering is environment-specific, phenomena-oriented, model-centric, external choice, and driven by decision-making in the face of uncertainty. Like economics and unlike science, engineering is not interested in all forms of uncertainty, only those with determinable and material consequences. Like economics, engineering conceives risk as a specific and unambiguous subset of uncertainty determined by measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences. Also, engineering recognizes the role information and, by extension, knowledge plays in constructing the built environment.
'''note-44 [37]]''' Like economics, engineering differs from economics and scientific uncertainty in its interest in behavioral uncertainty.
*"Behavioral or interaction uncertainty is how individuals or organizations act or interact. Behavioral uncertainty arises from four sources: design uncertainty, requirement uncertainty, volitional uncertainty, and human errors." '''note-45 [38]]'''
** A design uncertainty is a choice among alternatives over which an individual or group of individuals exercises direct control but has not yet decided upon.
** Requirement uncertainty includes parameters of interest to and determined by the stakeholder, independent of the engineer or designer.
** Volitional uncertainty is uncertainty about what the subject him/herself will decide. Other people’s future actions and conduct are not entirely predictable, particularly when dealing with other organizations.
** Human errors occur during the development of a system or project due to blunders or mistakes by an individual or individuals.

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==Practice Frameworks for Uncertainty and Risk in Civil Engineering Practice==
===PMIBoK===
PMI does not define uncertainty, but the PMI Body of Knowledge uses the term in 45 places (uncertainty) and as describing properties in 8 places (uncertainties). [See Note 8] PMI does define "risk" and uses the term over 1,300 times in the same document. In its glossary, PMI also defines terms such as "threat" and "opportunity" but not events.[391 The BoK contextually defines uncertainty when it states that project risk has its origins in the uncertainty present in all projects. (Op. Cit., pg 309) 
'''Uncertainty''' is contextually defined and taken as having the property of affecting project execution and the ability to meet stakeholder expectations. Uncertainty is more than the sum of all individual risks (known and unknown). The nature of this uncertainty is not defined, only its capacity to affect something else or its properties. 
'''Risk''' is defined as " ... an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives." (Ibid.) Beyond this formal definition, PMI adds context when, in its risk management section (Sec. 11 ), the BoK states that risk is a caused event or condition with multiple causes and impacts. The BoK even offers specific techniques for mapping cause-and-effect relationships and influence diagrams. (Sec. 11.2.2.5) Not as evident but equally important is the recognition that not only is risk caused but the impact is "triggered". The risk trigger is an event or situation that signals that it is about to occur (Glossary, pg. 566). Risk conditions are factors that affect or otherwise contribute to risk, such as project stakeholders. Risk implicitly retains some residual element of probability in it, or a value of less than 1.0 with risk that approaches a level of near certainty is termed "issues" or "realized risk" .(Op. Cit., pg 309). PMI also links risk to underlying variations in project outcomes. (Ibid.) Risk is based upon data and information which can be assessed as to its quality (Sec. 11.3.2.3). This analysis looks to determine the value of the information and examines the degree to which the risk information is understood as well as its " ... accuracy, quality, reliability and integrity ... " (Ibid.) Part of that is understanding the relationship between occurrence and impact as outlined in PMl's Figure 11-10 Risk Impact Matrix and developing a model for prioritizing risk products and setting the threshold for action.
====Commentary:====
For defining and identifying risk, PMI notes in Sec. 11.2.2.4 that " ... Every project and its plan is conceived and developed based on a set of hypotheses, scenarios, or assumptions." Part of the risk analysis process is identifying risks to the project, such as inaccuracy, instability, inconsistency, or incompleteness of assumptions. Similarly, the quality of management plans, as well as their consistency with others and the project objectives and assumptions are 11 •• .indicators of risk in the project." (Sec. 11.2.2.1) 
Similarly, the role of engineering economics in risk is embedded or implied in the PMI definition of risk when the BoK discusses risk identification as a process of assessing which risks out of the total risk exposure for the project "may affect" the project and distilling their characteristics. Embedded in this statement is the concept that not all risks can cause economic impacts (positive and negative) to the project. Also embedded is the requirement to understand risk characteristics to identify project risk (Cost and schedule). Lastly, risk identification is an iterative process of incrementing project risk documentation, much like project scope, cost, and schedule documentation. 
Lastly, PMI acknowledges the complex constitution of risk aggregates or the "sum" of all risks in its material, but is it a sum? Risk information has descriptive and explanatory content. These are knowledge and meta-knowledge components. Understanding project assumptions is project-specific knowledge, but identifying risks from assumption inaccuracy, instability, inconsistency, or incompleteness requires professional or program meta-knowledge. 
'''Uncertainty exists in all projects, but risk, a subset of uncertainty with material economic impacts, is a key meta-knowledge element for civil engineering practice.'''
===Software Development Process- Risk Focused===
The Unified Process requires the project team to focus on addressing the most critical risks early in the project life
cycle. The deliverables of each iteration, especially in the Elaboration phase, must be selected to ensure that the greatest risks are addressed first. <ref> Booch, Grady, Ivar Jacobson, and James Rumbaugh. "The unified software development process."
Reading: Addison Wesley (1999).</ref>
===American Society of Civil Engineers (ASCE)===
ASCE does not define uncertainty, but the ASCE CE Body of Knowledge (CEBoK) uses the term in 22 places (uncertainty) and describes properties in 13 places (uncertainties). 
The CEBoK does not use the phrase "uncertainty and risk" as PMI and AACE do in their publication. ASCE reverses the two (Risk and uncertainty) in twelve places regarding technical outcomes (Outcome 12). A fundamental difference between PMI and ISO is that risk is considered a subset of uncertainty. ASCE does not define "risk" but uses the term 25 times in the same document. Almost all are in the context of variation of design parameters and not in the classical context of cost, schedule uncertainty, and risk.
====Commentary:====
...
=== American Association of Cost Engineers (AACE) ===
AACE defined uncertainty, risk, and other related terms in its Risk Management Dictionary. <ref>Risk Management Committee uRisk Management Dictionary, Cost Engineering Vol. 37/No. 10, OCTOBER 1995. </ref>
AACE's overall strategy was to define risk and other terms that stem from base uncertainty. AACE first defined uncertainty as .... "(t)he total range of events that may happen and produce risks (including both threats and opportunities) affecting a project (see opportunities, events, conditions, risk, and threats" where the following sub-definitions apply:
** Biases--A lack of objectivity based on the individual's position or perspective. Systematic and predictable relationships between a person's opinion or statement and his/her underlying knowledge or circumstances. Note: there may be "system biases" as well as "individual biases."
** Condition (Uncertain Condition)-Any specific identifiable circumstance (such as the rate of inflation or the quality of labor available) that might affect the outcome of the project
** Event (Uncertain Condition)-is a specific identifiable action (such as a large government project being started in the same labor area as your project) or an act of nature that might happen and that (if it does happen) could affect the outcome of the project.
** Opportunities are Uncertain events that could improve the results or improve the probability that the desired outcome will happen
** Threats are Uncertain events that are potentially negative or reduce the probability that the desired outcome will happen.
*** AACE defines Risk as an "ambiguous term" (sic) that is synonymous with uncertainty or a negative subset such as "threats", or an overall negative impact of all possible uncertainties.
====Commentary:====
...

==Limitations of the definition==
...
==Working definition==
...
==Beneficial outcomes==
...
==See also==
Wiki articles on the project.

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==Notes==
<references group="note" />
<li> Implicit in this definition of uncertainty as a "state of' is ... </li>
: " ... a conceptualization of uncertainty as a subjective, cognitive experience of people--a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one's lack of knowledge, without which one could not feel uncertain; '''uncertainty is a form of "meta-cognition" ... ( or alternatively, its main component, meta-knowledge) ... -a knowing about knowing.'''" (Han, 2011, op. cit., Emphasis added)
</ol>
<ol start="5">
<li> Cox wanted his system to satisfy the following conditions:</li>
: Divisibility and comparability- The plausibility of a statement is a real number and depends on the information we have related to the statement.
: Common sense - Plausibilities should vary sensibly with the assessment of plausibilities in the model.
: Consistency - If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). -preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf
</ol>
<ol start="6">
<li> The utility of existing taxonomies of uncertainty in civil engineering and, by extension, "risk" has been unsatisfactory for this very reason. This is particularly true in cases where the source of the uncertainty is conflicting versus incomplete information. See Regan, et. al.,"A taxonomy and treatment of uncertainty for ecology and conservation biology" (2002)
for a survey of scientific taxonomies for uncertainty.</li>
<li> Regan et al. argue that " ... genuine examples of this kind of uncertainty are hard to find. Even classic cases of random experiments like coin tosses and the throwing of dice are deterministic; it is just that we do not have enough information about the dynamic processes and initial conditions to make any sensible estimates about the outcomes. Such processes are, for all intents and purposes, inherently random, but they are not genuinely inherently random. For similar reasons, complex systems such as ecosystems and weather patterns are unlikely to be inherently random. Similarly, chaotic systems are entirely deterministic. They are unpredictable because the deterministic processes generating them and the relevant initial conditions are hard
to fully specify (see Stewart 1989, Sugihara et al. 1990)." (Regan (2002) </li>
<li> The CEBoK uses the phrase "uncertainty and risk" twice in reference to scheduling (Section 6.5,2) and cost estimating (Section 7.2,2) as well as reversing the two (Risk and Uncertainty) also in two places in discussing project life cycle (both in Section 2.4.1) </li>
</ol>

==References==
<references />

Uncertainty and Risk in Civil Engineering Practice

2024-11-24T16:59:28Z

WikiSysop: /* Logical Framework for Specific Risk Taxonomies */

'''Life is short and the art long; the occasion instant, experiment perilous, decision difficult.''' (Hippocrates as quoted in Fox,)
:''See also [[Project Definition]] , [[Project Life Cycle and Phase Models]] , [[Project Delivery Methods]]''
:''See also [[Technical Outcomes for Civil Engineering:Risk and Uncertainty]]''
==Basic Considerations==
As human beings,'' ... being alive means seeking opportunities and taking risks.'' As knowledge professionals
living in the 21st century, this means coping with an increasingly complex number of uncertainties for humans
living in this environment. We seek to understand better how these uncertainties can be characterized and
managed. The essence of this article and the experience for engineers in general and civil engineers in particular is
that manageable uncertainty is, by definition, termed risk, and its kindred cousin is termed hazard. This causes us to experience the
:" ''... human dread of and fascination for risk and the increasingly important role of risk analysis within societies ...'' " <ref name="McDaniels"> McDaniels, Timothy, and Mitchell Small. Risk analysis and society: an interdisciplinary characterization of the field. Cambridge University Press, 2004, page 1 </ref> (Ibid.)
and, by extension, civil engineering. McDaniels et al. argue that risk management has been
fundamental to our social and governance development for the past 10,000 years. The scale and shape of the
uncertainties faced in this period shaped the societies that have developed today. The central thrust of this effort
over the centuries has been to reshape and re-frame our understanding and conception of uncertainty from one of
complete unknowing and simple acceptance as our fate in life to one of management (cf "''Against the Gods''"
concept in Bernstein's book <ref name = Bernstein">Bernstein, Peter L., and Jesse Boggs. Against the gods. Simon & Schuster 1997.</ref>.
: ''All the knowledge professions and disciplines have struggled with managing uncertainty, for it is impossible to manage unbounded uncertainty.'' <ref group="note">See McDaniels et al. 2004 for an extensive discussion and bibliography on the historical development of Risk Analysis and some key milestones in risk analysis in the 20th century.</ref>
As outlined below, professions such as civil engineering have successfully acted in the face of uncertainty. It lies in the profession's ability to reduce a wide variety of uncertainties into increasingly smaller and crucially bounded subsets that can be managed. These are called 'risks. More recently, this process has evolved, and individual disciplines such as Civil Engineering (CE) have developed their knowledge and models to perform risk analysis. Risk is also taught as a distinct discipline and specialty practice within civil engineering. Still, it has some unique features that set it apart from other more classical practices within CE. As such, it is one of the first of some very specialized CE practices that use knowledge about civil engineering knowledge, or meta-knowledge. Other examples of civil engineering metaknowledge are project controls and quality controls.
==Semantic, Epistemic and Logical frameworks==
The semantic, epistemic, and logical frameworks for uncertainty and risk have several dimensions and layers of logical frameworks. The semantics problems include interchangeable usages for risk and uncertainty and hazard, uncertain and imperfect, and a lack of definitional material or context. The semantical scheme for this article will be to proceed from the distinction between certainty and uncertainty through marginal refinements and reductions of uncertainty up to the point of causal uncertainties. Beyond this point of semantics will be the logic frameworks or the subject of taxonomies of professional knowledge. First, it is about simple, testable phenomena, and then, it moves on to complex taxonomy schemes for artificially constructed phenomena or engineering projects.
<ref group="note">TBD</ref>
==Semantics framework==
===Certainty and Uncertainty===
''''' There is no such thing as absolute certainty, but there is assurance sufficient for the purposes of human life.''''' ([https://en.wikipedia.org/wiki/John_Stuart_Mill John Stuart Mill]) 
'''''If you tried to doubt everything, you would not get as far as doubting anything. The game of doubting itself presupposes certainty.''''' ([https://en.wikipedia.org/wiki/Ludwig_Wittgenstein Wittgenstein] # 115 from On [https://en.wikipedia.org/wiki/On_Certainty Certainty]) 
It is important to note that the references to epistemic or epidemiological knowledge in this article are assumed to relate to three forms of knowledge, namely:
* Knowledge that ( [https://en.wikipedia.org/wiki/Descriptive_knowledge descriptive or declarative or propositional knowledge])
* Knowledge how ( or "[https://en.wikipedia.org/wiki/Procedural_knowledge know-how]"), and
* [https://en.wikipedia.org/wiki/Knowledge_by_acquaintance Knowledge by acquaintance].
'''Certainty''' has been defined as "an epistemic property" of knowledge in all its forms and the state of our beliefs about that knowledge.
<ref>Certainty, Stanford Encyclopedia of Philosophy, accessed on September 12, 2015, at http://plato.stanford.edu/entries/certainty/#ConCer </ref>
Certainty about any belief about knowledge implies that it is not subject to doubt or skepticism. This immunity to criticism can be dogmatically based, emphasizing the importance of a propositional-based sense of truth over experiential, sensory perceptions. <ref>Definition of [https://en.wikipedia.org/wiki/Dogmatic_theology Dogmatic theology or belief], accessed at Wikipedia</ref>
: '''The demarcation line between dogmatic and non-dogmatic beliefs lies in the presence and recognition of specific criteria and information that would make the believer change their beliefs.'''
An empirical framework of [https://en.wikipedia.org/wiki/Testability testability] and [https://en.wikipedia.org/wiki/Falsifiability falsification] is required to recognize this and limit dogmatism. For example, one could argue that people would change their minds if God asked them to. Similarly, one could construct a concept of epistemic as opposed to dogmatic belief certainty as the ability to know anything that one chooses to know and can be known or [https://en.wikipedia.org/wiki/Omniscience inherent omniscience].

A second kind of certainty is epistemic, when conviction reflects the highest possible support for a belief. In this sense, knowledge is separate from beliefs, although someone may have beliefs about a property, such as certainty of knowledge. Logically, it has been shown that there will be unprovable statements within the system for any such knowledge system. Secondly, the knowledge system cannot demonstrate its own consistency. ([https://en.wikipedia.org/wiki/Gödel%27s_incompleteness_theorems Gödel's incompleteness theorems])
: '''Certainty, in real life, is useless or often damaging (the idea is that "total security from error" is impossible in practice, and a complete "lack of doubt" is undesirable)''' ([[https://en.wikipedia.org/wiki/Certainty Physicist Carlo Rovelli]])
'''Uncertainty''', on the other hand, arises immediately in the slightest amount of doubt or criticism.
<ref group="note"> The Merriam-Webster dictionary defines uncertainty as “the quality or state of being uncertain,” which is something of a circular definition. Likewise, one could define uncertainty as the state of not being certain.

Synonyms are distrust, doubt, misgiving, mistrust, reservation, skepticism, and suspicion. Another writer added indefinite, indeterminate, not certain to occur, problematical, unreliable, untrustworthy, unknown beyond doubt, dubious, doubtful, not clearly identified or defined, not constant, variable, and fitful to the list.
:(Han, Paul KJ, William MP Klein, and Neeraj K. Arora. "Varieties of Uncertainty in Health Care: A Conceptual Taxonomy." Medical Decision Making 31.6 (2011): 828-838.)
Han et al. noted that any definition of uncertainty clearly encompasses "...numerous types, sources, and manifestations of uncertainty, and ...(any) ...useful working definition of uncertainty needs to specify the concept underlying these varied meanings of the term."</ref>
Implicit in this definition of uncertainty as a “state of” is ...
"...a conceptualization of uncertainty as a subjective, cognitive experience of people—a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one’s lack of knowledge, without which one could not feel uncertain; uncertainty is a form of “meta-cognition” ...(or its main component, meta-knowledge)... —a knowing about knowing." (Han, 2011, op. cit., Emphasis added)
Similarly, uncertainty could be defined as "...any departure from the unachievable goal of complete [https://en.wikipedia.org/wiki/Determinism determinism]."
<ref name="Walker1">Walker, W. E., Harremoës, P., Rotmans, J., Van Der Sluijs, J. P., Van Asselt, M. B., Janssen, P., & Krayer von Krauss, M. P. (2003). Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support. Integrated assessment, 4(1), 5-17.</ref>
Reasoning under uncertainty is very different than performing the same under certainty. In reasoning under certainty, one has complete knowledge and deduces without doubt and equally important, without limitation, thereby concluding free from error. Reasoning under uncertainty, one works in a state of incomplete, inconsistent, and limited knowledge; doubts cloud any statements or assertions and thereby taint any deductions/inferences resulting in the potential for error. <ref group="note">Implicit in this definition of uncertainty as a “state of” is ...
"...a conceptualization of uncertainty as a subjective, cognitive experience of people—a state of mind rather than a feature of the objective world. The defining feature of this state, furthermore, appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one’s lack of knowledge, without which one could not feel uncertain; uncertainty is a form of “meta-cognition” ...(or alternatively, its main component, meta-knowledge)... —a knowing about knowing." (Han, 2011, op. cit., Emphasis added)</ref>
:'''Knowledge professions reason in a state of incomplete, inconsistent and limited knowledge with doubts that cloud any statements or assertions and taint any deductions/inferences resulting in the potential for error.'''
:''' It is also important to note the semantical and logical correlation between 'reasoning under uncertainty' or 'acting in the face of uncertainty' with the 'potential for' or 'presence of' error. The presence of uncertainty is invariably linked to the potential presence of error.''' The [https://en.wikipedia.org/wiki/Contingency_(philosophy) contingent] nature of uncertainty logically implies the contingent nature of error.
:'''Knowledge Professions use error as a proxy for uncertainty such that within discipline knowledge frameworks, uncertainty can be managed.''' The rationale for this belief is that error can be reliably described, quantified, explained, and ultimately reduced in ways that uncertainty cannot.

'''Uncertainty''' implies a range of variation that is impossible in certainty. Concepts and definitions of uncertainty are unbounded and vast but start from the point of complete and total ignorance. In describing uncertainty, statements can range from complete ignorance to relatively high degrees of belief that there is less potential for error in our reasoning or action. The latter is based upon confidence in justified knowledge, favorable past experience, and reliable parameters/models. One can't make that statement about ranges in certainty. If certainty, by definition, precludes doubt of any type or nature, then how could that be graduated to any degree? 
Uncertainty, on the other hand, can be reduced through disciplined efforts to acquire knowledge. In short, we can reduce or even manage uncertainty (to a degree); how can certainty be improved? Are there higher degrees of perfection? The answer is no. We reason from an uncertain starting point. Yet this effort presumes, as Mill and Wittgenstein postulated, that we can develop or acquire an increasing degree of belief or conviction that there is less potential for error in our reasoning or action. This position assumes that some way exists to identify what a lesser degree of uncertainty would look like.
:'''Knowledge professions acquire an increasing belief or conviction that there is less potential for error in their reasoning or action based on justified knowledge, favorable past experience, and reliable parameters/models.'''
Uncertainty is, in some form, amenable to description and explanation through observation and analysis. It is explainable and predictable to such a degree that it interests professionals such as scientists and engineers. Professional knowledge of this type has explanatory or predictive power, albeit limited in scope and application to relevant subjects such as physical objects or phenomena and functionality.
:'''The very act of reducing the scope of uncertainty to a limited set of phenomena implies choice and, ultimately, the decision to act in the face of such uncertainty, but acting or judging in this manner adds more value than doing the same under relative ignorance.'''
'''Uncertainty''', as described above, implies a hybrid nature. Examples of this are economic or decision-theoretic applications. In economics, [https://en.wikipedia.org/wiki/Perfect_information perfect information] allows one within the limiting framework of perfect competition in economic games to make decisions with 'perfect knowledge.' The crucial difference here is the distinction between '[https://en.wikipedia.org/wiki/Perfection perfection],' broadly, a state of completeness and lawlessness, and 'perfect, which can be thought of as an analytical limit that is approachable but can never be attained. Arbitrarily delimiting the realm of knowledge into defined, finite boundaries allows the simulated production of perfect choice, assumed crucially, to be free from error. The hybrid nature of this form of 'simulated' certainty produced within boundaries defined by assumptions and, therefore, clouded by doubt is itself a form of uncertainty. The importance of this is the semantical and logical association that exists between certainty and perfection versus the hybrid concept of a perfect anything, whether knowledge, infraction, competition, etc., or in the case of engineering, elastic, permeable, conductive, etc., being contained within an admittedly imperfect matrix of uncertainty.
:'''Simply put, a finite, heavily bounded piece of uncertain knowledge can be improved through the simulated use of [https://www.merriam-webster.com/dictionary/assumed assumedly] perfect information, but the perfection of knowledge cannot be attained or simulated.'''
Engineering and Economics, for example, simulate finite elements of perfect knowledge or information, heavily bounded with assumptions and a range of applications. By doing so, these professions reduce the bounds and variability of uncertainty and thereby manage it. Using these hybrid models to acquire knowledge takes place in an economy (regulated or institutional) and environment (transparent and structured), which puts pressure on the various professions to recognize and use qualitative and quantitative knowledge better to reduce uncertainties in their activities. The challenges in this reflect the nature of the profession's or discipline's knowledge (e.g., scientific, engineering, or medical), the policies and structure of the relevant professional knowledge bodies, process objectives, constraints, and "the elusive demands of politics." <ref name="siren">Walker, Vern R. "The siren songs of science: toward a taxonomy of scientific uncertainty for decision-makers." Conn. L. Rev. 23 (1990): 567.</ref>
One common theme runs through all of these efforts to address the presence of uncertainty in professional activities. 
:"The available scientific information upon which a decision must be made is almost always a mixture of engineering knowledge and uncertainty. Regardless of the information or methodology, there is the potential for error. This is true regardless of the relevant science, whether archaeology or aeronautics, economics or engineering, pharmacology or toxicology or epidemiology. Achieving the best social decisions requires not only understanding and using what we know but also appreciating and weighing the extent of our uncertainty. In making the best use of ... information in ... decision-making, it is still true that the beginning of wisdom is knowing what it is we do not know." (Vern, Op. cit., Reformatted and engineering substituted for scientific) 
'''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty to develop explanations, calculate, and make predictions. This professional knowledge converges on but never reaches certainty, producing a "legitimacy" from the fruitfulness of its use.''' <ref>Randomness Is Unpredictability, Antony Eagle, The British Journal for the Philosophy of Science, Vol. 56, No. 4 (Dec., 2005), pp. 749-790 </ref> 
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=== Bias and Uncertainty ===
Up to this point, the concept of error, as outlined in uncertainty, could easily be simulated by a truly random variable. There should be no detectable differences around the unseen or specified statistical mean. However, several instances in experience point to a tendency towards one extreme or a pattern of error, a collective that is in itself an error of errors, namely [https://en.wikipedia.org/wiki/Bias bias].

Arguably, this could be part of the choice uncertainty discussion below, but it makes more sense to be looked at on its own. Acting in the face of uncertainty is an exercise of personal knowledge or experience. Whether using professional knowledge or individual experience, the potential is there to make a series of choices that, in some instances, make errors more likely than they would be if considered in the context of a statistical analysis.

This tendency for an individual to make the same error in a predictable or repeatable manner is termed bias. Personal experience, beliefs, knowledge, data, and models all have inherent built-in factors, errors, or defects that create predictable error patterns when applied in decision-making. Walker (1998) argues implicitly that such bias can also be considered systemic error. This type of uncertainty is difficult to identify except on theoretical grounds when the magnitude and direction of the bias are known. Most of these biases, whether systemic or knowledge-based, individual or practice-based, require disciplined efforts to "validate" such methods, models, and judgments at all layers while bounding the uncertainty.
: This is the challenge to professional knowledge that must be overcome, namely, developing and validating models and methods for managing bounded uncertainty that minimize this tendency towards repeatable groups of error or bias. Therefore, from this point forward, the discussion will refer to 'uncertainty and its proxies, error, and bias.'
===Propagating Error and Uncertainty===
Partitioning uncertainty and its proxies, error, and bias into logically distinct subsets involves or is associated with multiple-step execution or choice frameworks. Combining those measurements or choices into single parameters or choices involves, in some cases, assembling the data/information in a unique or limited range of sequences. This implies that associated errors in observing, calculating, or choosing will have unequal impacts or consequences depending on where in the chain the error occurs. Also implied in this is the possibility that subsequent errors will be causally linked to some degree. (In statistical practice, this depends upon whether the errors are independent of each other or correlated, specifically, co-variant.)
:'''Professions develop knowledge of how uncertainty and its proxies, error, and bias propagate through an ordered and normative series of observations or choices. This allows the profession to prioritize error and bias reduction to achieve an optimized reduction of uncertainty.'''
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===Cognition, Metacognition and Uncertainty===
The concept of choice and the resultant error in uncertainty can be further delimited depending on the role of [https://en.wikipedia.org/wiki/Cognition cognition], [https://en.wikipedia.org/wiki/Metacognition meta-cognition], and independent external elements. The rationale for this argument is that such choice activity in the face of uncertainty is a conscious, cognitive act.

An argument could be made that such cognition is a prerequisite for exercising choice and introducing error, as presented and discussed above. The counterfactual to this argument is the example of uninformed choice. Choices introduce as much as errors or more as informed choices could be made. (???) Choice, in this sense, is indifferent to knowledge. Purposeful choice in an environment of objectives requires knowledge, method, and experience, but uninformed, speculative choice does not.
:"''' Cognition''' is the set of all mental abilities and processes related to knowledge, attention, memory and working memory, judgment and evaluation, reasoning and "computation," problem-solving and decision making, comprehension and production of language, etc. Human cognition is conscious and unconscious, concrete or abstract, as well as intuitive (like knowledge of a language) and conceptual (like a model of a language). Cognitive processes use existing knowledge and generate new knowledge." (Wikipedia)
:"'''Metacognition''' is "cognition about cognition", or "knowing about knowing" and can take many forms. It includes knowledge about when and how to use particular strategies for learning or problem-solving. There are generally two aspects of [https://en.wikipedia.org/wiki/Metacognition metacognition]: knowledge about cognition and regulation of cognition."
Some types of metacognition knowledge are:
:'''Person knowledge''' (declarative knowledge), which is understanding one's own capabilities.
:'''Task knowledge''' (procedural knowledge), which is how one perceives the difficulty of a task, which is the content, length, and type of assignment.
:'''Strategic knowledge''' (conditional knowledge) is one's capability to use strategies to learn information. (Accessed at Wikipedia)
Like metacognitive knowledge, metacognitive regulation or "cognitive control" contains three essential skills.
:'''Planning:''' refers to the appropriate selection of strategies and the correct allocation of resources that affect task performance.
:'''Monitoring:''' refers to one's awareness of comprehension and task performance.
:'''Evaluating:''' refers to appraising the final product of a task and the efficiency at which the task was performed. This can include re-evaluating strategies that were used. (Accessed at Wikipedia)
Nothing has been said up to this point that limits this structure to an individual, or a collective, or two entities making cognitive choices that are incompatible or inconsistent to varying degrees. Interpretations of this scheme include an individual professional exercising judgment in a decision that, in turn, relies upon the collective judgment and decision of the discipline as an underlying basis versus the decision of another individual to oppose/protest or otherwise contest that decision. Thus, the framework of cognitive/meta-cognitive applies to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, economics of regulatory practice, and game theory.
:By solving useful problems in the face of inherent uncertainty, discipline-specific frameworks of cognitive/meta-cognitive activity apply to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, the economics of regulatory practice, and game theory.
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===Probability and Uncertainty===
Probability is a [https://en.wikipedia.org/wiki/Coherence_theory_of_truth coherent] approach to uncertainty in the physical and mathematics or "classical" domains such as engineering. <ref>See Colyvan for a critique of this claim, Colyvan Mark. "Is probability the only coherent approach to uncertainty?." Risk Analysis 28.3 (2008): 645-652.</ref> and is the "...is certainly the best-known and most widely used formalism for quantifying uncertainty.<ref>Morgan, Millett Granger, Max Henrion, and Mitchell Small. Uncertainty: a guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge University Press, 1992.</ref> [https://en.wikipedia.org/wiki/Cox%27s_theorem Cox’s theorem] ("Any measure of belief is [https://en.wikipedia.org/wiki/Isomorphism isomorphic] (but not necessarily equal) to a probability measure") is a well-known argument for the validity of that argument.
<ref group="note">Cox wanted his system to satisfy the following conditions: 
Divisibility and comparability – The plausibility of a statement is a real number and is dependent on the information we have related to the statement.
:Common sense – Plausibilities should vary sensibly with the assessment of plausibilities in the model.
:Consistency – If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). — preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf</ref>

Another writer, [https://en.wikipedia.org/wiki/Frank_Knight Knight] (1921,1956), presented the following taxonomy of probabilities:
<ref>As presented and discussed in Runde, Jochen. "Clarifying Frank Knight's discussion of the meaning of risk and uncertainty." Cambridge Journal of Economics 22.5 (1998): 539-546.</ref>
: '''Classical 'a priori' probability:''' As an idealized model, numerical probabilities are computed based on generalized principles of assigning equal likelihoods and mutually exhaustive possible outcomes (Runde, 1998) to all set members. Such probabilities are assigned to "...outcomes based on a judgment of indifference between those outcomes, that is, based on the absence of any evidence of real influences in play that may render any one outcome more or less probable than any other." (Runde, op. cit.) This builds upon Knights's concept of an 'absolutely homogeneous classification of instances that are completely identical except for [https://en.wikipedia.org/wiki/Indeterminate_(variable) indeterminate] factors. This judgment of probability or logical probability is on the same logical plane as the propositions of mathematics and ultimately [https://en.wikipedia.org/wiki/Inductivism inductions] from experience.'(Knight, 1956, pg.225)
: '''Bayesian 'a priori' probability:''' In contrast to interpreting probability as the "frequency" or "propensity" of some phenomenon, [https://en.wikipedia.org/wiki/Bayesian_probability Bayesian probability] is a quantity that we assign to represent a state of knowledge or a state of belief. In this view, probability is assigned to a hypothesis, often using the basis of past or prior experience. In contrast, under the frequentist view, a hypothesis is typically tested without being assigned a probability based on prior experience. The Bayesian interpretation of probability can be seen as an extension of propositional logic that enables reasoning with hypotheses, i.e., propositions whose truth or falsity is uncertain. Bayesian probability belongs to the category of evidential probabilities; to evaluate the probability of a hypothesis, the Bayesian probabilist specifies some prior probability, which is then updated in the light of new, relevant data (evidence). The Bayesian interpretation provides a standard set of procedures and formulae to perform this calculation. <ref>See also Larvor, B. "After Popper, Kuhn, and Feyerabend: Recent Issues in Theories of Scientific Method." Metascience (2002).</ref>
: '''Statistical or 'a posterior probability:''' Empirical evaluation of the frequency of association between predicates, not analyzable into varying combinations of equally probable or logical, 'a priori' alternatives. Knight argued that any "high degree of confidence" that the judgment that experience will remain valid for future predictions is "...still based on an 'a priori" judgment of indeterminateness." (Knight, Op. Cit.) Knight's argument to support this is what will be discussed further below that first, "...the impossibility of eliminating all factors not really indeterminate; and, second, the impossibility of enumerating the equally probable alternatives involved and determining their mode of combination so as to evaluate the probability by a priori calculation." (Knight, Op. Cit.) Knight noted that the main difference between this form and that of logical or inductive probability was the presence of empirical information, which allowed the analyst to identify propensity and direction in the observed phenomena. In this case, 'a priori' probability values may be derived from first principles and "statistical probabilities are determined a posteriori by the empirical method of counting instances." (Runde [1998] quoting Knight, op. cit.)
:'''Estimates:''' The distinction here is that there is no valid basis ('a priori' or 'a posteriori') for any classifying phenomena. For Knight, this form of probability presented the greatest logical difficulties of all ... but its distinction from the other types must be emphasized, and some of its complicated relations indicated..." (pp. 224-5, emphasis in the original)

Knight's concept of probability can be viewed as "..a continuum of probability situations, depending on the degree of homogeneity of the 'instances' in question" <ref>Runde, Jochen. "Clarifying Frank Knight's discussion of the meaning of risk and uncertainty." Cambridge Journal of Economics 22.5 (1998): 539-546.</ref>; i.e. going from a logically distinct but equally likely set of elements to a unique set with one member with statistical frequency in the middle. Knight's main motivation for distinguishing between a priori probability and statistical probability underscores his opinion that "(i) the 'mathematical or a priori type of probability is practically never met with in business, while the second is extremely common'; and (ii) 'the statistical treatment never gives closely accurate quantitative results' (Runde quoting Knight pp. 215-16). Runde argues that Knight couldn't conceive of (empirically tabulated) occurrences that are used in daily commerce classes of instances that we have to make do within the course of everyday economic life could be divided into subclasses of instances that are sufficiently homogeneous to permit the determination of what he calls 'real' probability (p. 217).
:'''In modeling degrees of uncertainty, any measure of belief is isomorphic but not necessarily equal to a probability or statistics measure'''.
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===Explanation, Prediction and Uncertainty===
It should be clear that the general notion of uncertainty synonymous with complete ignorance must be bounded as part of a scheme to produce a useful concept of uncertainty in professional practice such as civil engineering.
:The first principle that must be introduced is that such uncertainty must be bounded by reliable and valuable knowledge gained from past experience combined with analytical and computational capabilities to address an immediate and real problem of interest. This could be viewed as the economic interest argument. Only uncertainties associated with physical phenomena and economic scarcity will be addressed.
:The second principle is that the knowledge model is capable of producing statements or assertions in the form of hypotheses that themselves are testable. [https://en.wikipedia.org/wiki/Testability Testability], in this sense, is defined as the property applying to an empirical hypothesis and involves two components:
::The logical property that is variously described as contingency, defeasibility, or falsifiability, which means that counterexamples to the hypothesis are logically possible. Contrast this to a [https://en.wikipedia.org/wiki/Tautology_(logic) logical tautology], which is always true that is true in every possible interpretation. The practical feasibility of observing a reproducible series of such counterexamples if they do exist. In short, a hypothesis is testable if there is some real, non-zero expectation of deciding whether it is true or false of verifiable, reproducible experience. Upon this property of its constituent hypotheses rests the ability to decide whether a theory can be supported or falsified by actual experience data. (Source Wikipedia.)
'''Knowledge models must be capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria described above in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error and bias.'''
===Variability and Aleatory Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge referred to as epistemic uncertainty. Acting in the face of epistemic uncertainty introduces choice uncertainty (discussed below) and results in the production of explanations and predictions. It is implied that there will be testable hypotheses in the form of outcomes of interest to the knowledge professions. Inherent in nature is a variation of results, which may appear to uncertain but isn't. Observations of phenomena will not be the same, and part of the experimental process is to narrow those results down to within an acceptable level of tolerance. Statistical measures seek to identify underlying and unobserved measures of central tendency. These parameters are not directly observed but are derived to a degree of confidence.
In some cases, variation around a mean or within a statistical range may be perfectly acceptable to the knowledge profession. Examples of this are in the physical sciences and medicine. In Engineering and, to a lesser extent, economics, the process or phenomenon may be required to be more controlled to a narrower range than what a natural or "unmanaged" amount of variation would allow. Several courses of action present themselves.
:The first would be to ignore the aleatory phenomenon and choice, which is rejected outright.
:The second would be to assume that, in effect, such variation is itself a form of minimally managed uncertainty and treat it the same as epistemic uncertainty. This is not a desirable outcome as reducing Aleatory error and bias requires
:A third approach would be to acknowledge that process variation is, in some forms, manageable. This means making choices and acting in a way that presumes that the natural occurrence of error can be reduced, or more importantly, bias can be reduced. There is ample precedent for the third approach in civil engineering design and project management. In this sense, the argument is made that the aleatory response to managed efforts results in an elastic variation range. Presumably, an effective management effort either "shifts" the central tendency of the process results or reduces the variation range of occurrence or some other population parameter. Choosing to act and accept that narrower range rather than the broader natural variation may result in an outcome that, while well within the bounds of the expected variation, is outside the arbitrary control limits established by the choice and action. While this phenomenon mimics the broader uncertainty and its proxy error, it is not an error. The information on the variation was known, and the ability to produce narrower results was an error, albeit a pseudo-error, when compared to the broader classes of uncertainty discussed above, such as total ignorance.
::Looking at uncertainty using this third approach means that the error in choosing a new target parameter for this process may contain epistemic errors (our knowledge of the phenomenon may be erroneous, or the model flawed), aleatory uncertainties as discussed, or, more importantly, choice error or bias in thinking that we can move the process results to meet the requirements.
:'''In civil engineering practice, this may mean applying professional judgment in developing partitioning schemes or allocating uncertainty between the three dimensions (Epistemic, Aleatory, and Behavioral) of uncertainty.'''
Lastly, raising the topic like this brings the questions of effectiveness and efficiency to the fore. Advancing knowledge models that reduce error and bias at first are largely choice models resulting in justifications and favorable experiences. At some point in the process, the knowledge profession advances explanations and predictions that require reliable mathematical or logical concepts that are reduced to models and parameters. The question of efficiency becomes important; the broader uncertainty may have been reduced, but the process is economically inefficient. The profession is urged to become more frugal or, for example, reduce the variability of the outcomes. Knowledge professions often operate under conditions that require them to acquire knowledge and reduce uncertainty effectively and efficiently. Doing so requires understanding both epistemic uncertainty, as discussed above, and statistical process variation or aleatory variation.
:'''Knowledge professions manage and reduce uncertainty in the form of error and bias in an economically effective and efficient manner. This effort requires understanding both epistemic and aleatory uncertainty.'''
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===Choice and Behavioral Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge, referred to as epistemic uncertainty, as well as the inherent variations in results, or what was termed aleatory uncertainty. Acting in the face of that epistemic and aleatory uncertainty introduces yet another uncertainty into the process, namely choice uncertainty. This uncertainty reflects uncertainties associated with first-person and third-person choice. This is particularly relevant for engineering with project stakeholders.
===Summary of Semantics Framework for Uncertainty===
Professional disciplines have gradually re-framed their understanding of uncertainty and, thru experience and analysis, have arrived at the following meta-knowledge concepts of uncertainty:
* '''Uncertainty and its proxy, error, have three components: [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic epistemic uncertainty], [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic aleatory uncertainty], and behavioral uncertainty.'''
* '''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty using non-dogmatic beliefs, which recognize the existence of specific criteria and information that would make the believer change their beliefs using coherent approaches such as probability.'''
* '''Knowledge professions develop knowledge of how uncertainty and its proxy, error, propagate through an ordered and normative series of observations or choices that meet testability criteria in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error. An error can then be described, explained, and reduced in ways that uncertainty cannot. This allows the professions to prioritize error reduction to achieve an optimized reduction of uncertainty and produce a "legitimacy" from the fruitfulness of its use.'''
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===Uncertainty versus Risk/Hazard===
Up to this point, nothing has been said about risk or hazard. Now, risk can be defined. Simply put risk or hazard are finite, logically distinct subsets of the broader uncertainty. Risk and hazard are currently defined interchangeably. First and foremost, risk is a specific subset of general uncertainty. Any arbitrary subset of general uncertainty can be defined as a risk if it meets the following criteria:
* Uncertainty is associated with the potential for material error when acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena.
* Uncertainty and its proxy, error, can be partitioned into logically distinct subsets associated with multiple-step execution or choice frameworks in physical environments linked to a unique or limited range of sequences.
**Subcriteria 1:Resulting errors will have varying impacts or consequences depending on where the error occurs and the degree of causal linkage or influence in the chain.
**Subcriteria 2:Potential errors can be sequenced and prioritized to achieve an optimized reduction of error or bias and, by proxy, uncertainty over time.
* Uncertainty and its proxies, error bias, and environments are isomorphic to coherent approaches such as probability.
* Uncertainty and its proxies, error, and bias are isomorphic to coherent approaches such as knowledge models capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria in 'reasoning under uncertainty.'
This is a definition of general risk. There is no differentiation for engineering or economics, medicine or insurance. There is no difference between a 'good' risk consequence and a 'negative' one. Redefining general risk into simpler terms gives the following:
:'''General Risk is a subset of general uncertainty that is associated with the potential for material errors and biases when sequentially acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena; isomorphic to probability measures and methods and capable of testability.'''
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==Logical Framework for Specific Risk Taxonomies==
As noted above, at the most fundamental level, uncertainty is ".....the subjective perception of ignorance."
<ref name="Han">Han, Paul KJ, William MP Klein, and Neeraj K. Arora. "Varieties of Uncertainty in Health Care A Conceptual Taxonomy." Medical Decision Making 31.6 (2011): 828-838.</ref>
At this point in the discussion, the focus has moved from discussing uncertainty to its framed subset, risk.
:'''Further, the discussion moves from general risk to a professional or discipline-specific risk [https://en.wikipedia.org/wiki/Taxonomy taxonomy]. Beyond this point, risk will be used in place of uncertainty.'''
Han et al. note that, in general, "(t) taxonomies are valuable not only in their comprehensiveness but in their coherent reduction of uncertainty to conceptually discrete elements."
Uncertainty (and, by implication, risk or hazard) taxonomies offer an approach or tool to more precisely identify and define ontological uncertainty so that it can be quantified, analyzed, and communicated. Viewed this way, uncertainty is not a single, monolithic phenomenon but "...multi-dimensional with theoretically distinct domains and constructs that are potentially measurable and related to different outcomes, mechanisms of action, and management strategies." <ref name="Han"/> Taxonomic schemes for classifying the different kinds of scientific and, by extension, engineering uncertainty require identifying the various kinds of potential error associated with descriptive scientific or engineering information and knowledge. An example of this is the following quote from a legal authority:
:"I would especially stress the need for an agency to disclose the uncertainty that surrounds its determinations. ''' And by uncertainty, I mean the agency's ignorance as well as its quantitative estimates of error."''' (Emphasis added)
<ref>Walker citing Bazelon, Science, and Uncertainty: A Jurist's View, 5 HARV. ENVTLt L. REv. 209, 212 (1981).</ref>
''' Given the necessity of acting in the face of enormous uncertainties
<ref>Vern citing Ruckelshaus, Science. Risk, and Public Policy, 221 SCIENCE 1026, 1027 (1983)</ref>,
discipline taxonomies must offer a clearly coherent and probabilistic approach to uncertainty that is sufficiently general in nature, logically distinct and exhaustive in scope and "...provide decision-makers with a foundation for understanding the nature of discipline information..."''' <ref name="siren"/>
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=== Scientific Risk Taxonomies ===
The [https://en.wikipedia.org/wiki/Scientific_method scientific method] is a body of techniques for investigating phenomena in its environment, acquiring new knowledge, or correcting and integrating previous knowledge. Inherent in that process are uncertainties of many forms. Any one taxonomy of [https://en.wikipedia.org/wiki/Uncertainty scientific uncertainty] has largely been focused on statistical models used to assess and quantify sampling, errors, and parameters, although several different taxonomies are conceivable: note-24 [18]
====Environment-specific, Phenomena oriented, model-centric taxonomy of uncertainties====
** [https://en.wikipedia.org/wiki/Uncertainty_quantification#Sources_of_uncertainty '''Parameter uncertainty'''] the source of which is model parameters that are inputs to the computer model (mathematical model) but whose exact values are unknown and cannot be controlled, or whose values cannot be exactly inferred by statistical methods. Subsets of this type of uncertainty include but are not limited to:
***'''Experimental uncertainty''' is also known as observation error, which comes from the variability of experimental measurements. An example is repeating an experiment measurement several times using exactly the same settings for all inputs/variables and recording the variability. It is a subset of the larger uncertainty component, parameter uncertainty.
*** '''Parametric variability''' comes from the variability of the input variables of the phenomena model and is also a subset of the larger uncertainty component, parameter uncertainty.
** '''Structural uncertainty''', or model inadequacy, model [https://en.wikipedia.org/wiki/bias bias] or "[https://en.wikipedia.org/wiki/Systemic_bias systemic bias]", or model discrepancy, which comes from the lack of knowledge of the underlying true state of the phenomenon or its environment. It depends on how accurately a mathematical model describes the true state of the phenomenon or what is termed "model fitness". Due to the inherent nature of uncertainty in any knowledge body (scientific or engineering), models are only an approximation to reality. Therefore, any conclusions drawn from the model can be very misleading when the underlying basis is not plausible or lacks validity. '''note-25 [19]]'''
*** What is missing in this context is any concept of output variability due to the model itself. Part of this is due to the lack of recognition of "process" in scientific investigation. The concept of the scientific method started with a single investigator, such as Galileo or Michael Faraday, working in their laboratories. It has grown into planet and solar system scale experiments such as data gathered on planetary flybys such as Mars, Pluto, and Ceres to the hunt for the Higgs Boson. Ultimately, science has become more like engineering in the scale and complexity of its investigations and theory aggregates, such as the "Unified theory" of particle physics. Subsets of this type of uncertainty include but are not limited to:
*** '''Algorithmic uncertainty''', or numerical uncertainty, comes from numerical errors and numerical approximations per implementation of the computer model. Most models are too complicated to solve exactly. For example, the [https://en.wikipedia.org/wiki/Finite_element_method finite element method] or [https://en.wikipedia.org/wiki/Finite_difference_method finite difference method] [may be used to approximate a solution [https://en.wikipedia.org/wiki/Partial_differential_equation partial differential equation], which introduces numerical errors. Other examples are numerical integration and infinite sum truncation, which are necessary approximations in numerical implementation.
*** '''Interpolation uncertainty''' comes from a lack of available data collected from computer model simulations or experimental measurements. For other input settings that don't have simulation data or experimental measurements, one must interpolate or extrapolate to predict the corresponding responses.
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====Commentary====
A more comprehensive taxonomy of uncertainties that acknowledges knowledge, data, and linguistic uncertainties is:
*'''Epistemic uncertainty''' -[https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Epistemic uncertainty] or systematic uncertainty, in contrast, reflects limitations in the current “state of knowledge” underlying models themselves, originates from competing theories or models, is not readily quantifiable, and is manifest by subjective confusion or indecision. It includes uncertainty due to measurement limitations, insufficient data, extrapolations and interpolations, and variability over time and space. Epistemic uncertainty is uncertainty about "...some determinate fact ... because of a lack of complete information. '''cite_note-26 [20]]''' Epistemic uncertainty can be classified into six main types:'''cite_note-27 [21]]'''
*''' Measurement error''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Observational_error Measurement error] is uncertainty that manifests itself as (apparently) random variation in the measurement of a quantity. Repeated measurements will vary and demonstrate classic statistical behavior.
* '''Systematic error''' - [https://en.wikipedia.org/wiki/Observational_error Systematic error] occurs due to bias in the measuring equipment, model, analysis, observation, or sampling procedure. It is formally defined as the difference between the true value of the quantity of interest and the value to which the mean of the results converges as sample or data sizes increase. Unlike measurement error, it is not (apparently) random, and therefore, results subject to systematic error alone do not vary about a true value. Systematic error can result from deliberate judgment to exclude (or include) data, parameters, or models that ought not to be excluded (or included).
** "The only way to deal with systematic error is to recognize a bias in the process, model, or procedure and remove it. Systematic error, however, is notoriously difficult to recognize except on theoretical grounds. Corrections may only be applied when the magnitude and direction of the bias are known. Such corrections underlie the application of double-sampling methods in environmental science." (Regan, et. al., op. cit.)
*'''Natural variation''' - [https://en.wikipedia.org/wiki/Natural_process_variation Natural variation] or underlying process variation occurs in systems that "...change (concerning time, space, or other variables) in ways that are difficult to predict...across the full range of temporal and spatial values (or other related variables)." '''note-28 [22]'''
** This form of uncertainty is reduceable utilizing tools such as [https://en.wikipedia.org/wiki/Statistical_process_control statistical process control] and replication of experiments or observations.
*'''Inherent randomness''' - [https://en.wikipedia.org/wiki/Randomness randomness] or [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty aleatory uncertainty] exists because the system is"...in principle, irreducible to a deterministic one (the most well-known case is described by [https://en.wikipedia.org/wiki/Uncertainty_principle Heisenberg’s uncertainty principle] in quantum mechanics)." '''note-29 [23], note-30 [Note 7]'''
** Even if the argument is accepted that it is unlikely that any given physical system at some level is inherently random, it is important for any taxonomy to distinguish between systems that appear random due to incomplete or inconsistent information and those that are intrinsically random. (Again, see Regan (2002)).
*'''Model uncertainty''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling Model uncertainty] occurs in the process of generating a model as a conceptual representation of a particular phenomenon to create a [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling#Overview simplified reflection of reality]. This selectivity of factors and variables creates uncertainty in at least three ways. (Regan (2002))
** '''Variables and processes''' that are regarded as relevant often represent a trade-off between system knowledge and assumed states and model objectives;
** '''Models''' depict observed processes using logical or mathematical constructs based on underlying theories about system states or dynamics using continuous equations to describe discrete processes.
** '''Curve fitting''' (including interpolation and extrapolation) with mathematical expressions using model variables where inputs are discrete data points.
** Regan (2002) argues that '''model uncertainty''' is "...notoriously difficult to quantify and impossible to eliminate ...(and)...(t)he only reliable way of determining how appropriate a model is for prediction is to perform validation studies."
** Subjective judgment occurs due to the interpretation of scarce or error-prone data.
*** The only way to address this type of uncertainty is to "...assign a degree of belief about an event in the form of a subjective probability." (Regan (2002))
*Linguistic uncertainty - Linguistic uncertainty, or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. '''note-31 [24]], note-32 [25]]'''
** '''Context dependence''' is uncertainty concerning the context in which a statement is to be understood;
** '''Ambiguity''' occurs when words have multiple meanings, and further reduction to a single meaning is not logically possible in a given context.
** '''Underspecificity''' occurs in the presence of "unwanted generality" or multiple interpretations, and reduction to a smaller set or even a single interpretation is not logically possible.
*Stochastic or statistical uncertainty - Stochastic or statistical uncertainty, which is sometimes known as [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Aleatoric uncertainty] as well as the subject of [https://en.wikipedia.org/wiki/Stochastic_control stochastic control] pertains to the parameters of a risk model, originates from sampling or measurement error, and can be quantified and mathematically expressed (e.g., using confidence intervals).
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== Uncertainty Taxonomies in a Legal Environment ==
From a legal perspective, a definition of taxonomy is dictionary-based, such as "the systematic distinguishing, ordering, and naming of type groups within a subject field." given by Walker (op. cit.). Such legal concepts of scientific or engineering taxonomies are not required to be epistemically complete. Still, they must cover "the most significant aspects of ...(current, good discipline practices) ... and of the descriptive information commonly encountered by decision-makers" in a logically distinct manner. (Walker, 1991, p. 571) These taxonomies must be sufficient to catalog the types of uncertainty associated with descriptive assertion, including the critical subset of cause and effect assertions. This legal/decision-maker framework has presented descriptive uncertainty as having six components (Walker, 1991). Although not part of the Walker taxonomy, underlying the scheme is linguistic uncertainty, or "vagueness," as a source of uncertainty, including uncertainties due to context dependence, ambiguity, and under-specificity. '''note-33 [26]]''' Arguably, these uncertainties underlie the most basic element in the taxonomy.
* '''Linguistic '''- or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. (Regan, 2002)
* '''Conceptual'''- the definition by choice and design of descriptive concepts or variables to be used as [https://en.wikipedia.org/wiki/Predicate_(grammar) predicates] where the subject of an assertion identifies what is being discussed and the predicate provides information about the topic or characterizations such as what the subject is, what the subject is doing, or what the subject is like (Wikipedia).
* "Conceptual uncertainty, or the potential for conceptual error, arises whenever predication occurs. Whenever a concept is used to describe something, using certain concepts instead of others begins to structure how we understand the object, event, or instance under discussion. Predication or conceptualization generates useful information about things, but it also can inhibit our ability to think about those things with concepts other than those selected. The concepts used may not be the most fruitful or the best designed- either for scientific purposes or for making wise, fair, effective, and efficient decisions." (Edited for reading, Walker, 1991)
* Similarly, choice and design of descriptive variables enhance and restrict data set membership, creating potentially incomplete, incompatible, or inconsistent data sets, "...the classification categories employed, and the relationships among those categories (nominal, ordinal or scalar)." '''note-34 [27]]'''
* Concept uncertainty '''note-35 [28]''' like systematic error discussed above is notoriously difficult to recognize, except on theoretical grounds, and the magnitude and direction of the bias are known.
Both terms could be analogized to that of [https://en.wikipedia.org/wiki/Accuracy_and_precision#Terminology_of_ISO_5725 accuracy] in [https://www.bipm.org/en/publications/guides/ ISO 5725] which looks at the ability of the concept or system's ability to produce results that are proximate to the "true" value or state of the phenomenon. Conceptual uncertainty and systemic bias are related to another measure of uncertainty: validity. Walker has defined validity as the ability of a concept to quantitatively describe and explain what the phenomenon objective "truly" is. Walker also ties the concepts together when he states, "Validity concerns the "accuracy" of the measurement data, not its precision." (Walker, 1998) 
Additionally, conceptual uncertainty and associated validity issues could be propagated throughout this taxonomy. Every component of action and decision in the face of uncertainty raises validity issues. Walker recognized this when the author defined epistemic choice for concepts used throughout the other uncertainty components or layers.
* '''Measurement''' uncertainty or Misclassification error- the application of the underlying concepts or variables to specific, individual cases and the uncertainty of the reliability (Walker, 1998) of the resultant data. (It is important to remember that assessment observation and measurement are used interchangeably in this analysis.) Thus, an assessment method or procedure is considered "reliable" or "precise" in the scientific terms of ISO 5715 if it repeatedly produces consistent results.
* Measurement uncertainty is logically distinct from conceptual uncertainty, which can only occur after any conceptual uncertainty has been established. Still, misclassification can occur even when the conceptual uncertainty has been minimized. Conversely, underlying errors or errors made in measurement or classification propagate thru the reasoning chain and reduce the quality or value of the final result.
* While uncertainties associated with quantitative measurements or assessments can be statistically evaluated and described, uncertainties related to qualitative assessments are more problematic.
**Working through the taxonomic chain from base concept to causal explanation requires amassing and integrating qualitative information and quantitative data into input parameters for mathematical models. Developing procedures for producing consistent, repeatable sets of information from assessments that minimize conceptual error or systemic bias requires professional efforts to "validate" such methods, models, and judgments at all layers in the process. Validation as a process is something that civil engineering as a practice needs to embrace more frequently.
* '''[https://en.wikipedia.org/wiki/Sampling Sampling]'''- in the classical statistics sense, is the selection of a limited subset of a larger population to use as a basis for making estimates about the population in the form of attributes, proprieties, or parameters. The resulting information would be determinative in making forecasts about future population sampling or possessing a desired amount of predictive power. The uncertainty about the ability of this information to correctly predict future samples is termed sampling error.
* '''Modeling'''- Modeling uncertainty arises whenever a claim is made that out of a class of candidate relationships and variable pairings, one variable pairing inclusive of constants is chosen that possesses a particular or persistent mathematical relationship to another variable X. Modeling errors arise in selecting the wrong variable pair and constants or by incorrectly specifying its constants. (Walker, 1991, pg. 599)
** '''Causal'''- In causal modeling and analysis, the relationship between causal variables is not strictly a mathematical function. Still, it makes testable predictions and explains "...how a system of variables works, why a system works the way it does, or why it makes sense to think of certain variables as a system" at all." (Walker, 1991, pg. 609)
** '''Epistemic'''- the choice of interpretations for fundamental, logical concepts used throughout the other components or layers.
The components of this uncertainty taxonomy can combine in different ways to "...produce different aggregate uncertainties." '''note-36 [29]'''
:''' Only the scientific and engineering professions possess the capacity to develop and validate procedures and models that combine the different kinds of qualitative '''note-37 [30]''' and quantitative information and their associated potential for error into an aggregate measure or "scalar variables" of uncertainty.'''note-38 [31]]
Scalar variables are quantitative variables whose categories are related by some measure of the relevant property's incremental frequency, degree, or amount. (Walker, 1991) Examples are Modulus of Elasticity, Compression stress in elastic materials, etc.

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=== Economics Uncertainty Taxonomies ===
Like scientific concepts of uncertainty, economics is environment-specific (in this case, read sector of economy or markets), phenomenon-oriented (in this case, read human activity and manufactured phenomena such as firms and markets guided by human judgment and decision), and model-centric.
:''' Economics is not interested in all forms of uncertainty, only specific, meaningful subsets possessing casual connections with material impacts, often labeled as 'risks'.'''
One of its earlier writers ([https://en.wikipedia.org/wiki/Frank_Knight#Life_and_career Knight], 1921, 1948, 1957) attempted to define "uncertainty" as the presence of "defects of managerial knowledge" (or [https://en.wikipedia.org/wiki/Knightian_uncertainty knightian uncertainty] and thus risk centric) as risk that is immeasurable, not possible to calculate.(Wikipedia) Risk in this sense was defined as "the ordinary risks of business activity which can ... be reduced... by applying the insurance principle." Knight does acknowledge that risk is a specific and unambiguous subset of uncertainty. (Risk, Uncertainty and Profit, 1957, pg. 19) For Knight, measuring uncertainty meant measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences, which Knight referred to as "the imperfection of knowledge". (Risk, Uncertainty and Profit, 1957, pg. 197) One of Knight's many contributions to this analysis was recognizing information and knowledge's role in economic activity.
:"''(W)e are concerned only to emphasize the fact that '''knowledge is in a sense variable in degree and that the practical problem may relate to the degree of knowledge rather than to its presence or absence in toto.''' We live only by knowing something about the future, while the problems of life, or conduct at least, arise from knowing so little. This is as true of business as of other spheres of activity. The situation's essence is action according to opinion, of greater or less foundation and value, neither entire ignorance nor complete and perfect information, but partial knowledge. If we are to understand the workings of the economic system, we must examine the meaning and significance of uncertainty; and to this end, some inquiry into the nature and function of knowledge itself is necessary.''" (Knight, pg. 199) Emphasis added
Knight also reinforced the difficulties in classifying uncertainties when he analyzed life insurance. In this case, accidental death was an uncertain phenomenon compared to sickness and accident, where an "...objective description and classification of cases was impossible..." (Knight, pg. 248) Another factor that made uncertainties ineligible as risks and, therefore, uninsurable because they were unclassifiable was the exercise of judgment in making decisions by the businessman. (Knight, pg. 251)
This is a simple classification of economic uncertainty and, as an idealization, is philosophically controversial. '''note-39 [32]'''
* Uncertainty and information about the economic environment are distinct from uncertainty about others’ behavior or choices.
* Risk as a specific subset of uncertainty implies that "...all possible acts are known, all possible outcomes arising from each act are known, and it is possible to assign probabilities to each act." '''note-40 [33]'''
Like the economic [https://en.wikipedia.org/wiki/Theory_of_the_firm theory of the firm], an economic theory of uncertainty seeks to explain and predict the nature of a specific subset of uncertainty (in this case, risk relating to physical phenomena and human activity), including its behavior, structure, and relationship to the universal uncertainty of physical phenomena. In this sense, uncertainty, like firms and markets, can establish price equilibriums under the right circumstances different from those that might have occurred in either of the other two. Such a theory of uncertainty offers a partitioning scheme that decomposes uncertainty into distinct and meaningful subsets, of which risk is a significant one. It also explains how the presence and variability of knowledge cause or " drive" economic consequences. Unlike physical phenomena, economic activity creates artificial objects such as products, firms, mediums of exchange, markets, economies, and knowledge about those activities. The economics of uncertainty exists as an alternative to "certainty" models that assume away imperfect knowledge and unknown choice/judgment preferences when it is more efficient for economic decision-making. This also allows the introduction of the logical concept of "hazard" versus "risk". Frank used hazard extensively in Risk, Uncertainty, and Profit, but not to the extent that one could say it was interchangeable. An example of this is in the term "[https://en.wikipedia.org/wiki/Moral_hazard Moral hazard]," where a firm could engage in "riskier" and potentially "uninsurable" behavior if the information were transparent, and, therefore, insurance coverage of losses is limited. note-41 [34]], note-42 [35]]

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=== Uncertainty and Risk in Policy and Regulatory Environment ===
:''"Hume's 'just reasoner', when faced with a difficult public policy decision in current times, would probably commission a risk analysis. But how could they incorporate 'a degree of doubt, caution, and modesty?"'' '''note-43 [36]'''
The problem of "decision-making in the face of uncertainty" is how to regulate based on incomplete information that has the potential to be materially inaccurate. A contextual assumption is that we need to evaluate decision rules for dealing with uncertainty. As a social enterprise, risk regulation, whatever its substantive objectives, should be as '''effective, efficient, and equitable''' as practically possible. These "three E's" form a set of "process objectives" or "meta-goals." From the uncertainty standpoint, causal information can be usefully divided into two major categories: information about groups and information about individuals.

Each category, group, and individual has its distinctive types of inherent uncertainty. These are logically distinct, generally independent, and cumulative, contributing to '''(?????)'''

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=== Engineering Uncertainty Taxonomies ===
Like scientific and economic concepts of uncertainty, engineering is environment-specific, phenomena-oriented, model-centric, external choice, and driven by decision-making in the face of uncertainty. Like economics and unlike science, engineering is not interested in all forms of uncertainty, only those with determinable and material consequences. Like economics, engineering conceives risk as a specific and unambiguous subset of uncertainty determined by measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences. Also, engineering recognizes the role information and, by extension, knowledge plays in constructing the built environment.
'''note-44 [37]]''' Like economics, engineering differs from economics and scientific uncertainty in its interest in behavioral uncertainty.
*"Behavioral or interaction uncertainty is how individuals or organizations act or interact. Behavioral uncertainty arises from four sources: design uncertainty, requirement uncertainty, volitional uncertainty, and human errors." '''note-45 [38]]'''
** A design uncertainty is a choice among alternatives over which an individual or group of individuals exercises direct control but has not yet decided upon.
** Requirement uncertainty includes parameters of interest to and determined by the stakeholder, independent of the engineer or designer.
** Volitional uncertainty is uncertainty about what the subject him/herself will decide. Other people’s future actions and conduct are not entirely predictable, particularly when dealing with other organizations.
** Human errors occur during the development of a system or project due to blunders or mistakes by an individual or individuals.

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==Practice Frameworks for Uncertainty and Risk in Civil Engineering Practice==
===PMIBoK===
PMI does not define uncertainty, but the PMI Body of Knowledge uses the term in 45 places (uncertainty) and as describing properties in 8 places (uncertainties). [See Note 8] PMI does define "risk" and uses the term over 1,300 times in the same document. In its glossary, PMI also defines terms such as "threat" and "opportunity" but not events.[391 The BoK contextually defines uncertainty when it states that project risk has its origins in the uncertainty present in all projects. (Op. Cit., pg 309) 
'''Uncertainty''' is contextually defined and taken as having the property of affecting project execution and the ability to meet stakeholder expectations. Uncertainty is more than the sum of all individual risks (known and unknown). The nature of this uncertainty is not defined, only its capacity to affect something else or its properties. 
'''Risk''' is defined as " ... an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives." (Ibid.) Beyond this formal definition, PMI adds context when, in its risk management section (Sec. 11 ), the BoK states that risk is a caused event or condition with multiple causes and impacts. The BoK even offers specific techniques for mapping cause-and-effect relationships and influence diagrams. (Sec. 11.2.2.5) Not as evident but equally important is the recognition that not only is risk caused but the impact is "triggered". The risk trigger is an event or situation that signals that it is about to occur (Glossary, pg. 566). Risk conditions are factors that affect or otherwise contribute to risk, such as project stakeholders. Risk implicitly retains some residual element of probability in it, or a value of less than 1.0 with risk that approaches a level of near certainty is termed "issues" or "realized risk" .(Op. Cit., pg 309). PMI also links risk to underlying variations in project outcomes. (Ibid.) Risk is based upon data and information which can be assessed as to its quality (Sec. 11.3.2.3). This analysis looks to determine the value of the information and examines the degree to which the risk information is understood as well as its " ... accuracy, quality, reliability and integrity ... " (Ibid.) Part of that is understanding the relationship between occurrence and impact as outlined in PMl's Figure 11-10 Risk Impact Matrix and developing a model for prioritizing risk products and setting the threshold for action.
====Commentary:====
For defining and identifying risk, PMI notes in Sec. 11.2.2.4 that " ... Every project and its plan is conceived and developed based on a set of hypotheses, scenarios, or assumptions." Part of the risk analysis process is identifying risks to the project, such as inaccuracy, instability, inconsistency, or incompleteness of assumptions. Similarly, the quality of management plans, as well as their consistency with others and the project objectives and assumptions are 11 •• .indicators of risk in the project." (Sec. 11.2.2.1) 
Similarly, the role of engineering economics in risk is embedded or implied in the PMI definition of risk when the BoK discusses risk identification as a process of assessing which risks out of the total risk exposure for the project "may affect" the project and distilling their characteristics. Embedded in this statement is the concept that not all risks can cause economic impacts (positive and negative) to the project. Also embedded is the requirement to understand risk characteristics to identify project risk (Cost and schedule). Lastly, risk identification is an iterative process of incrementing project risk documentation, much like project scope, cost, and schedule documentation. 
Lastly, PMI acknowledges the complex constitution of risk aggregates or the "sum" of all risks in its material, but is it a sum? Risk information has descriptive and explanatory content. These are knowledge and meta-knowledge components. Understanding project assumptions is project-specific knowledge, but identifying risks from assumption inaccuracy, instability, inconsistency, or incompleteness requires professional or program meta-knowledge. 
'''Uncertainty exists in all projects, but risk, a subset of uncertainty with material economic impacts, is a key meta-knowledge element for civil engineering practice.'''
===Software Development Process- Risk Focused===
The Unified Process requires the project team to focus on addressing the most critical risks early in the project life
cycle. The deliverables of each iteration, especially in the Elaboration phase, must be selected to ensure that the greatest risks are addressed first. <ref> Booch, Grady, Ivar Jacobson, and James Rumbaugh. "The unified software development process."
Reading: Addison Wesley (1999).</ref>
===American Society of Civil Engineers (ASCE)===
ASCE does not define uncertainty, but the ASCE CE Body of Knowledge (CEBoK) uses the term in 22 places (uncertainty) and describes properties in 13 places (uncertainties). 
The CEBoK does not use the phrase "uncertainty and risk" as PMI and AACE do in their publication. ASCE reverses the two (Risk and uncertainty) in twelve places regarding technical outcomes (Outcome 12). A fundamental difference between PMI and ISO is that risk is considered a subset of uncertainty. ASCE does not define "risk" but uses the term 25 times in the same document. Almost all are in the context of variation of design parameters and not in the classical context of cost, schedule uncertainty, and risk.
====Commentary:====
...
=== American Association of Cost Engineers (AACE) ===
AACE defined uncertainty, risk, and other related terms in its Risk Management Dictionary. <ref>Risk Management Committee uRisk Management Dictionary, Cost Engineering Vol. 37/No. 10, OCTOBER 1995. </ref>
AACE's overall strategy was to define risk and other terms that stem from base uncertainty. AACE first defined uncertainty as .... "(t)he total range of events that may happen and produce risks (including both threats and opportunities) affecting a project (see opportunities, events, conditions, risk, and threats" where the following sub-definitions apply:
** Biases--A lack of objectivity based on the individual's position or perspective. Systematic and predictable relationships between a person's opinion or statement and his/her underlying knowledge or circumstances. Note: there may be "system biases" as well as "individual biases."
** Condition (Uncertain Condition)-Any specific identifiable circumstance (such as the rate of inflation or the quality of labor available) that might affect the outcome of the project
** Event (Uncertain Condition)-is a specific identifiable action (such as a large government project being started in the same labor area as your project) or an act of nature that might happen and that (if it does happen) could affect the outcome of the project.
** Opportunities are Uncertain events that could improve the results or improve the probability that the desired outcome will happen
** Threats are Uncertain events that are potentially negative or reduce the probability that the desired outcome will happen.
*** AACE defines Risk as an "ambiguous term" (sic) that is synonymous with uncertainty or a negative subset such as "threats", or an overall negative impact of all possible uncertainties.
====Commentary:====
...

==Limitations of the definition==
...
==Working definition==
...
==Beneficial outcomes==
...
==See also==
Wiki articles on the project.

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==Notes==
<references group="note" />
<li> Implicit in this definition of uncertainty as a "state of' is ... </li>
: " ... a conceptualization of uncertainty as a subjective, cognitive experience of people--a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one's lack of knowledge, without which one could not feel uncertain; '''uncertainty is a form of "meta-cognition" ... ( or alternatively, its main component, meta-knowledge) ... -a knowing about knowing.'''" (Han, 2011, op. cit., Emphasis added)
</ol>
<ol start="5">
<li> Cox wanted his system to satisfy the following conditions:</li>
: Divisibility and comparability- The plausibility of a statement is a real number and depends on the information we have related to the statement.
: Common sense - Plausibilities should vary sensibly with the assessment of plausibilities in the model.
: Consistency - If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). -preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf
</ol>
<ol start="6">
<li> The utility of existing taxonomies of uncertainty in civil engineering and, by extension, "risk" has been unsatisfactory for this very reason. This is particularly true in cases where the source of the uncertainty is conflicting versus incomplete information. See Regan, et. al.,"A taxonomy and treatment of uncertainty for ecology and conservation biology" (2002)
for a survey of scientific taxonomies for uncertainty.</li>
<li> Regan et al. argue that " ... genuine examples of this kind of uncertainty are hard to find. Even classic cases of random experiments like coin tosses and the throwing of dice are deterministic; it is just that we do not have enough information about the dynamic processes and initial conditions to make any sensible estimates about the outcomes. Such processes are, for all intents and purposes, inherently random, but they are not genuinely inherently random. For similar reasons, complex systems such as ecosystems and weather patterns are unlikely to be inherently random. Similarly, chaotic systems are entirely deterministic. They are unpredictable because the deterministic processes generating them and the relevant initial conditions are hard
to fully specify (see Stewart 1989, Sugihara et al. 1990)." (Regan (2002) </li>
<li> The CEBoK uses the phrase "uncertainty and risk" twice in reference to scheduling (Section 6.5,2) and cost estimating (Section 7.2,2) as well as reversing the two (Risk and Uncertainty) also in two places in discussing project life cycle (both in Section 2.4.1) </li>
</ol>

==References==
<references />

Uncertainty and Risk in Civil Engineering Practice

2024-11-24T16:58:54Z

WikiSysop: /* Certainty and Uncertainty */

'''Life is short and the art long; the occasion instant, experiment perilous, decision difficult.''' (Hippocrates as quoted in Fox,)
:''See also [[Project Definition]] , [[Project Life Cycle and Phase Models]] , [[Project Delivery Methods]]''
:''See also [[Technical Outcomes for Civil Engineering:Risk and Uncertainty]]''
==Basic Considerations==
As human beings,'' ... being alive means seeking opportunities and taking risks.'' As knowledge professionals
living in the 21st century, this means coping with an increasingly complex number of uncertainties for humans
living in this environment. We seek to understand better how these uncertainties can be characterized and
managed. The essence of this article and the experience for engineers in general and civil engineers in particular is
that manageable uncertainty is, by definition, termed risk, and its kindred cousin is termed hazard. This causes us to experience the
:" ''... human dread of and fascination for risk and the increasingly important role of risk analysis within societies ...'' " <ref name="McDaniels"> McDaniels, Timothy, and Mitchell Small. Risk analysis and society: an interdisciplinary characterization of the field. Cambridge University Press, 2004, page 1 </ref> (Ibid.)
and, by extension, civil engineering. McDaniels et al. argue that risk management has been
fundamental to our social and governance development for the past 10,000 years. The scale and shape of the
uncertainties faced in this period shaped the societies that have developed today. The central thrust of this effort
over the centuries has been to reshape and re-frame our understanding and conception of uncertainty from one of
complete unknowing and simple acceptance as our fate in life to one of management (cf "''Against the Gods''"
concept in Bernstein's book <ref name = Bernstein">Bernstein, Peter L., and Jesse Boggs. Against the gods. Simon & Schuster 1997.</ref>.
: ''All the knowledge professions and disciplines have struggled with managing uncertainty, for it is impossible to manage unbounded uncertainty.'' <ref group="note">See McDaniels et al. 2004 for an extensive discussion and bibliography on the historical development of Risk Analysis and some key milestones in risk analysis in the 20th century.</ref>
As outlined below, professions such as civil engineering have successfully acted in the face of uncertainty. It lies in the profession's ability to reduce a wide variety of uncertainties into increasingly smaller and crucially bounded subsets that can be managed. These are called 'risks. More recently, this process has evolved, and individual disciplines such as Civil Engineering (CE) have developed their knowledge and models to perform risk analysis. Risk is also taught as a distinct discipline and specialty practice within civil engineering. Still, it has some unique features that set it apart from other more classical practices within CE. As such, it is one of the first of some very specialized CE practices that use knowledge about civil engineering knowledge, or meta-knowledge. Other examples of civil engineering metaknowledge are project controls and quality controls.
==Semantic, Epistemic and Logical frameworks==
The semantic, epistemic, and logical frameworks for uncertainty and risk have several dimensions and layers of logical frameworks. The semantics problems include interchangeable usages for risk and uncertainty and hazard, uncertain and imperfect, and a lack of definitional material or context. The semantical scheme for this article will be to proceed from the distinction between certainty and uncertainty through marginal refinements and reductions of uncertainty up to the point of causal uncertainties. Beyond this point of semantics will be the logic frameworks or the subject of taxonomies of professional knowledge. First, it is about simple, testable phenomena, and then, it moves on to complex taxonomy schemes for artificially constructed phenomena or engineering projects.
<ref group="note">TBD</ref>
==Semantics framework==
===Certainty and Uncertainty===
''''' There is no such thing as absolute certainty, but there is assurance sufficient for the purposes of human life.''''' ([https://en.wikipedia.org/wiki/John_Stuart_Mill John Stuart Mill]) 
'''''If you tried to doubt everything, you would not get as far as doubting anything. The game of doubting itself presupposes certainty.''''' ([https://en.wikipedia.org/wiki/Ludwig_Wittgenstein Wittgenstein] # 115 from On [https://en.wikipedia.org/wiki/On_Certainty Certainty]) 
It is important to note that the references to epistemic or epidemiological knowledge in this article are assumed to relate to three forms of knowledge, namely:
* Knowledge that ( [https://en.wikipedia.org/wiki/Descriptive_knowledge descriptive or declarative or propositional knowledge])
* Knowledge how ( or "[https://en.wikipedia.org/wiki/Procedural_knowledge know-how]"), and
* [https://en.wikipedia.org/wiki/Knowledge_by_acquaintance Knowledge by acquaintance].
'''Certainty''' has been defined as "an epistemic property" of knowledge in all its forms and the state of our beliefs about that knowledge.
<ref>Certainty, Stanford Encyclopedia of Philosophy, accessed on September 12, 2015, at http://plato.stanford.edu/entries/certainty/#ConCer </ref>
Certainty about any belief about knowledge implies that it is not subject to doubt or skepticism. This immunity to criticism can be dogmatically based, emphasizing the importance of a propositional-based sense of truth over experiential, sensory perceptions. <ref>Definition of [https://en.wikipedia.org/wiki/Dogmatic_theology Dogmatic theology or belief], accessed at Wikipedia</ref>
: '''The demarcation line between dogmatic and non-dogmatic beliefs lies in the presence and recognition of specific criteria and information that would make the believer change their beliefs.'''
An empirical framework of [https://en.wikipedia.org/wiki/Testability testability] and [https://en.wikipedia.org/wiki/Falsifiability falsification] is required to recognize this and limit dogmatism. For example, one could argue that people would change their minds if God asked them to. Similarly, one could construct a concept of epistemic as opposed to dogmatic belief certainty as the ability to know anything that one chooses to know and can be known or [https://en.wikipedia.org/wiki/Omniscience inherent omniscience].

A second kind of certainty is epistemic, when conviction reflects the highest possible support for a belief. In this sense, knowledge is separate from beliefs, although someone may have beliefs about a property, such as certainty of knowledge. Logically, it has been shown that there will be unprovable statements within the system for any such knowledge system. Secondly, the knowledge system cannot demonstrate its own consistency. ([https://en.wikipedia.org/wiki/Gödel%27s_incompleteness_theorems Gödel's incompleteness theorems])
: '''Certainty, in real life, is useless or often damaging (the idea is that "total security from error" is impossible in practice, and a complete "lack of doubt" is undesirable)''' ([[https://en.wikipedia.org/wiki/Certainty Physicist Carlo Rovelli]])
'''Uncertainty''', on the other hand, arises immediately in the slightest amount of doubt or criticism.
<ref group="note"> The Merriam-Webster dictionary defines uncertainty as “the quality or state of being uncertain,” which is something of a circular definition. Likewise, one could define uncertainty as the state of not being certain.

Synonyms are distrust, doubt, misgiving, mistrust, reservation, skepticism, and suspicion. Another writer added indefinite, indeterminate, not certain to occur, problematical, unreliable, untrustworthy, unknown beyond doubt, dubious, doubtful, not clearly identified or defined, not constant, variable, and fitful to the list.
:(Han, Paul KJ, William MP Klein, and Neeraj K. Arora. "Varieties of Uncertainty in Health Care: A Conceptual Taxonomy." Medical Decision Making 31.6 (2011): 828-838.)
Han et al. noted that any definition of uncertainty clearly encompasses "...numerous types, sources, and manifestations of uncertainty, and ...(any) ...useful working definition of uncertainty needs to specify the concept underlying these varied meanings of the term."</ref>
Implicit in this definition of uncertainty as a “state of” is ...
"...a conceptualization of uncertainty as a subjective, cognitive experience of people—a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one’s lack of knowledge, without which one could not feel uncertain; uncertainty is a form of “meta-cognition” ...(or its main component, meta-knowledge)... —a knowing about knowing." (Han, 2011, op. cit., Emphasis added)
Similarly, uncertainty could be defined as "...any departure from the unachievable goal of complete [https://en.wikipedia.org/wiki/Determinism determinism]."
<ref name="Walker1">Walker, W. E., Harremoës, P., Rotmans, J., Van Der Sluijs, J. P., Van Asselt, M. B., Janssen, P., & Krayer von Krauss, M. P. (2003). Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support. Integrated assessment, 4(1), 5-17.</ref>
Reasoning under uncertainty is very different than performing the same under certainty. In reasoning under certainty, one has complete knowledge and deduces without doubt and equally important, without limitation, thereby concluding free from error. Reasoning under uncertainty, one works in a state of incomplete, inconsistent, and limited knowledge; doubts cloud any statements or assertions and thereby taint any deductions/inferences resulting in the potential for error. <ref group="note">Implicit in this definition of uncertainty as a “state of” is ...
"...a conceptualization of uncertainty as a subjective, cognitive experience of people—a state of mind rather than a feature of the objective world. The defining feature of this state, furthermore, appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one’s lack of knowledge, without which one could not feel uncertain; uncertainty is a form of “meta-cognition” ...(or alternatively, its main component, meta-knowledge)... —a knowing about knowing." (Han, 2011, op. cit., Emphasis added)</ref>
:'''Knowledge professions reason in a state of incomplete, inconsistent and limited knowledge with doubts that cloud any statements or assertions and taint any deductions/inferences resulting in the potential for error.'''
:''' It is also important to note the semantical and logical correlation between 'reasoning under uncertainty' or 'acting in the face of uncertainty' with the 'potential for' or 'presence of' error. The presence of uncertainty is invariably linked to the potential presence of error.''' The [https://en.wikipedia.org/wiki/Contingency_(philosophy) contingent] nature of uncertainty logically implies the contingent nature of error.
:'''Knowledge Professions use error as a proxy for uncertainty such that within discipline knowledge frameworks, uncertainty can be managed.''' The rationale for this belief is that error can be reliably described, quantified, explained, and ultimately reduced in ways that uncertainty cannot.

'''Uncertainty''' implies a range of variation that is impossible in certainty. Concepts and definitions of uncertainty are unbounded and vast but start from the point of complete and total ignorance. In describing uncertainty, statements can range from complete ignorance to relatively high degrees of belief that there is less potential for error in our reasoning or action. The latter is based upon confidence in justified knowledge, favorable past experience, and reliable parameters/models. One can't make that statement about ranges in certainty. If certainty, by definition, precludes doubt of any type or nature, then how could that be graduated to any degree? 
Uncertainty, on the other hand, can be reduced through disciplined efforts to acquire knowledge. In short, we can reduce or even manage uncertainty (to a degree); how can certainty be improved? Are there higher degrees of perfection? The answer is no. We reason from an uncertain starting point. Yet this effort presumes, as Mill and Wittgenstein postulated, that we can develop or acquire an increasing degree of belief or conviction that there is less potential for error in our reasoning or action. This position assumes that some way exists to identify what a lesser degree of uncertainty would look like.
:'''Knowledge professions acquire an increasing belief or conviction that there is less potential for error in their reasoning or action based on justified knowledge, favorable past experience, and reliable parameters/models.'''
Uncertainty is, in some form, amenable to description and explanation through observation and analysis. It is explainable and predictable to such a degree that it interests professionals such as scientists and engineers. Professional knowledge of this type has explanatory or predictive power, albeit limited in scope and application to relevant subjects such as physical objects or phenomena and functionality.
:'''The very act of reducing the scope of uncertainty to a limited set of phenomena implies choice and, ultimately, the decision to act in the face of such uncertainty, but acting or judging in this manner adds more value than doing the same under relative ignorance.'''
'''Uncertainty''', as described above, implies a hybrid nature. Examples of this are economic or decision-theoretic applications. In economics, [https://en.wikipedia.org/wiki/Perfect_information perfect information] allows one within the limiting framework of perfect competition in economic games to make decisions with 'perfect knowledge.' The crucial difference here is the distinction between '[https://en.wikipedia.org/wiki/Perfection perfection],' broadly, a state of completeness and lawlessness, and 'perfect, which can be thought of as an analytical limit that is approachable but can never be attained. Arbitrarily delimiting the realm of knowledge into defined, finite boundaries allows the simulated production of perfect choice, assumed crucially, to be free from error. The hybrid nature of this form of 'simulated' certainty produced within boundaries defined by assumptions and, therefore, clouded by doubt is itself a form of uncertainty. The importance of this is the semantical and logical association that exists between certainty and perfection versus the hybrid concept of a perfect anything, whether knowledge, infraction, competition, etc., or in the case of engineering, elastic, permeable, conductive, etc., being contained within an admittedly imperfect matrix of uncertainty.
:'''Simply put, a finite, heavily bounded piece of uncertain knowledge can be improved through the simulated use of [https://www.merriam-webster.com/dictionary/assumed assumedly] perfect information, but the perfection of knowledge cannot be attained or simulated.'''
Engineering and Economics, for example, simulate finite elements of perfect knowledge or information, heavily bounded with assumptions and a range of applications. By doing so, these professions reduce the bounds and variability of uncertainty and thereby manage it. Using these hybrid models to acquire knowledge takes place in an economy (regulated or institutional) and environment (transparent and structured), which puts pressure on the various professions to recognize and use qualitative and quantitative knowledge better to reduce uncertainties in their activities. The challenges in this reflect the nature of the profession's or discipline's knowledge (e.g., scientific, engineering, or medical), the policies and structure of the relevant professional knowledge bodies, process objectives, constraints, and "the elusive demands of politics." <ref name="siren">Walker, Vern R. "The siren songs of science: toward a taxonomy of scientific uncertainty for decision-makers." Conn. L. Rev. 23 (1990): 567.</ref>
One common theme runs through all of these efforts to address the presence of uncertainty in professional activities. 
:"The available scientific information upon which a decision must be made is almost always a mixture of engineering knowledge and uncertainty. Regardless of the information or methodology, there is the potential for error. This is true regardless of the relevant science, whether archaeology or aeronautics, economics or engineering, pharmacology or toxicology or epidemiology. Achieving the best social decisions requires not only understanding and using what we know but also appreciating and weighing the extent of our uncertainty. In making the best use of ... information in ... decision-making, it is still true that the beginning of wisdom is knowing what it is we do not know." (Vern, Op. cit., Reformatted and engineering substituted for scientific) 
'''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty to develop explanations, calculate, and make predictions. This professional knowledge converges on but never reaches certainty, producing a "legitimacy" from the fruitfulness of its use.''' <ref>Randomness Is Unpredictability, Antony Eagle, The British Journal for the Philosophy of Science, Vol. 56, No. 4 (Dec., 2005), pp. 749-790 </ref> 
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=== Bias and Uncertainty ===
Up to this point, the concept of error, as outlined in uncertainty, could easily be simulated by a truly random variable. There should be no detectable differences around the unseen or specified statistical mean. However, several instances in experience point to a tendency towards one extreme or a pattern of error, a collective that is in itself an error of errors, namely [https://en.wikipedia.org/wiki/Bias bias].

Arguably, this could be part of the choice uncertainty discussion below, but it makes more sense to be looked at on its own. Acting in the face of uncertainty is an exercise of personal knowledge or experience. Whether using professional knowledge or individual experience, the potential is there to make a series of choices that, in some instances, make errors more likely than they would be if considered in the context of a statistical analysis.

This tendency for an individual to make the same error in a predictable or repeatable manner is termed bias. Personal experience, beliefs, knowledge, data, and models all have inherent built-in factors, errors, or defects that create predictable error patterns when applied in decision-making. Walker (1998) argues implicitly that such bias can also be considered systemic error. This type of uncertainty is difficult to identify except on theoretical grounds when the magnitude and direction of the bias are known. Most of these biases, whether systemic or knowledge-based, individual or practice-based, require disciplined efforts to "validate" such methods, models, and judgments at all layers while bounding the uncertainty.
: This is the challenge to professional knowledge that must be overcome, namely, developing and validating models and methods for managing bounded uncertainty that minimize this tendency towards repeatable groups of error or bias. Therefore, from this point forward, the discussion will refer to 'uncertainty and its proxies, error, and bias.'
===Propagating Error and Uncertainty===
Partitioning uncertainty and its proxies, error, and bias into logically distinct subsets involves or is associated with multiple-step execution or choice frameworks. Combining those measurements or choices into single parameters or choices involves, in some cases, assembling the data/information in a unique or limited range of sequences. This implies that associated errors in observing, calculating, or choosing will have unequal impacts or consequences depending on where in the chain the error occurs. Also implied in this is the possibility that subsequent errors will be causally linked to some degree. (In statistical practice, this depends upon whether the errors are independent of each other or correlated, specifically, co-variant.)
:'''Professions develop knowledge of how uncertainty and its proxies, error, and bias propagate through an ordered and normative series of observations or choices. This allows the profession to prioritize error and bias reduction to achieve an optimized reduction of uncertainty.'''
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===Cognition, Metacognition and Uncertainty===
The concept of choice and the resultant error in uncertainty can be further delimited depending on the role of [https://en.wikipedia.org/wiki/Cognition cognition], [https://en.wikipedia.org/wiki/Metacognition meta-cognition], and independent external elements. The rationale for this argument is that such choice activity in the face of uncertainty is a conscious, cognitive act.

An argument could be made that such cognition is a prerequisite for exercising choice and introducing error, as presented and discussed above. The counterfactual to this argument is the example of uninformed choice. Choices introduce as much as errors or more as informed choices could be made. (???) Choice, in this sense, is indifferent to knowledge. Purposeful choice in an environment of objectives requires knowledge, method, and experience, but uninformed, speculative choice does not.
:"''' Cognition''' is the set of all mental abilities and processes related to knowledge, attention, memory and working memory, judgment and evaluation, reasoning and "computation," problem-solving and decision making, comprehension and production of language, etc. Human cognition is conscious and unconscious, concrete or abstract, as well as intuitive (like knowledge of a language) and conceptual (like a model of a language). Cognitive processes use existing knowledge and generate new knowledge." (Wikipedia)
:"'''Metacognition''' is "cognition about cognition", or "knowing about knowing" and can take many forms. It includes knowledge about when and how to use particular strategies for learning or problem-solving. There are generally two aspects of [https://en.wikipedia.org/wiki/Metacognition metacognition]: knowledge about cognition and regulation of cognition."
Some types of metacognition knowledge are:
:'''Person knowledge''' (declarative knowledge), which is understanding one's own capabilities.
:'''Task knowledge''' (procedural knowledge), which is how one perceives the difficulty of a task, which is the content, length, and type of assignment.
:'''Strategic knowledge''' (conditional knowledge) is one's capability to use strategies to learn information. (Accessed at Wikipedia)
Like metacognitive knowledge, metacognitive regulation or "cognitive control" contains three essential skills.
:'''Planning:''' refers to the appropriate selection of strategies and the correct allocation of resources that affect task performance.
:'''Monitoring:''' refers to one's awareness of comprehension and task performance.
:'''Evaluating:''' refers to appraising the final product of a task and the efficiency at which the task was performed. This can include re-evaluating strategies that were used. (Accessed at Wikipedia)
Nothing has been said up to this point that limits this structure to an individual, or a collective, or two entities making cognitive choices that are incompatible or inconsistent to varying degrees. Interpretations of this scheme include an individual professional exercising judgment in a decision that, in turn, relies upon the collective judgment and decision of the discipline as an underlying basis versus the decision of another individual to oppose/protest or otherwise contest that decision. Thus, the framework of cognitive/meta-cognitive applies to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, economics of regulatory practice, and game theory.
:By solving useful problems in the face of inherent uncertainty, discipline-specific frameworks of cognitive/meta-cognitive activity apply to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, the economics of regulatory practice, and game theory.
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===Probability and Uncertainty===
Probability is a [https://en.wikipedia.org/wiki/Coherence_theory_of_truth coherent] approach to uncertainty in the physical and mathematics or "classical" domains such as engineering. <ref>See Colyvan for a critique of this claim, Colyvan Mark. "Is probability the only coherent approach to uncertainty?." Risk Analysis 28.3 (2008): 645-652.</ref> and is the "...is certainly the best-known and most widely used formalism for quantifying uncertainty.<ref>Morgan, Millett Granger, Max Henrion, and Mitchell Small. Uncertainty: a guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge University Press, 1992.</ref> [https://en.wikipedia.org/wiki/Cox%27s_theorem Cox’s theorem] ("Any measure of belief is [https://en.wikipedia.org/wiki/Isomorphism isomorphic] (but not necessarily equal) to a probability measure") is a well-known argument for the validity of that argument.
<ref group="note">Cox wanted his system to satisfy the following conditions: 
Divisibility and comparability – The plausibility of a statement is a real number and is dependent on the information we have related to the statement.
:Common sense – Plausibilities should vary sensibly with the assessment of plausibilities in the model.
:Consistency – If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). — preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf</ref>

Another writer, [https://en.wikipedia.org/wiki/Frank_Knight Knight] (1921,1956), presented the following taxonomy of probabilities:
<ref>As presented and discussed in Runde, Jochen. "Clarifying Frank Knight's discussion of the meaning of risk and uncertainty." Cambridge Journal of Economics 22.5 (1998): 539-546.</ref>
: '''Classical 'a priori' probability:''' As an idealized model, numerical probabilities are computed based on generalized principles of assigning equal likelihoods and mutually exhaustive possible outcomes (Runde, 1998) to all set members. Such probabilities are assigned to "...outcomes based on a judgment of indifference between those outcomes, that is, based on the absence of any evidence of real influences in play that may render any one outcome more or less probable than any other." (Runde, op. cit.) This builds upon Knights's concept of an 'absolutely homogeneous classification of instances that are completely identical except for [https://en.wikipedia.org/wiki/Indeterminate_(variable) indeterminate] factors. This judgment of probability or logical probability is on the same logical plane as the propositions of mathematics and ultimately [https://en.wikipedia.org/wiki/Inductivism inductions] from experience.'(Knight, 1956, pg.225)
: '''Bayesian 'a priori' probability:''' In contrast to interpreting probability as the "frequency" or "propensity" of some phenomenon, [https://en.wikipedia.org/wiki/Bayesian_probability Bayesian probability] is a quantity that we assign to represent a state of knowledge or a state of belief. In this view, probability is assigned to a hypothesis, often using the basis of past or prior experience. In contrast, under the frequentist view, a hypothesis is typically tested without being assigned a probability based on prior experience. The Bayesian interpretation of probability can be seen as an extension of propositional logic that enables reasoning with hypotheses, i.e., propositions whose truth or falsity is uncertain. Bayesian probability belongs to the category of evidential probabilities; to evaluate the probability of a hypothesis, the Bayesian probabilist specifies some prior probability, which is then updated in the light of new, relevant data (evidence). The Bayesian interpretation provides a standard set of procedures and formulae to perform this calculation. <ref>See also Larvor, B. "After Popper, Kuhn, and Feyerabend: Recent Issues in Theories of Scientific Method." Metascience (2002).</ref>
: '''Statistical or 'a posterior probability:''' Empirical evaluation of the frequency of association between predicates, not analyzable into varying combinations of equally probable or logical, 'a priori' alternatives. Knight argued that any "high degree of confidence" that the judgment that experience will remain valid for future predictions is "...still based on an 'a priori" judgment of indeterminateness." (Knight, Op. Cit.) Knight's argument to support this is what will be discussed further below that first, "...the impossibility of eliminating all factors not really indeterminate; and, second, the impossibility of enumerating the equally probable alternatives involved and determining their mode of combination so as to evaluate the probability by a priori calculation." (Knight, Op. Cit.) Knight noted that the main difference between this form and that of logical or inductive probability was the presence of empirical information, which allowed the analyst to identify propensity and direction in the observed phenomena. In this case, 'a priori' probability values may be derived from first principles and "statistical probabilities are determined a posteriori by the empirical method of counting instances." (Runde [1998] quoting Knight, op. cit.)
:'''Estimates:''' The distinction here is that there is no valid basis ('a priori' or 'a posteriori') for any classifying phenomena. For Knight, this form of probability presented the greatest logical difficulties of all ... but its distinction from the other types must be emphasized, and some of its complicated relations indicated..." (pp. 224-5, emphasis in the original)

Knight's concept of probability can be viewed as "..a continuum of probability situations, depending on the degree of homogeneity of the 'instances' in question" <ref>Runde, Jochen. "Clarifying Frank Knight's discussion of the meaning of risk and uncertainty." Cambridge Journal of Economics 22.5 (1998): 539-546.</ref>; i.e. going from a logically distinct but equally likely set of elements to a unique set with one member with statistical frequency in the middle. Knight's main motivation for distinguishing between a priori probability and statistical probability underscores his opinion that "(i) the 'mathematical or a priori type of probability is practically never met with in business, while the second is extremely common'; and (ii) 'the statistical treatment never gives closely accurate quantitative results' (Runde quoting Knight pp. 215-16). Runde argues that Knight couldn't conceive of (empirically tabulated) occurrences that are used in daily commerce classes of instances that we have to make do within the course of everyday economic life could be divided into subclasses of instances that are sufficiently homogeneous to permit the determination of what he calls 'real' probability (p. 217).
:'''In modeling degrees of uncertainty, any measure of belief is isomorphic but not necessarily equal to a probability or statistics measure'''.
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===Explanation, Prediction and Uncertainty===
It should be clear that the general notion of uncertainty synonymous with complete ignorance must be bounded as part of a scheme to produce a useful concept of uncertainty in professional practice such as civil engineering.
:The first principle that must be introduced is that such uncertainty must be bounded by reliable and valuable knowledge gained from past experience combined with analytical and computational capabilities to address an immediate and real problem of interest. This could be viewed as the economic interest argument. Only uncertainties associated with physical phenomena and economic scarcity will be addressed.
:The second principle is that the knowledge model is capable of producing statements or assertions in the form of hypotheses that themselves are testable. [https://en.wikipedia.org/wiki/Testability Testability], in this sense, is defined as the property applying to an empirical hypothesis and involves two components:
::The logical property that is variously described as contingency, defeasibility, or falsifiability, which means that counterexamples to the hypothesis are logically possible. Contrast this to a [https://en.wikipedia.org/wiki/Tautology_(logic) logical tautology], which is always true that is true in every possible interpretation. The practical feasibility of observing a reproducible series of such counterexamples if they do exist. In short, a hypothesis is testable if there is some real, non-zero expectation of deciding whether it is true or false of verifiable, reproducible experience. Upon this property of its constituent hypotheses rests the ability to decide whether a theory can be supported or falsified by actual experience data. (Source Wikipedia.)
'''Knowledge models must be capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria described above in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error and bias.'''
===Variability and Aleatory Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge referred to as epistemic uncertainty. Acting in the face of epistemic uncertainty introduces choice uncertainty (discussed below) and results in the production of explanations and predictions. It is implied that there will be testable hypotheses in the form of outcomes of interest to the knowledge professions. Inherent in nature is a variation of results, which may appear to uncertain but isn't. Observations of phenomena will not be the same, and part of the experimental process is to narrow those results down to within an acceptable level of tolerance. Statistical measures seek to identify underlying and unobserved measures of central tendency. These parameters are not directly observed but are derived to a degree of confidence.
In some cases, variation around a mean or within a statistical range may be perfectly acceptable to the knowledge profession. Examples of this are in the physical sciences and medicine. In Engineering and, to a lesser extent, economics, the process or phenomenon may be required to be more controlled to a narrower range than what a natural or "unmanaged" amount of variation would allow. Several courses of action present themselves.
:The first would be to ignore the aleatory phenomenon and choice, which is rejected outright.
:The second would be to assume that, in effect, such variation is itself a form of minimally managed uncertainty and treat it the same as epistemic uncertainty. This is not a desirable outcome as reducing Aleatory error and bias requires
:A third approach would be to acknowledge that process variation is, in some forms, manageable. This means making choices and acting in a way that presumes that the natural occurrence of error can be reduced, or more importantly, bias can be reduced. There is ample precedent for the third approach in civil engineering design and project management. In this sense, the argument is made that the aleatory response to managed efforts results in an elastic variation range. Presumably, an effective management effort either "shifts" the central tendency of the process results or reduces the variation range of occurrence or some other population parameter. Choosing to act and accept that narrower range rather than the broader natural variation may result in an outcome that, while well within the bounds of the expected variation, is outside the arbitrary control limits established by the choice and action. While this phenomenon mimics the broader uncertainty and its proxy error, it is not an error. The information on the variation was known, and the ability to produce narrower results was an error, albeit a pseudo-error, when compared to the broader classes of uncertainty discussed above, such as total ignorance.
::Looking at uncertainty using this third approach means that the error in choosing a new target parameter for this process may contain epistemic errors (our knowledge of the phenomenon may be erroneous, or the model flawed), aleatory uncertainties as discussed, or, more importantly, choice error or bias in thinking that we can move the process results to meet the requirements.
:'''In civil engineering practice, this may mean applying professional judgment in developing partitioning schemes or allocating uncertainty between the three dimensions (Epistemic, Aleatory, and Behavioral) of uncertainty.'''
Lastly, raising the topic like this brings the questions of effectiveness and efficiency to the fore. Advancing knowledge models that reduce error and bias at first are largely choice models resulting in justifications and favorable experiences. At some point in the process, the knowledge profession advances explanations and predictions that require reliable mathematical or logical concepts that are reduced to models and parameters. The question of efficiency becomes important; the broader uncertainty may have been reduced, but the process is economically inefficient. The profession is urged to become more frugal or, for example, reduce the variability of the outcomes. Knowledge professions often operate under conditions that require them to acquire knowledge and reduce uncertainty effectively and efficiently. Doing so requires understanding both epistemic uncertainty, as discussed above, and statistical process variation or aleatory variation.
:'''Knowledge professions manage and reduce uncertainty in the form of error and bias in an economically effective and efficient manner. This effort requires understanding both epistemic and aleatory uncertainty.'''
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===Choice and Behavioral Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge, referred to as epistemic uncertainty, as well as the inherent variations in results, or what was termed aleatory uncertainty. Acting in the face of that epistemic and aleatory uncertainty introduces yet another uncertainty into the process, namely choice uncertainty. This uncertainty reflects uncertainties associated with first-person and third-person choice. This is particularly relevant for engineering with project stakeholders.
===Summary of Semantics Framework for Uncertainty===
Professional disciplines have gradually re-framed their understanding of uncertainty and, thru experience and analysis, have arrived at the following meta-knowledge concepts of uncertainty:
* '''Uncertainty and its proxy, error, have three components: [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic epistemic uncertainty], [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic aleatory uncertainty], and behavioral uncertainty.'''
* '''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty using non-dogmatic beliefs, which recognize the existence of specific criteria and information that would make the believer change their beliefs using coherent approaches such as probability.'''
* '''Knowledge professions develop knowledge of how uncertainty and its proxy, error, propagate through an ordered and normative series of observations or choices that meet testability criteria in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error. An error can then be described, explained, and reduced in ways that uncertainty cannot. This allows the professions to prioritize error reduction to achieve an optimized reduction of uncertainty and produce a "legitimacy" from the fruitfulness of its use.'''
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===Uncertainty versus Risk/Hazard===
Up to this point, nothing has been said about risk or hazard. Now, risk can be defined. Simply put risk or hazard are finite, logically distinct subsets of the broader uncertainty. Risk and hazard are currently defined interchangeably. First and foremost, risk is a specific subset of general uncertainty. Any arbitrary subset of general uncertainty can be defined as a risk if it meets the following criteria:
* Uncertainty is associated with the potential for material error when acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena.
* Uncertainty and its proxy, error, can be partitioned into logically distinct subsets associated with multiple-step execution or choice frameworks in physical environments linked to a unique or limited range of sequences.
**Subcriteria 1:Resulting errors will have varying impacts or consequences depending on where the error occurs and the degree of causal linkage or influence in the chain.
**Subcriteria 2:Potential errors can be sequenced and prioritized to achieve an optimized reduction of error or bias and, by proxy, uncertainty over time.
* Uncertainty and its proxies, error bias, and environments are isomorphic to coherent approaches such as probability.
* Uncertainty and its proxies, error, and bias are isomorphic to coherent approaches such as knowledge models capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria in 'reasoning under uncertainty.'
This is a definition of general risk. There is no differentiation for engineering or economics, medicine or insurance. There is no difference between a 'good' risk consequence and a 'negative' one. Redefining general risk into simpler terms gives the following:
:'''General Risk is a subset of general uncertainty that is associated with the potential for material errors and biases when sequentially acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena; isomorphic to probability measures and methods and capable of testability.'''
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==Logical Framework for Specific Risk Taxonomies==
As noted above, at the most fundamental level, uncertainty is ".....the subjective perception of ignorance."
<ref name="Han">Han, Paul KJ, William MP Klein, and Neeraj K. Arora. "Varieties of Uncertainty in Health Care A Conceptual Taxonomy." Medical Decision Making 31.6 (2011): 828-838.</ref>
At this point in the discussion, the focus has moved from discussing uncertainty to its framed subset, risk.
:'''Further, the discussion moves from general risk to a professional or discipline-specific risk [https://en.wikipedia.org/wiki/Taxonomy taxonomy]. Beyond this point, risk will be used in place of uncertainty.'''
Han et al. note that, in general, "(t) taxonomies are valuable not only in their comprehensiveness but in their coherent reduction of uncertainty to conceptually discrete elements."
Uncertainty (and, by implication, risk or hazard) taxonomies offer an approach or tool to more precisely identify and define ontological uncertainty so that it can be quantified, analyzed, and communicated. Viewed this way, uncertainty is not a single, monolithic phenomenon but "...multi-dimensional with theoretically distinct domains and constructs that are potentially measurable and related to different outcomes, mechanisms of action, and management strategies." <ref name="Han"/> Taxonomic schemes for classifying the different kinds of scientific and, by extension, engineering uncertainty require identifying the various kinds of potential error associated with descriptive scientific or engineering information and knowledge. An example of this is the following quote from a legal authority:
:"I would especially stress the need for an agency to disclose the uncertainty that surrounds its determinations. ''' And by uncertainty, I mean the agency's ignorance as well as its quantitative estimates of error."''' (Emphasis added)
<ref>Walker citing Bazelon, Science, and Uncertainty: A Jurist's View, 5 HARV. ENVTLt L. REv. 209, 212 (1981).</ref>
''' Given the necessity of acting in the face of enormous uncertainties
<ref>Vern citing Ruckelshaus, Science. Risk, and Public Policy, 221 SCIENCE 1026, 1027 (1983)</ref>,
discipline taxonomies must offer a clearly coherent and probabilistic approach to uncertainty that is sufficiently general in nature, logically distinct and exhaustive in scope and "...provide decision-makers with a foundation for understanding the nature of discipline information..."''' <ref name=
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=== Scientific Risk Taxonomies ===
The [https://en.wikipedia.org/wiki/Scientific_method scientific method] is a body of techniques for investigating phenomena in its environment, acquiring new knowledge, or correcting and integrating previous knowledge. Inherent in that process are uncertainties of many forms. Any one taxonomy of [https://en.wikipedia.org/wiki/Uncertainty scientific uncertainty] has largely been focused on statistical models used to assess and quantify sampling, errors, and parameters, although several different taxonomies are conceivable: note-24 [18]
====Environment-specific, Phenomena oriented, model-centric taxonomy of uncertainties====
** [https://en.wikipedia.org/wiki/Uncertainty_quantification#Sources_of_uncertainty '''Parameter uncertainty'''] the source of which is model parameters that are inputs to the computer model (mathematical model) but whose exact values are unknown and cannot be controlled, or whose values cannot be exactly inferred by statistical methods. Subsets of this type of uncertainty include but are not limited to:
***'''Experimental uncertainty''' is also known as observation error, which comes from the variability of experimental measurements. An example is repeating an experiment measurement several times using exactly the same settings for all inputs/variables and recording the variability. It is a subset of the larger uncertainty component, parameter uncertainty.
*** '''Parametric variability''' comes from the variability of the input variables of the phenomena model and is also a subset of the larger uncertainty component, parameter uncertainty.
** '''Structural uncertainty''', or model inadequacy, model [https://en.wikipedia.org/wiki/bias bias] or "[https://en.wikipedia.org/wiki/Systemic_bias systemic bias]", or model discrepancy, which comes from the lack of knowledge of the underlying true state of the phenomenon or its environment. It depends on how accurately a mathematical model describes the true state of the phenomenon or what is termed "model fitness". Due to the inherent nature of uncertainty in any knowledge body (scientific or engineering), models are only an approximation to reality. Therefore, any conclusions drawn from the model can be very misleading when the underlying basis is not plausible or lacks validity. '''note-25 [19]]'''
*** What is missing in this context is any concept of output variability due to the model itself. Part of this is due to the lack of recognition of "process" in scientific investigation. The concept of the scientific method started with a single investigator, such as Galileo or Michael Faraday, working in their laboratories. It has grown into planet and solar system scale experiments such as data gathered on planetary flybys such as Mars, Pluto, and Ceres to the hunt for the Higgs Boson. Ultimately, science has become more like engineering in the scale and complexity of its investigations and theory aggregates, such as the "Unified theory" of particle physics. Subsets of this type of uncertainty include but are not limited to:
*** '''Algorithmic uncertainty''', or numerical uncertainty, comes from numerical errors and numerical approximations per implementation of the computer model. Most models are too complicated to solve exactly. For example, the [https://en.wikipedia.org/wiki/Finite_element_method finite element method] or [https://en.wikipedia.org/wiki/Finite_difference_method finite difference method] [may be used to approximate a solution [https://en.wikipedia.org/wiki/Partial_differential_equation partial differential equation], which introduces numerical errors. Other examples are numerical integration and infinite sum truncation, which are necessary approximations in numerical implementation.
*** '''Interpolation uncertainty''' comes from a lack of available data collected from computer model simulations or experimental measurements. For other input settings that don't have simulation data or experimental measurements, one must interpolate or extrapolate to predict the corresponding responses.
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====Commentary====
A more comprehensive taxonomy of uncertainties that acknowledges knowledge, data, and linguistic uncertainties is:
*'''Epistemic uncertainty''' -[https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Epistemic uncertainty] or systematic uncertainty, in contrast, reflects limitations in the current “state of knowledge” underlying models themselves, originates from competing theories or models, is not readily quantifiable, and is manifest by subjective confusion or indecision. It includes uncertainty due to measurement limitations, insufficient data, extrapolations and interpolations, and variability over time and space. Epistemic uncertainty is uncertainty about "...some determinate fact ... because of a lack of complete information. '''cite_note-26 [20]]''' Epistemic uncertainty can be classified into six main types:'''cite_note-27 [21]]'''
*''' Measurement error''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Observational_error Measurement error] is uncertainty that manifests itself as (apparently) random variation in the measurement of a quantity. Repeated measurements will vary and demonstrate classic statistical behavior.
* '''Systematic error''' - [https://en.wikipedia.org/wiki/Observational_error Systematic error] occurs due to bias in the measuring equipment, model, analysis, observation, or sampling procedure. It is formally defined as the difference between the true value of the quantity of interest and the value to which the mean of the results converges as sample or data sizes increase. Unlike measurement error, it is not (apparently) random, and therefore, results subject to systematic error alone do not vary about a true value. Systematic error can result from deliberate judgment to exclude (or include) data, parameters, or models that ought not to be excluded (or included).
** "The only way to deal with systematic error is to recognize a bias in the process, model, or procedure and remove it. Systematic error, however, is notoriously difficult to recognize except on theoretical grounds. Corrections may only be applied when the magnitude and direction of the bias are known. Such corrections underlie the application of double-sampling methods in environmental science." (Regan, et. al., op. cit.)
*'''Natural variation''' - [https://en.wikipedia.org/wiki/Natural_process_variation Natural variation] or underlying process variation occurs in systems that "...change (concerning time, space, or other variables) in ways that are difficult to predict...across the full range of temporal and spatial values (or other related variables)." '''note-28 [22]'''
** This form of uncertainty is reduceable utilizing tools such as [https://en.wikipedia.org/wiki/Statistical_process_control statistical process control] and replication of experiments or observations.
*'''Inherent randomness''' - [https://en.wikipedia.org/wiki/Randomness randomness] or [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty aleatory uncertainty] exists because the system is"...in principle, irreducible to a deterministic one (the most well-known case is described by [https://en.wikipedia.org/wiki/Uncertainty_principle Heisenberg’s uncertainty principle] in quantum mechanics)." '''note-29 [23], note-30 [Note 7]'''
** Even if the argument is accepted that it is unlikely that any given physical system at some level is inherently random, it is important for any taxonomy to distinguish between systems that appear random due to incomplete or inconsistent information and those that are intrinsically random. (Again, see Regan (2002)).
*'''Model uncertainty''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling Model uncertainty] occurs in the process of generating a model as a conceptual representation of a particular phenomenon to create a [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling#Overview simplified reflection of reality]. This selectivity of factors and variables creates uncertainty in at least three ways. (Regan (2002))
** '''Variables and processes''' that are regarded as relevant often represent a trade-off between system knowledge and assumed states and model objectives;
** '''Models''' depict observed processes using logical or mathematical constructs based on underlying theories about system states or dynamics using continuous equations to describe discrete processes.
** '''Curve fitting''' (including interpolation and extrapolation) with mathematical expressions using model variables where inputs are discrete data points.
** Regan (2002) argues that '''model uncertainty''' is "...notoriously difficult to quantify and impossible to eliminate ...(and)...(t)he only reliable way of determining how appropriate a model is for prediction is to perform validation studies."
** Subjective judgment occurs due to the interpretation of scarce or error-prone data.
*** The only way to address this type of uncertainty is to "...assign a degree of belief about an event in the form of a subjective probability." (Regan (2002))
*Linguistic uncertainty - Linguistic uncertainty, or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. '''note-31 [24]], note-32 [25]]'''
** '''Context dependence''' is uncertainty concerning the context in which a statement is to be understood;
** '''Ambiguity''' occurs when words have multiple meanings, and further reduction to a single meaning is not logically possible in a given context.
** '''Underspecificity''' occurs in the presence of "unwanted generality" or multiple interpretations, and reduction to a smaller set or even a single interpretation is not logically possible.
*Stochastic or statistical uncertainty - Stochastic or statistical uncertainty, which is sometimes known as [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Aleatoric uncertainty] as well as the subject of [https://en.wikipedia.org/wiki/Stochastic_control stochastic control] pertains to the parameters of a risk model, originates from sampling or measurement error, and can be quantified and mathematically expressed (e.g., using confidence intervals).
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== Uncertainty Taxonomies in a Legal Environment ==
From a legal perspective, a definition of taxonomy is dictionary-based, such as "the systematic distinguishing, ordering, and naming of type groups within a subject field." given by Walker (op. cit.). Such legal concepts of scientific or engineering taxonomies are not required to be epistemically complete. Still, they must cover "the most significant aspects of ...(current, good discipline practices) ... and of the descriptive information commonly encountered by decision-makers" in a logically distinct manner. (Walker, 1991, p. 571) These taxonomies must be sufficient to catalog the types of uncertainty associated with descriptive assertion, including the critical subset of cause and effect assertions. This legal/decision-maker framework has presented descriptive uncertainty as having six components (Walker, 1991). Although not part of the Walker taxonomy, underlying the scheme is linguistic uncertainty, or "vagueness," as a source of uncertainty, including uncertainties due to context dependence, ambiguity, and under-specificity. '''note-33 [26]]''' Arguably, these uncertainties underlie the most basic element in the taxonomy.
* '''Linguistic '''- or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. (Regan, 2002)
* '''Conceptual'''- the definition by choice and design of descriptive concepts or variables to be used as [https://en.wikipedia.org/wiki/Predicate_(grammar) predicates] where the subject of an assertion identifies what is being discussed and the predicate provides information about the topic or characterizations such as what the subject is, what the subject is doing, or what the subject is like (Wikipedia).
* "Conceptual uncertainty, or the potential for conceptual error, arises whenever predication occurs. Whenever a concept is used to describe something, using certain concepts instead of others begins to structure how we understand the object, event, or instance under discussion. Predication or conceptualization generates useful information about things, but it also can inhibit our ability to think about those things with concepts other than those selected. The concepts used may not be the most fruitful or the best designed- either for scientific purposes or for making wise, fair, effective, and efficient decisions." (Edited for reading, Walker, 1991)
* Similarly, choice and design of descriptive variables enhance and restrict data set membership, creating potentially incomplete, incompatible, or inconsistent data sets, "...the classification categories employed, and the relationships among those categories (nominal, ordinal or scalar)." '''note-34 [27]]'''
* Concept uncertainty '''note-35 [28]''' like systematic error discussed above is notoriously difficult to recognize, except on theoretical grounds, and the magnitude and direction of the bias are known.
Both terms could be analogized to that of [https://en.wikipedia.org/wiki/Accuracy_and_precision#Terminology_of_ISO_5725 accuracy] in [https://www.bipm.org/en/publications/guides/ ISO 5725] which looks at the ability of the concept or system's ability to produce results that are proximate to the "true" value or state of the phenomenon. Conceptual uncertainty and systemic bias are related to another measure of uncertainty: validity. Walker has defined validity as the ability of a concept to quantitatively describe and explain what the phenomenon objective "truly" is. Walker also ties the concepts together when he states, "Validity concerns the "accuracy" of the measurement data, not its precision." (Walker, 1998) 
Additionally, conceptual uncertainty and associated validity issues could be propagated throughout this taxonomy. Every component of action and decision in the face of uncertainty raises validity issues. Walker recognized this when the author defined epistemic choice for concepts used throughout the other uncertainty components or layers.
* '''Measurement''' uncertainty or Misclassification error- the application of the underlying concepts or variables to specific, individual cases and the uncertainty of the reliability (Walker, 1998) of the resultant data. (It is important to remember that assessment observation and measurement are used interchangeably in this analysis.) Thus, an assessment method or procedure is considered "reliable" or "precise" in the scientific terms of ISO 5715 if it repeatedly produces consistent results.
* Measurement uncertainty is logically distinct from conceptual uncertainty, which can only occur after any conceptual uncertainty has been established. Still, misclassification can occur even when the conceptual uncertainty has been minimized. Conversely, underlying errors or errors made in measurement or classification propagate thru the reasoning chain and reduce the quality or value of the final result.
* While uncertainties associated with quantitative measurements or assessments can be statistically evaluated and described, uncertainties related to qualitative assessments are more problematic.
**Working through the taxonomic chain from base concept to causal explanation requires amassing and integrating qualitative information and quantitative data into input parameters for mathematical models. Developing procedures for producing consistent, repeatable sets of information from assessments that minimize conceptual error or systemic bias requires professional efforts to "validate" such methods, models, and judgments at all layers in the process. Validation as a process is something that civil engineering as a practice needs to embrace more frequently.
* '''[https://en.wikipedia.org/wiki/Sampling Sampling]'''- in the classical statistics sense, is the selection of a limited subset of a larger population to use as a basis for making estimates about the population in the form of attributes, proprieties, or parameters. The resulting information would be determinative in making forecasts about future population sampling or possessing a desired amount of predictive power. The uncertainty about the ability of this information to correctly predict future samples is termed sampling error.
* '''Modeling'''- Modeling uncertainty arises whenever a claim is made that out of a class of candidate relationships and variable pairings, one variable pairing inclusive of constants is chosen that possesses a particular or persistent mathematical relationship to another variable X. Modeling errors arise in selecting the wrong variable pair and constants or by incorrectly specifying its constants. (Walker, 1991, pg. 599)
** '''Causal'''- In causal modeling and analysis, the relationship between causal variables is not strictly a mathematical function. Still, it makes testable predictions and explains "...how a system of variables works, why a system works the way it does, or why it makes sense to think of certain variables as a system" at all." (Walker, 1991, pg. 609)
** '''Epistemic'''- the choice of interpretations for fundamental, logical concepts used throughout the other components or layers.
The components of this uncertainty taxonomy can combine in different ways to "...produce different aggregate uncertainties." '''note-36 [29]'''
:''' Only the scientific and engineering professions possess the capacity to develop and validate procedures and models that combine the different kinds of qualitative '''note-37 [30]''' and quantitative information and their associated potential for error into an aggregate measure or "scalar variables" of uncertainty.'''note-38 [31]]
Scalar variables are quantitative variables whose categories are related by some measure of the relevant property's incremental frequency, degree, or amount. (Walker, 1991) Examples are Modulus of Elasticity, Compression stress in elastic materials, etc.

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=== Economics Uncertainty Taxonomies ===
Like scientific concepts of uncertainty, economics is environment-specific (in this case, read sector of economy or markets), phenomenon-oriented (in this case, read human activity and manufactured phenomena such as firms and markets guided by human judgment and decision), and model-centric.
:''' Economics is not interested in all forms of uncertainty, only specific, meaningful subsets possessing casual connections with material impacts, often labeled as 'risks'.'''
One of its earlier writers ([https://en.wikipedia.org/wiki/Frank_Knight#Life_and_career Knight], 1921, 1948, 1957) attempted to define "uncertainty" as the presence of "defects of managerial knowledge" (or [https://en.wikipedia.org/wiki/Knightian_uncertainty knightian uncertainty] and thus risk centric) as risk that is immeasurable, not possible to calculate.(Wikipedia) Risk in this sense was defined as "the ordinary risks of business activity which can ... be reduced... by applying the insurance principle." Knight does acknowledge that risk is a specific and unambiguous subset of uncertainty. (Risk, Uncertainty and Profit, 1957, pg. 19) For Knight, measuring uncertainty meant measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences, which Knight referred to as "the imperfection of knowledge". (Risk, Uncertainty and Profit, 1957, pg. 197) One of Knight's many contributions to this analysis was recognizing information and knowledge's role in economic activity.
:"''(W)e are concerned only to emphasize the fact that '''knowledge is in a sense variable in degree and that the practical problem may relate to the degree of knowledge rather than to its presence or absence in toto.''' We live only by knowing something about the future, while the problems of life, or conduct at least, arise from knowing so little. This is as true of business as of other spheres of activity. The situation's essence is action according to opinion, of greater or less foundation and value, neither entire ignorance nor complete and perfect information, but partial knowledge. If we are to understand the workings of the economic system, we must examine the meaning and significance of uncertainty; and to this end, some inquiry into the nature and function of knowledge itself is necessary.''" (Knight, pg. 199) Emphasis added
Knight also reinforced the difficulties in classifying uncertainties when he analyzed life insurance. In this case, accidental death was an uncertain phenomenon compared to sickness and accident, where an "...objective description and classification of cases was impossible..." (Knight, pg. 248) Another factor that made uncertainties ineligible as risks and, therefore, uninsurable because they were unclassifiable was the exercise of judgment in making decisions by the businessman. (Knight, pg. 251)
This is a simple classification of economic uncertainty and, as an idealization, is philosophically controversial. '''note-39 [32]'''
* Uncertainty and information about the economic environment are distinct from uncertainty about others’ behavior or choices.
* Risk as a specific subset of uncertainty implies that "...all possible acts are known, all possible outcomes arising from each act are known, and it is possible to assign probabilities to each act." '''note-40 [33]'''
Like the economic [https://en.wikipedia.org/wiki/Theory_of_the_firm theory of the firm], an economic theory of uncertainty seeks to explain and predict the nature of a specific subset of uncertainty (in this case, risk relating to physical phenomena and human activity), including its behavior, structure, and relationship to the universal uncertainty of physical phenomena. In this sense, uncertainty, like firms and markets, can establish price equilibriums under the right circumstances different from those that might have occurred in either of the other two. Such a theory of uncertainty offers a partitioning scheme that decomposes uncertainty into distinct and meaningful subsets, of which risk is a significant one. It also explains how the presence and variability of knowledge cause or " drive" economic consequences. Unlike physical phenomena, economic activity creates artificial objects such as products, firms, mediums of exchange, markets, economies, and knowledge about those activities. The economics of uncertainty exists as an alternative to "certainty" models that assume away imperfect knowledge and unknown choice/judgment preferences when it is more efficient for economic decision-making. This also allows the introduction of the logical concept of "hazard" versus "risk". Frank used hazard extensively in Risk, Uncertainty, and Profit, but not to the extent that one could say it was interchangeable. An example of this is in the term "[https://en.wikipedia.org/wiki/Moral_hazard Moral hazard]," where a firm could engage in "riskier" and potentially "uninsurable" behavior if the information were transparent, and, therefore, insurance coverage of losses is limited. note-41 [34]], note-42 [35]]

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=== Uncertainty and Risk in Policy and Regulatory Environment ===
:''"Hume's 'just reasoner', when faced with a difficult public policy decision in current times, would probably commission a risk analysis. But how could they incorporate 'a degree of doubt, caution, and modesty?"'' '''note-43 [36]'''
The problem of "decision-making in the face of uncertainty" is how to regulate based on incomplete information that has the potential to be materially inaccurate. A contextual assumption is that we need to evaluate decision rules for dealing with uncertainty. As a social enterprise, risk regulation, whatever its substantive objectives, should be as '''effective, efficient, and equitable''' as practically possible. These "three E's" form a set of "process objectives" or "meta-goals." From the uncertainty standpoint, causal information can be usefully divided into two major categories: information about groups and information about individuals.

Each category, group, and individual has its distinctive types of inherent uncertainty. These are logically distinct, generally independent, and cumulative, contributing to '''(?????)'''

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=== Engineering Uncertainty Taxonomies ===
Like scientific and economic concepts of uncertainty, engineering is environment-specific, phenomena-oriented, model-centric, external choice, and driven by decision-making in the face of uncertainty. Like economics and unlike science, engineering is not interested in all forms of uncertainty, only those with determinable and material consequences. Like economics, engineering conceives risk as a specific and unambiguous subset of uncertainty determined by measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences. Also, engineering recognizes the role information and, by extension, knowledge plays in constructing the built environment.
'''note-44 [37]]''' Like economics, engineering differs from economics and scientific uncertainty in its interest in behavioral uncertainty.
*"Behavioral or interaction uncertainty is how individuals or organizations act or interact. Behavioral uncertainty arises from four sources: design uncertainty, requirement uncertainty, volitional uncertainty, and human errors." '''note-45 [38]]'''
** A design uncertainty is a choice among alternatives over which an individual or group of individuals exercises direct control but has not yet decided upon.
** Requirement uncertainty includes parameters of interest to and determined by the stakeholder, independent of the engineer or designer.
** Volitional uncertainty is uncertainty about what the subject him/herself will decide. Other people’s future actions and conduct are not entirely predictable, particularly when dealing with other organizations.
** Human errors occur during the development of a system or project due to blunders or mistakes by an individual or individuals.

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==Practice Frameworks for Uncertainty and Risk in Civil Engineering Practice==
===PMIBoK===
PMI does not define uncertainty, but the PMI Body of Knowledge uses the term in 45 places (uncertainty) and as describing properties in 8 places (uncertainties). [See Note 8] PMI does define "risk" and uses the term over 1,300 times in the same document. In its glossary, PMI also defines terms such as "threat" and "opportunity" but not events.[391 The BoK contextually defines uncertainty when it states that project risk has its origins in the uncertainty present in all projects. (Op. Cit., pg 309) 
'''Uncertainty''' is contextually defined and taken as having the property of affecting project execution and the ability to meet stakeholder expectations. Uncertainty is more than the sum of all individual risks (known and unknown). The nature of this uncertainty is not defined, only its capacity to affect something else or its properties. 
'''Risk''' is defined as " ... an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives." (Ibid.) Beyond this formal definition, PMI adds context when, in its risk management section (Sec. 11 ), the BoK states that risk is a caused event or condition with multiple causes and impacts. The BoK even offers specific techniques for mapping cause-and-effect relationships and influence diagrams. (Sec. 11.2.2.5) Not as evident but equally important is the recognition that not only is risk caused but the impact is "triggered". The risk trigger is an event or situation that signals that it is about to occur (Glossary, pg. 566). Risk conditions are factors that affect or otherwise contribute to risk, such as project stakeholders. Risk implicitly retains some residual element of probability in it, or a value of less than 1.0 with risk that approaches a level of near certainty is termed "issues" or "realized risk" .(Op. Cit., pg 309). PMI also links risk to underlying variations in project outcomes. (Ibid.) Risk is based upon data and information which can be assessed as to its quality (Sec. 11.3.2.3). This analysis looks to determine the value of the information and examines the degree to which the risk information is understood as well as its " ... accuracy, quality, reliability and integrity ... " (Ibid.) Part of that is understanding the relationship between occurrence and impact as outlined in PMl's Figure 11-10 Risk Impact Matrix and developing a model for prioritizing risk products and setting the threshold for action.
====Commentary:====
For defining and identifying risk, PMI notes in Sec. 11.2.2.4 that " ... Every project and its plan is conceived and developed based on a set of hypotheses, scenarios, or assumptions." Part of the risk analysis process is identifying risks to the project, such as inaccuracy, instability, inconsistency, or incompleteness of assumptions. Similarly, the quality of management plans, as well as their consistency with others and the project objectives and assumptions are 11 •• .indicators of risk in the project." (Sec. 11.2.2.1) 
Similarly, the role of engineering economics in risk is embedded or implied in the PMI definition of risk when the BoK discusses risk identification as a process of assessing which risks out of the total risk exposure for the project "may affect" the project and distilling their characteristics. Embedded in this statement is the concept that not all risks can cause economic impacts (positive and negative) to the project. Also embedded is the requirement to understand risk characteristics to identify project risk (Cost and schedule). Lastly, risk identification is an iterative process of incrementing project risk documentation, much like project scope, cost, and schedule documentation. 
Lastly, PMI acknowledges the complex constitution of risk aggregates or the "sum" of all risks in its material, but is it a sum? Risk information has descriptive and explanatory content. These are knowledge and meta-knowledge components. Understanding project assumptions is project-specific knowledge, but identifying risks from assumption inaccuracy, instability, inconsistency, or incompleteness requires professional or program meta-knowledge. 
'''Uncertainty exists in all projects, but risk, a subset of uncertainty with material economic impacts, is a key meta-knowledge element for civil engineering practice.'''
===Software Development Process- Risk Focused===
The Unified Process requires the project team to focus on addressing the most critical risks early in the project life
cycle. The deliverables of each iteration, especially in the Elaboration phase, must be selected to ensure that the greatest risks are addressed first. <ref> Booch, Grady, Ivar Jacobson, and James Rumbaugh. "The unified software development process."
Reading: Addison Wesley (1999).</ref>
===American Society of Civil Engineers (ASCE)===
ASCE does not define uncertainty, but the ASCE CE Body of Knowledge (CEBoK) uses the term in 22 places (uncertainty) and describes properties in 13 places (uncertainties). 
The CEBoK does not use the phrase "uncertainty and risk" as PMI and AACE do in their publication. ASCE reverses the two (Risk and uncertainty) in twelve places regarding technical outcomes (Outcome 12). A fundamental difference between PMI and ISO is that risk is considered a subset of uncertainty. ASCE does not define "risk" but uses the term 25 times in the same document. Almost all are in the context of variation of design parameters and not in the classical context of cost, schedule uncertainty, and risk.
====Commentary:====
...
=== American Association of Cost Engineers (AACE) ===
AACE defined uncertainty, risk, and other related terms in its Risk Management Dictionary. <ref>Risk Management Committee uRisk Management Dictionary, Cost Engineering Vol. 37/No. 10, OCTOBER 1995. </ref>
AACE's overall strategy was to define risk and other terms that stem from base uncertainty. AACE first defined uncertainty as .... "(t)he total range of events that may happen and produce risks (including both threats and opportunities) affecting a project (see opportunities, events, conditions, risk, and threats" where the following sub-definitions apply:
** Biases--A lack of objectivity based on the individual's position or perspective. Systematic and predictable relationships between a person's opinion or statement and his/her underlying knowledge or circumstances. Note: there may be "system biases" as well as "individual biases."
** Condition (Uncertain Condition)-Any specific identifiable circumstance (such as the rate of inflation or the quality of labor available) that might affect the outcome of the project
** Event (Uncertain Condition)-is a specific identifiable action (such as a large government project being started in the same labor area as your project) or an act of nature that might happen and that (if it does happen) could affect the outcome of the project.
** Opportunities are Uncertain events that could improve the results or improve the probability that the desired outcome will happen
** Threats are Uncertain events that are potentially negative or reduce the probability that the desired outcome will happen.
*** AACE defines Risk as an "ambiguous term" (sic) that is synonymous with uncertainty or a negative subset such as "threats", or an overall negative impact of all possible uncertainties.
====Commentary:====
...
==Limitations of the definition==
...
==Working definition==
...
==Beneficial outcomes==
...
==See also==
Wiki articles on the project.

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==Notes==
<references group="note" />
<li> Implicit in this definition of uncertainty as a "state of' is ... </li>
: " ... a conceptualization of uncertainty as a subjective, cognitive experience of people--a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one's lack of knowledge, without which one could not feel uncertain; '''uncertainty is a form of "meta-cognition" ... ( or alternatively, its main component, meta-knowledge) ... -a knowing about knowing.'''" (Han, 2011, op. cit., Emphasis added)
</ol>
<ol start="5">
<li> Cox wanted his system to satisfy the following conditions:</li>
: Divisibility and comparability- The plausibility of a statement is a real number and depends on the information we have related to the statement.
: Common sense - Plausibilities should vary sensibly with the assessment of plausibilities in the model.
: Consistency - If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). -preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf
</ol>
<ol start="6">
<li> The utility of existing taxonomies of uncertainty in civil engineering and, by extension, "risk" has been unsatisfactory for this very reason. This is particularly true in cases where the source of the uncertainty is conflicting versus incomplete information. See Regan, et. al.,"A taxonomy and treatment of uncertainty for ecology and conservation biology" (2002)
for a survey of scientific taxonomies for uncertainty.</li>
<li> Regan et al. argue that " ... genuine examples of this kind of uncertainty are hard to find. Even classic cases of random experiments like coin tosses and the throwing of dice are deterministic; it is just that we do not have enough information about the dynamic processes and initial conditions to make any sensible estimates about the outcomes. Such processes are, for all intents and purposes, inherently random, but they are not genuinely inherently random. For similar reasons, complex systems such as ecosystems and weather patterns are unlikely to be inherently random. Similarly, chaotic systems are entirely deterministic. They are unpredictable because the deterministic processes generating them and the relevant initial conditions are hard
to fully specify (see Stewart 1989, Sugihara et al. 1990)." (Regan (2002) </li>
<li> The CEBoK uses the phrase "uncertainty and risk" twice in reference to scheduling (Section 6.5,2) and cost estimating (Section 7.2,2) as well as reversing the two (Risk and Uncertainty) also in two places in discussing project life cycle (both in Section 2.4.1) </li>
</ol>

==References==
<references />

Uncertainty and Risk in Civil Engineering Practice

2024-11-24T16:57:56Z

WikiSysop: /* Logical Framework for Specific Risk Taxonomies */

'''Life is short and the art long; the occasion instant, experiment perilous, decision difficult.''' (Hippocrates as quoted in Fox,)
:''See also [[Project Definition]] , [[Project Life Cycle and Phase Models]] , [[Project Delivery Methods]]''
:''See also [[Technical Outcomes for Civil Engineering:Risk and Uncertainty]]''
==Basic Considerations==
As human beings,'' ... being alive means seeking opportunities and taking risks.'' As knowledge professionals
living in the 21st century, this means coping with an increasingly complex number of uncertainties for humans
living in this environment. We seek to understand better how these uncertainties can be characterized and
managed. The essence of this article and the experience for engineers in general and civil engineers in particular is
that manageable uncertainty is, by definition, termed risk, and its kindred cousin is termed hazard. This causes us to experience the
:" ''... human dread of and fascination for risk and the increasingly important role of risk analysis within societies ...'' " <ref name="McDaniels"> McDaniels, Timothy, and Mitchell Small. Risk analysis and society: an interdisciplinary characterization of the field. Cambridge University Press, 2004, page 1 </ref> (Ibid.)
and, by extension, civil engineering. McDaniels et al. argue that risk management has been
fundamental to our social and governance development for the past 10,000 years. The scale and shape of the
uncertainties faced in this period shaped the societies that have developed today. The central thrust of this effort
over the centuries has been to reshape and re-frame our understanding and conception of uncertainty from one of
complete unknowing and simple acceptance as our fate in life to one of management (cf "''Against the Gods''"
concept in Bernstein's book <ref name = Bernstein">Bernstein, Peter L., and Jesse Boggs. Against the gods. Simon & Schuster 1997.</ref>.
: ''All the knowledge professions and disciplines have struggled with managing uncertainty, for it is impossible to manage unbounded uncertainty.'' <ref group="note">See McDaniels et al. 2004 for an extensive discussion and bibliography on the historical development of Risk Analysis and some key milestones in risk analysis in the 20th century.</ref>
As outlined below, professions such as civil engineering have successfully acted in the face of uncertainty. It lies in the profession's ability to reduce a wide variety of uncertainties into increasingly smaller and crucially bounded subsets that can be managed. These are called 'risks. More recently, this process has evolved, and individual disciplines such as Civil Engineering (CE) have developed their knowledge and models to perform risk analysis. Risk is also taught as a distinct discipline and specialty practice within civil engineering. Still, it has some unique features that set it apart from other more classical practices within CE. As such, it is one of the first of some very specialized CE practices that use knowledge about civil engineering knowledge, or meta-knowledge. Other examples of civil engineering metaknowledge are project controls and quality controls.
==Semantic, Epistemic and Logical frameworks==
The semantic, epistemic, and logical frameworks for uncertainty and risk have several dimensions and layers of logical frameworks. The semantics problems include interchangeable usages for risk and uncertainty and hazard, uncertain and imperfect, and a lack of definitional material or context. The semantical scheme for this article will be to proceed from the distinction between certainty and uncertainty through marginal refinements and reductions of uncertainty up to the point of causal uncertainties. Beyond this point of semantics will be the logic frameworks or the subject of taxonomies of professional knowledge. First, it is about simple, testable phenomena, and then, it moves on to complex taxonomy schemes for artificially constructed phenomena or engineering projects.
<ref group="note">TBD</ref>
==Semantics framework==
===Certainty and Uncertainty===
''''' There is no such thing as absolute certainty, but there is assurance sufficient for the purposes of human life.''''' ([https://en.wikipedia.org/wiki/John_Stuart_Mill John Stuart Mill]) 
'''''If you tried to doubt everything, you would not get as far as doubting anything. The game of doubting itself presupposes certainty.''''' ([https://en.wikipedia.org/wiki/Ludwig_Wittgenstein Wittgenstein] # 115 from On [https://en.wikipedia.org/wiki/On_Certainty Certainty]) 
It is important to note that the references to epistemic or epidemiological knowledge in this article are assumed to relate to three forms of knowledge, namely:
* Knowledge that ( [https://en.wikipedia.org/wiki/Descriptive_knowledge descriptive or declarative or propositional knowledge])
* Knowledge how ( or "[https://en.wikipedia.org/wiki/Procedural_knowledge know-how]"), and
* [https://en.wikipedia.org/wiki/Knowledge_by_acquaintance Knowledge by acquaintance].
'''Certainty''' has been defined as "an epistemic property" of knowledge in all its forms and the state of our beliefs about that knowledge.
<ref>Certainty, Stanford Encyclopedia of Philosophy, accessed on September 12, 2015, at http://plato.stanford.edu/entries/certainty/#ConCer </ref>
Certainty about any belief about knowledge implies that it is not subject to doubt or skepticism. This immunity to criticism can be dogmatically based, emphasizing the importance of a propositional-based sense of truth over experiential, sensory perceptions. <ref>Definition of [https://en.wikipedia.org/wiki/Dogmatic_theology Dogmatic theology or belief], accessed at Wikipedia</ref>
: '''The demarcation line between dogmatic and non-dogmatic beliefs lies in the presence and recognition of specific criteria and information that would make the believer change their beliefs.'''
An empirical framework of [https://en.wikipedia.org/wiki/Testability testability] and [https://en.wikipedia.org/wiki/Falsifiability falsification] is required to recognize this and limit dogmatism. For example, one could argue that people would change their minds if God asked them to. Similarly, one could construct a concept of epistemic as opposed to dogmatic belief certainty as the ability to know anything that one chooses to know and can be known or [https://en.wikipedia.org/wiki/Omniscience inherent omniscience].

A second kind of certainty is epistemic, when conviction reflects the highest possible support for a belief. In this sense, knowledge is separate from beliefs, although someone may have beliefs about a property, such as certainty of knowledge. Logically, it has been shown that there will be unprovable statements within the system for any such knowledge system. Secondly, the knowledge system cannot demonstrate its own consistency. ([https://en.wikipedia.org/wiki/Gödel%27s_incompleteness_theorems Gödel's incompleteness theorems])
: '''Certainty, in real life, is useless or often damaging (the idea is that "total security from error" is impossible in practice, and a complete "lack of doubt" is undesirable)''' ([[https://en.wikipedia.org/wiki/Certainty Physicist Carlo Rovelli]])
'''Uncertainty''', on the other hand, arises immediately in the slightest amount of doubt or criticism.
<ref group="note"> The Merriam-Webster dictionary defines uncertainty as “the quality or state of being uncertain,” which is something of a circular definition. Likewise, one could define uncertainty as the state of not being certain.

Synonyms are distrust, doubt, misgiving, mistrust, reservation, skepticism, and suspicion. Another writer added indefinite, indeterminate, not certain to occur, problematical, unreliable, untrustworthy, unknown beyond doubt, dubious, doubtful, not clearly identified or defined, not constant, variable, and fitful to the list.
:(Han, Paul KJ, William MP Klein, and Neeraj K. Arora. "Varieties of Uncertainty in Health Care: A Conceptual Taxonomy." Medical Decision Making 31.6 (2011): 828-838.)
Han et al. noted that any definition of uncertainty clearly encompasses "...numerous types, sources, and manifestations of uncertainty, and ...(any) ...useful working definition of uncertainty needs to specify the concept underlying these varied meanings of the term."</ref>
Implicit in this definition of uncertainty as a “state of” is ...
"...a conceptualization of uncertainty as a subjective, cognitive experience of people—a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one’s lack of knowledge, without which one could not feel uncertain; uncertainty is a form of “meta-cognition” ...(or its main component, meta-knowledge)... —a knowing about knowing." (Han, 2011, op. cit., Emphasis added)
Similarly, uncertainty could be defined as "...any departure from the unachievable goal of complete [https://en.wikipedia.org/wiki/Determinism determinism]."
<ref name="Walker1">Walker, W. E., Harremoës, P., Rotmans, J., Van Der Sluijs, J. P., Van Asselt, M. B., Janssen, P., & Krayer von Krauss, M. P. (2003). Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support. Integrated assessment, 4(1), 5-17.</ref>
Reasoning under uncertainty is very different than performing the same under certainty. In reasoning under certainty, one has complete knowledge and deduces without doubt and equally important, without limitation, thereby concluding free from error. Reasoning under uncertainty, one works in a state of incomplete, inconsistent, and limited knowledge; doubts cloud any statements or assertions and thereby taint any deductions/inferences resulting in the potential for error. <ref group="note">Implicit in this definition of uncertainty as a “state of” is ...
"...a conceptualization of uncertainty as a subjective, cognitive experience of people—a state of mind rather than a feature of the objective world. The defining feature of this state, furthermore, appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one’s lack of knowledge, without which one could not feel uncertain; uncertainty is a form of “meta-cognition” ...(or alternatively, its main component, meta-knowledge)... —a knowing about knowing." (Han, 2011, op. cit., Emphasis added)</ref>
:'''Knowledge professions reason in a state of incomplete, inconsistent and limited knowledge with doubts that cloud any statements or assertions and taint any deductions/inferences resulting in the potential for error.'''
:''' It is also important to note the semantical and logical correlation between 'reasoning under uncertainty' or 'acting in the face of uncertainty' with the 'potential for' or 'presence of' error. The presence of uncertainty is invariably linked to the potential presence of error.''' The [https://en.wikipedia.org/wiki/Contingency_(philosophy) contingent] nature of uncertainty logically implies the contingent nature of error.
:'''Knowledge Professions use error as a proxy for uncertainty such that within discipline knowledge frameworks, uncertainty can be managed.''' The rationale for this belief is that error can be reliably described, quantified, explained, and ultimately reduced in ways that uncertainty cannot.

'''Uncertainty''' implies a range of variation that is impossible in certainty. Concepts and definitions of uncertainty are unbounded and vast but start from the point of complete and total ignorance. In describing uncertainty, statements can range from complete ignorance to relatively high degrees of belief that there is less potential for error in our reasoning or action. The latter is based upon confidence in justified knowledge, favorable past experience, and reliable parameters/models. One can't make that statement about ranges in certainty. If certainty, by definition, precludes doubt of any type or nature, then how could that be graduated to any degree? 
Uncertainty, on the other hand, can be reduced through disciplined efforts to acquire knowledge. In short, we can reduce or even manage uncertainty (to a degree); how can certainty be improved? Are there higher degrees of perfection? The answer is no. We reason from an uncertain starting point. Yet this effort presumes, as Mill and Wittgenstein postulated, that we can develop or acquire an increasing degree of belief or conviction that there is less potential for error in our reasoning or action. This position assumes that some way exists to identify what a lesser degree of uncertainty would look like.
:'''Knowledge professions acquire an increasing belief or conviction that there is less potential for error in their reasoning or action based on justified knowledge, favorable past experience, and reliable parameters/models.'''
Uncertainty is, in some form, amenable to description and explanation through observation and analysis. It is explainable and predictable to such a degree that it interests professionals such as scientists and engineers. Professional knowledge of this type has explanatory or predictive power, albeit limited in scope and application to relevant subjects such as physical objects or phenomena and functionality.
:'''The very act of reducing the scope of uncertainty to a limited set of phenomena implies choice and, ultimately, the decision to act in the face of such uncertainty, but acting or judging in this manner adds more value than doing the same under relative ignorance.'''
'''Uncertainty''', as described above, implies a hybrid nature. Examples of this are economic or decision-theoretic applications. In economics, [https://en.wikipedia.org/wiki/Perfect_information perfect information] allows one within the limiting framework of perfect competition in economic games to make decisions with 'perfect knowledge.' The crucial difference here is the distinction between '[https://en.wikipedia.org/wiki/Perfection perfection],' broadly, a state of completeness and lawlessness, and 'perfect, which can be thought of as an analytical limit that is approachable but can never be attained. Arbitrarily delimiting the realm of knowledge into defined, finite boundaries allows the simulated production of perfect choice, assumed crucially, to be free from error. The hybrid nature of this form of 'simulated' certainty produced within boundaries defined by assumptions and, therefore, clouded by doubt is itself a form of uncertainty. The importance of this is the semantical and logical association that exists between certainty and perfection versus the hybrid concept of a perfect anything, whether knowledge, infraction, competition, etc., or in the case of engineering, elastic, permeable, conductive, etc., being contained within an admittedly imperfect matrix of uncertainty.
:'''Simply put, a finite, heavily bounded piece of uncertain knowledge can be improved through the simulated use of [https://www.merriam-webster.com/dictionary/assumed assumedly] perfect information, but the perfection of knowledge cannot be attained or simulated.'''
Engineering and Economics, for example, simulate finite elements of perfect knowledge or information, heavily bounded with assumptions and a range of applications. By doing so, these professions reduce the bounds and variability of uncertainty and thereby manage it. Using these hybrid models to acquire knowledge takes place in an economy (regulated or institutional) and environment (transparent and structured), which puts pressure on the various professions to recognize and use qualitative and quantitative knowledge better to reduce uncertainties in their activities. The challenges in this reflect the nature of the profession's or discipline's knowledge (e.g., scientific, engineering, or medical), the policies and structure of the relevant professional knowledge bodies, process objectives, constraints, and "the elusive demands of politics." <ref>Walker, Vern R. "The siren songs of science: toward a taxonomy of scientific uncertainty for decisionmakers." Conn. L. Rev. 23 (1990): 567.</ref>
One common theme runs through all of these efforts to address the presence of uncertainty in professional activities. 
:"The available scientific information upon which a decision must be made is almost always a mixture of engineering knowledge and uncertainty. Regardless of the information or methodology, there is the potential for error. This is true regardless of the relevant science, whether archaeology or aeronautics, economics or engineering, pharmacology or toxicology or epidemiology. Achieving the best social decisions requires not only understanding and using what we know but also appreciating and weighing the extent of our uncertainty. In making the best use of ... information in ... decision-making, it is still true that the beginning of wisdom is knowing what it is we do not know." (Vern, Op. cit., Reformatted and engineering substituted for scientific) 
'''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty to develop explanations, calculate, and make predictions. This professional knowledge converges on but never reaches certainty, producing a "legitimacy" from the fruitfulness of its use.''' <ref>Randomness Is Unpredictability, Antony Eagle, The British Journal for the Philosophy of Science, Vol. 56, No. 4 (Dec., 2005), pp. 749-790 </ref> 
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=== Bias and Uncertainty ===
Up to this point, the concept of error, as outlined in uncertainty, could easily be simulated by a truly random variable. There should be no detectable differences around the unseen or specified statistical mean. However, several instances in experience point to a tendency towards one extreme or a pattern of error, a collective that is in itself an error of errors, namely [https://en.wikipedia.org/wiki/Bias bias].

Arguably, this could be part of the choice uncertainty discussion below, but it makes more sense to be looked at on its own. Acting in the face of uncertainty is an exercise of personal knowledge or experience. Whether using professional knowledge or individual experience, the potential is there to make a series of choices that, in some instances, make errors more likely than they would be if considered in the context of a statistical analysis.

This tendency for an individual to make the same error in a predictable or repeatable manner is termed bias. Personal experience, beliefs, knowledge, data, and models all have inherent built-in factors, errors, or defects that create predictable error patterns when applied in decision-making. Walker (1998) argues implicitly that such bias can also be considered systemic error. This type of uncertainty is difficult to identify except on theoretical grounds when the magnitude and direction of the bias are known. Most of these biases, whether systemic or knowledge-based, individual or practice-based, require disciplined efforts to "validate" such methods, models, and judgments at all layers while bounding the uncertainty.
: This is the challenge to professional knowledge that must be overcome, namely, developing and validating models and methods for managing bounded uncertainty that minimize this tendency towards repeatable groups of error or bias. Therefore, from this point forward, the discussion will refer to 'uncertainty and its proxies, error, and bias.'
===Propagating Error and Uncertainty===
Partitioning uncertainty and its proxies, error, and bias into logically distinct subsets involves or is associated with multiple-step execution or choice frameworks. Combining those measurements or choices into single parameters or choices involves, in some cases, assembling the data/information in a unique or limited range of sequences. This implies that associated errors in observing, calculating, or choosing will have unequal impacts or consequences depending on where in the chain the error occurs. Also implied in this is the possibility that subsequent errors will be causally linked to some degree. (In statistical practice, this depends upon whether the errors are independent of each other or correlated, specifically, co-variant.)
:'''Professions develop knowledge of how uncertainty and its proxies, error, and bias propagate through an ordered and normative series of observations or choices. This allows the profession to prioritize error and bias reduction to achieve an optimized reduction of uncertainty.'''
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===Cognition, Metacognition and Uncertainty===
The concept of choice and the resultant error in uncertainty can be further delimited depending on the role of [https://en.wikipedia.org/wiki/Cognition cognition], [https://en.wikipedia.org/wiki/Metacognition meta-cognition], and independent external elements. The rationale for this argument is that such choice activity in the face of uncertainty is a conscious, cognitive act.

An argument could be made that such cognition is a prerequisite for exercising choice and introducing error, as presented and discussed above. The counterfactual to this argument is the example of uninformed choice. Choices introduce as much as errors or more as informed choices could be made. (???) Choice, in this sense, is indifferent to knowledge. Purposeful choice in an environment of objectives requires knowledge, method, and experience, but uninformed, speculative choice does not.
:"''' Cognition''' is the set of all mental abilities and processes related to knowledge, attention, memory and working memory, judgment and evaluation, reasoning and "computation," problem-solving and decision making, comprehension and production of language, etc. Human cognition is conscious and unconscious, concrete or abstract, as well as intuitive (like knowledge of a language) and conceptual (like a model of a language). Cognitive processes use existing knowledge and generate new knowledge." (Wikipedia)
:"'''Metacognition''' is "cognition about cognition", or "knowing about knowing" and can take many forms. It includes knowledge about when and how to use particular strategies for learning or problem-solving. There are generally two aspects of [https://en.wikipedia.org/wiki/Metacognition metacognition]: knowledge about cognition and regulation of cognition."
Some types of metacognition knowledge are:
:'''Person knowledge''' (declarative knowledge), which is understanding one's own capabilities.
:'''Task knowledge''' (procedural knowledge), which is how one perceives the difficulty of a task, which is the content, length, and type of assignment.
:'''Strategic knowledge''' (conditional knowledge) is one's capability to use strategies to learn information. (Accessed at Wikipedia)
Like metacognitive knowledge, metacognitive regulation or "cognitive control" contains three essential skills.
:'''Planning:''' refers to the appropriate selection of strategies and the correct allocation of resources that affect task performance.
:'''Monitoring:''' refers to one's awareness of comprehension and task performance.
:'''Evaluating:''' refers to appraising the final product of a task and the efficiency at which the task was performed. This can include re-evaluating strategies that were used. (Accessed at Wikipedia)
Nothing has been said up to this point that limits this structure to an individual, or a collective, or two entities making cognitive choices that are incompatible or inconsistent to varying degrees. Interpretations of this scheme include an individual professional exercising judgment in a decision that, in turn, relies upon the collective judgment and decision of the discipline as an underlying basis versus the decision of another individual to oppose/protest or otherwise contest that decision. Thus, the framework of cognitive/meta-cognitive applies to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, economics of regulatory practice, and game theory.
:By solving useful problems in the face of inherent uncertainty, discipline-specific frameworks of cognitive/meta-cognitive activity apply to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, the economics of regulatory practice, and game theory.
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===Probability and Uncertainty===
Probability is a [https://en.wikipedia.org/wiki/Coherence_theory_of_truth coherent] approach to uncertainty in the physical and mathematics or "classical" domains such as engineering. <ref>See Colyvan for a critique of this claim, Colyvan Mark. "Is probability the only coherent approach to uncertainty?." Risk Analysis 28.3 (2008): 645-652.</ref> and is the "...is certainly the best-known and most widely used formalism for quantifying uncertainty.<ref>Morgan, Millett Granger, Max Henrion, and Mitchell Small. Uncertainty: a guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge University Press, 1992.</ref> [https://en.wikipedia.org/wiki/Cox%27s_theorem Cox’s theorem] ("Any measure of belief is [https://en.wikipedia.org/wiki/Isomorphism isomorphic] (but not necessarily equal) to a probability measure") is a well-known argument for the validity of that argument.
<ref group="note">Cox wanted his system to satisfy the following conditions: 
Divisibility and comparability – The plausibility of a statement is a real number and is dependent on the information we have related to the statement.
:Common sense – Plausibilities should vary sensibly with the assessment of plausibilities in the model.
:Consistency – If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). — preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf</ref>

Another writer, [https://en.wikipedia.org/wiki/Frank_Knight Knight] (1921,1956), presented the following taxonomy of probabilities:
<ref>As presented and discussed in Runde, Jochen. "Clarifying Frank Knight's discussion of the meaning of risk and uncertainty." Cambridge Journal of Economics 22.5 (1998): 539-546.</ref>
: '''Classical 'a priori' probability:''' As an idealized model, numerical probabilities are computed based on generalized principles of assigning equal likelihoods and mutually exhaustive possible outcomes (Runde, 1998) to all set members. Such probabilities are assigned to "...outcomes based on a judgment of indifference between those outcomes, that is, based on the absence of any evidence of real influences in play that may render any one outcome more or less probable than any other." (Runde, op. cit.) This builds upon Knights's concept of an 'absolutely homogeneous classification of instances that are completely identical except for [https://en.wikipedia.org/wiki/Indeterminate_(variable) indeterminate] factors. This judgment of probability or logical probability is on the same logical plane as the propositions of mathematics and ultimately [https://en.wikipedia.org/wiki/Inductivism inductions] from experience.'(Knight, 1956, pg.225)
: '''Bayesian 'a priori' probability:''' In contrast to interpreting probability as the "frequency" or "propensity" of some phenomenon, [https://en.wikipedia.org/wiki/Bayesian_probability Bayesian probability] is a quantity that we assign to represent a state of knowledge or a state of belief. In this view, probability is assigned to a hypothesis, often using the basis of past or prior experience. In contrast, under the frequentist view, a hypothesis is typically tested without being assigned a probability based on prior experience. The Bayesian interpretation of probability can be seen as an extension of propositional logic that enables reasoning with hypotheses, i.e., propositions whose truth or falsity is uncertain. Bayesian probability belongs to the category of evidential probabilities; to evaluate the probability of a hypothesis, the Bayesian probabilist specifies some prior probability, which is then updated in the light of new, relevant data (evidence). The Bayesian interpretation provides a standard set of procedures and formulae to perform this calculation. <ref>See also Larvor, B. "After Popper, Kuhn, and Feyerabend: Recent Issues in Theories of Scientific Method." Metascience (2002).</ref>
: '''Statistical or 'a posterior probability:''' Empirical evaluation of the frequency of association between predicates, not analyzable into varying combinations of equally probable or logical, 'a priori' alternatives. Knight argued that any "high degree of confidence" that the judgment that experience will remain valid for future predictions is "...still based on an 'a priori" judgment of indeterminateness." (Knight, Op. Cit.) Knight's argument to support this is what will be discussed further below that first, "...the impossibility of eliminating all factors not really indeterminate; and, second, the impossibility of enumerating the equally probable alternatives involved and determining their mode of combination so as to evaluate the probability by a priori calculation." (Knight, Op. Cit.) Knight noted that the main difference between this form and that of logical or inductive probability was the presence of empirical information, which allowed the analyst to identify propensity and direction in the observed phenomena. In this case, 'a priori' probability values may be derived from first principles and "statistical probabilities are determined a posteriori by the empirical method of counting instances." (Runde [1998] quoting Knight, op. cit.)
:'''Estimates:''' The distinction here is that there is no valid basis ('a priori' or 'a posteriori') for any classifying phenomena. For Knight, this form of probability presented the greatest logical difficulties of all ... but its distinction from the other types must be emphasized, and some of its complicated relations indicated..." (pp. 224-5, emphasis in the original)

Knight's concept of probability can be viewed as "..a continuum of probability situations, depending on the degree of homogeneity of the 'instances' in question" <ref>Runde, Jochen. "Clarifying Frank Knight's discussion of the meaning of risk and uncertainty." Cambridge Journal of Economics 22.5 (1998): 539-546.</ref>; i.e. going from a logically distinct but equally likely set of elements to a unique set with one member with statistical frequency in the middle. Knight's main motivation for distinguishing between a priori probability and statistical probability underscores his opinion that "(i) the 'mathematical or a priori type of probability is practically never met with in business, while the second is extremely common'; and (ii) 'the statistical treatment never gives closely accurate quantitative results' (Runde quoting Knight pp. 215-16). Runde argues that Knight couldn't conceive of (empirically tabulated) occurrences that are used in daily commerce classes of instances that we have to make do within the course of everyday economic life could be divided into subclasses of instances that are sufficiently homogeneous to permit the determination of what he calls 'real' probability (p. 217).
:'''In modeling degrees of uncertainty, any measure of belief is isomorphic but not necessarily equal to a probability or statistics measure'''.
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===Explanation, Prediction and Uncertainty===
It should be clear that the general notion of uncertainty synonymous with complete ignorance must be bounded as part of a scheme to produce a useful concept of uncertainty in professional practice such as civil engineering.
:The first principle that must be introduced is that such uncertainty must be bounded by reliable and valuable knowledge gained from past experience combined with analytical and computational capabilities to address an immediate and real problem of interest. This could be viewed as the economic interest argument. Only uncertainties associated with physical phenomena and economic scarcity will be addressed.
:The second principle is that the knowledge model is capable of producing statements or assertions in the form of hypotheses that themselves are testable. [https://en.wikipedia.org/wiki/Testability Testability], in this sense, is defined as the property applying to an empirical hypothesis and involves two components:
::The logical property that is variously described as contingency, defeasibility, or falsifiability, which means that counterexamples to the hypothesis are logically possible. Contrast this to a [https://en.wikipedia.org/wiki/Tautology_(logic) logical tautology], which is always true that is true in every possible interpretation. The practical feasibility of observing a reproducible series of such counterexamples if they do exist. In short, a hypothesis is testable if there is some real, non-zero expectation of deciding whether it is true or false of verifiable, reproducible experience. Upon this property of its constituent hypotheses rests the ability to decide whether a theory can be supported or falsified by actual experience data. (Source Wikipedia.)
'''Knowledge models must be capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria described above in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error and bias.'''
===Variability and Aleatory Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge referred to as epistemic uncertainty. Acting in the face of epistemic uncertainty introduces choice uncertainty (discussed below) and results in the production of explanations and predictions. It is implied that there will be testable hypotheses in the form of outcomes of interest to the knowledge professions. Inherent in nature is a variation of results, which may appear to uncertain but isn't. Observations of phenomena will not be the same, and part of the experimental process is to narrow those results down to within an acceptable level of tolerance. Statistical measures seek to identify underlying and unobserved measures of central tendency. These parameters are not directly observed but are derived to a degree of confidence.
In some cases, variation around a mean or within a statistical range may be perfectly acceptable to the knowledge profession. Examples of this are in the physical sciences and medicine. In Engineering and, to a lesser extent, economics, the process or phenomenon may be required to be more controlled to a narrower range than what a natural or "unmanaged" amount of variation would allow. Several courses of action present themselves.
:The first would be to ignore the aleatory phenomenon and choice, which is rejected outright.
:The second would be to assume that, in effect, such variation is itself a form of minimally managed uncertainty and treat it the same as epistemic uncertainty. This is not a desirable outcome as reducing Aleatory error and bias requires
:A third approach would be to acknowledge that process variation is, in some forms, manageable. This means making choices and acting in a way that presumes that the natural occurrence of error can be reduced, or more importantly, bias can be reduced. There is ample precedent for the third approach in civil engineering design and project management. In this sense, the argument is made that the aleatory response to managed efforts results in an elastic variation range. Presumably, an effective management effort either "shifts" the central tendency of the process results or reduces the variation range of occurrence or some other population parameter. Choosing to act and accept that narrower range rather than the broader natural variation may result in an outcome that, while well within the bounds of the expected variation, is outside the arbitrary control limits established by the choice and action. While this phenomenon mimics the broader uncertainty and its proxy error, it is not an error. The information on the variation was known, and the ability to produce narrower results was an error, albeit a pseudo-error, when compared to the broader classes of uncertainty discussed above, such as total ignorance.
::Looking at uncertainty using this third approach means that the error in choosing a new target parameter for this process may contain epistemic errors (our knowledge of the phenomenon may be erroneous, or the model flawed), aleatory uncertainties as discussed, or, more importantly, choice error or bias in thinking that we can move the process results to meet the requirements.
:'''In civil engineering practice, this may mean applying professional judgment in developing partitioning schemes or allocating uncertainty between the three dimensions (Epistemic, Aleatory, and Behavioral) of uncertainty.'''
Lastly, raising the topic like this brings the questions of effectiveness and efficiency to the fore. Advancing knowledge models that reduce error and bias at first are largely choice models resulting in justifications and favorable experiences. At some point in the process, the knowledge profession advances explanations and predictions that require reliable mathematical or logical concepts that are reduced to models and parameters. The question of efficiency becomes important; the broader uncertainty may have been reduced, but the process is economically inefficient. The profession is urged to become more frugal or, for example, reduce the variability of the outcomes. Knowledge professions often operate under conditions that require them to acquire knowledge and reduce uncertainty effectively and efficiently. Doing so requires understanding both epistemic uncertainty, as discussed above, and statistical process variation or aleatory variation.
:'''Knowledge professions manage and reduce uncertainty in the form of error and bias in an economically effective and efficient manner. This effort requires understanding both epistemic and aleatory uncertainty.'''
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===Choice and Behavioral Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge, referred to as epistemic uncertainty, as well as the inherent variations in results, or what was termed aleatory uncertainty. Acting in the face of that epistemic and aleatory uncertainty introduces yet another uncertainty into the process, namely choice uncertainty. This uncertainty reflects uncertainties associated with first-person and third-person choice. This is particularly relevant for engineering with project stakeholders.
===Summary of Semantics Framework for Uncertainty===
Professional disciplines have gradually re-framed their understanding of uncertainty and, thru experience and analysis, have arrived at the following meta-knowledge concepts of uncertainty:
* '''Uncertainty and its proxy, error, have three components: [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic epistemic uncertainty], [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic aleatory uncertainty], and behavioral uncertainty.'''
* '''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty using non-dogmatic beliefs, which recognize the existence of specific criteria and information that would make the believer change their beliefs using coherent approaches such as probability.'''
* '''Knowledge professions develop knowledge of how uncertainty and its proxy, error, propagate through an ordered and normative series of observations or choices that meet testability criteria in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error. An error can then be described, explained, and reduced in ways that uncertainty cannot. This allows the professions to prioritize error reduction to achieve an optimized reduction of uncertainty and produce a "legitimacy" from the fruitfulness of its use.'''
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===Uncertainty versus Risk/Hazard===
Up to this point, nothing has been said about risk or hazard. Now, risk can be defined. Simply put risk or hazard are finite, logically distinct subsets of the broader uncertainty. Risk and hazard are currently defined interchangeably. First and foremost, risk is a specific subset of general uncertainty. Any arbitrary subset of general uncertainty can be defined as a risk if it meets the following criteria:
* Uncertainty is associated with the potential for material error when acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena.
* Uncertainty and its proxy, error, can be partitioned into logically distinct subsets associated with multiple-step execution or choice frameworks in physical environments linked to a unique or limited range of sequences.
**Subcriteria 1:Resulting errors will have varying impacts or consequences depending on where the error occurs and the degree of causal linkage or influence in the chain.
**Subcriteria 2:Potential errors can be sequenced and prioritized to achieve an optimized reduction of error or bias and, by proxy, uncertainty over time.
* Uncertainty and its proxies, error bias, and environments are isomorphic to coherent approaches such as probability.
* Uncertainty and its proxies, error, and bias are isomorphic to coherent approaches such as knowledge models capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria in 'reasoning under uncertainty.'
This is a definition of general risk. There is no differentiation for engineering or economics, medicine or insurance. There is no difference between a 'good' risk consequence and a 'negative' one. Redefining general risk into simpler terms gives the following:
:'''General Risk is a subset of general uncertainty that is associated with the potential for material errors and biases when sequentially acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena; isomorphic to probability measures and methods and capable of testability.'''
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==Logical Framework for Specific Risk Taxonomies==
As noted above, at the most fundamental level, uncertainty is ".....the subjective perception of ignorance."
<ref name="Han">Han, Paul KJ, William MP Klein, and Neeraj K. Arora. "Varieties of Uncertainty in Health Care A Conceptual Taxonomy." Medical Decision Making 31.6 (2011): 828-838.</ref>
At this point in the discussion, the focus has moved from discussing uncertainty to its framed subset, risk.
:'''Further, the discussion moves from general risk to a professional or discipline-specific risk [https://en.wikipedia.org/wiki/Taxonomy taxonomy]. Beyond this point, risk will be used in place of uncertainty.'''
Han et al. note that, in general, "(t) taxonomies are valuable not only in their comprehensiveness but in their coherent reduction of uncertainty to conceptually discrete elements."
Uncertainty (and, by implication, risk or hazard) taxonomies offer an approach or tool to more precisely identify and define ontological uncertainty so that it can be quantified, analyzed, and communicated. Viewed this way, uncertainty is not a single, monolithic phenomenon but "...multi-dimensional with theoretically distinct domains and constructs that are potentially measurable and related to different outcomes, mechanisms of action, and management strategies." <ref name="Han"/> Taxonomic schemes for classifying the different kinds of scientific and, by extension, engineering uncertainty require identifying the various kinds of potential error associated with descriptive scientific or engineering information and knowledge. An example of this is the following quote from a legal authority:
:"I would especially stress the need for an agency to disclose the uncertainty that surrounds its determinations. ''' And by uncertainty, I mean the agency's ignorance as well as its quantitative estimates of error."''' (Emphasis added)
<ref>Walker citing Bazelon, Science, and Uncertainty: A Jurist's View, 5 HARV. ENVTLt L. REv. 209, 212 (1981).</ref>
''' Given the necessity of acting in the face of enormous uncertainties
<ref>Vern citing Ruckelshaus, Science. Risk, and Public Policy, 221 SCIENCE 1026, 1027 (1983)</ref>,
discipline taxonomies must offer a clearly coherent and probabilistic approach to uncertainty that is sufficiently general in nature, logically distinct and exhaustive in scope and "...provide decision-makers with a foundation for understanding the nature of discipline information..."''' <ref name=
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=== Scientific Risk Taxonomies ===
The [https://en.wikipedia.org/wiki/Scientific_method scientific method] is a body of techniques for investigating phenomena in its environment, acquiring new knowledge, or correcting and integrating previous knowledge. Inherent in that process are uncertainties of many forms. Any one taxonomy of [https://en.wikipedia.org/wiki/Uncertainty scientific uncertainty] has largely been focused on statistical models used to assess and quantify sampling, errors, and parameters, although several different taxonomies are conceivable: note-24 [18]
====Environment-specific, Phenomena oriented, model-centric taxonomy of uncertainties====
** [https://en.wikipedia.org/wiki/Uncertainty_quantification#Sources_of_uncertainty '''Parameter uncertainty'''] the source of which is model parameters that are inputs to the computer model (mathematical model) but whose exact values are unknown and cannot be controlled, or whose values cannot be exactly inferred by statistical methods. Subsets of this type of uncertainty include but are not limited to:
***'''Experimental uncertainty''' is also known as observation error, which comes from the variability of experimental measurements. An example is repeating an experiment measurement several times using exactly the same settings for all inputs/variables and recording the variability. It is a subset of the larger uncertainty component, parameter uncertainty.
*** '''Parametric variability''' comes from the variability of the input variables of the phenomena model and is also a subset of the larger uncertainty component, parameter uncertainty.
** '''Structural uncertainty''', or model inadequacy, model [https://en.wikipedia.org/wiki/bias bias] or "[https://en.wikipedia.org/wiki/Systemic_bias systemic bias]", or model discrepancy, which comes from the lack of knowledge of the underlying true state of the phenomenon or its environment. It depends on how accurately a mathematical model describes the true state of the phenomenon or what is termed "model fitness". Due to the inherent nature of uncertainty in any knowledge body (scientific or engineering), models are only an approximation to reality. Therefore, any conclusions drawn from the model can be very misleading when the underlying basis is not plausible or lacks validity. '''note-25 [19]]'''
*** What is missing in this context is any concept of output variability due to the model itself. Part of this is due to the lack of recognition of "process" in scientific investigation. The concept of the scientific method started with a single investigator, such as Galileo or Michael Faraday, working in their laboratories. It has grown into planet and solar system scale experiments such as data gathered on planetary flybys such as Mars, Pluto, and Ceres to the hunt for the Higgs Boson. Ultimately, science has become more like engineering in the scale and complexity of its investigations and theory aggregates, such as the "Unified theory" of particle physics. Subsets of this type of uncertainty include but are not limited to:
*** '''Algorithmic uncertainty''', or numerical uncertainty, comes from numerical errors and numerical approximations per implementation of the computer model. Most models are too complicated to solve exactly. For example, the [https://en.wikipedia.org/wiki/Finite_element_method finite element method] or [https://en.wikipedia.org/wiki/Finite_difference_method finite difference method] [may be used to approximate a solution [https://en.wikipedia.org/wiki/Partial_differential_equation partial differential equation], which introduces numerical errors. Other examples are numerical integration and infinite sum truncation, which are necessary approximations in numerical implementation.
*** '''Interpolation uncertainty''' comes from a lack of available data collected from computer model simulations or experimental measurements. For other input settings that don't have simulation data or experimental measurements, one must interpolate or extrapolate to predict the corresponding responses.
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====Commentary====
A more comprehensive taxonomy of uncertainties that acknowledges knowledge, data, and linguistic uncertainties is:
*'''Epistemic uncertainty''' -[https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Epistemic uncertainty] or systematic uncertainty, in contrast, reflects limitations in the current “state of knowledge” underlying models themselves, originates from competing theories or models, is not readily quantifiable, and is manifest by subjective confusion or indecision. It includes uncertainty due to measurement limitations, insufficient data, extrapolations and interpolations, and variability over time and space. Epistemic uncertainty is uncertainty about "...some determinate fact ... because of a lack of complete information. '''cite_note-26 [20]]''' Epistemic uncertainty can be classified into six main types:'''cite_note-27 [21]]'''
*''' Measurement error''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Observational_error Measurement error] is uncertainty that manifests itself as (apparently) random variation in the measurement of a quantity. Repeated measurements will vary and demonstrate classic statistical behavior.
* '''Systematic error''' - [https://en.wikipedia.org/wiki/Observational_error Systematic error] occurs due to bias in the measuring equipment, model, analysis, observation, or sampling procedure. It is formally defined as the difference between the true value of the quantity of interest and the value to which the mean of the results converges as sample or data sizes increase. Unlike measurement error, it is not (apparently) random, and therefore, results subject to systematic error alone do not vary about a true value. Systematic error can result from deliberate judgment to exclude (or include) data, parameters, or models that ought not to be excluded (or included).
** "The only way to deal with systematic error is to recognize a bias in the process, model, or procedure and remove it. Systematic error, however, is notoriously difficult to recognize except on theoretical grounds. Corrections may only be applied when the magnitude and direction of the bias are known. Such corrections underlie the application of double-sampling methods in environmental science." (Regan, et. al., op. cit.)
*'''Natural variation''' - [https://en.wikipedia.org/wiki/Natural_process_variation Natural variation] or underlying process variation occurs in systems that "...change (concerning time, space, or other variables) in ways that are difficult to predict...across the full range of temporal and spatial values (or other related variables)." '''note-28 [22]'''
** This form of uncertainty is reduceable utilizing tools such as [https://en.wikipedia.org/wiki/Statistical_process_control statistical process control] and replication of experiments or observations.
*'''Inherent randomness''' - [https://en.wikipedia.org/wiki/Randomness randomness] or [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty aleatory uncertainty] exists because the system is"...in principle, irreducible to a deterministic one (the most well-known case is described by [https://en.wikipedia.org/wiki/Uncertainty_principle Heisenberg’s uncertainty principle] in quantum mechanics)." '''note-29 [23], note-30 [Note 7]'''
** Even if the argument is accepted that it is unlikely that any given physical system at some level is inherently random, it is important for any taxonomy to distinguish between systems that appear random due to incomplete or inconsistent information and those that are intrinsically random. (Again, see Regan (2002)).
*'''Model uncertainty''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling Model uncertainty] occurs in the process of generating a model as a conceptual representation of a particular phenomenon to create a [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling#Overview simplified reflection of reality]. This selectivity of factors and variables creates uncertainty in at least three ways. (Regan (2002))
** '''Variables and processes''' that are regarded as relevant often represent a trade-off between system knowledge and assumed states and model objectives;
** '''Models''' depict observed processes using logical or mathematical constructs based on underlying theories about system states or dynamics using continuous equations to describe discrete processes.
** '''Curve fitting''' (including interpolation and extrapolation) with mathematical expressions using model variables where inputs are discrete data points.
** Regan (2002) argues that '''model uncertainty''' is "...notoriously difficult to quantify and impossible to eliminate ...(and)...(t)he only reliable way of determining how appropriate a model is for prediction is to perform validation studies."
** Subjective judgment occurs due to the interpretation of scarce or error-prone data.
*** The only way to address this type of uncertainty is to "...assign a degree of belief about an event in the form of a subjective probability." (Regan (2002))
*Linguistic uncertainty - Linguistic uncertainty, or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. '''note-31 [24]], note-32 [25]]'''
** '''Context dependence''' is uncertainty concerning the context in which a statement is to be understood;
** '''Ambiguity''' occurs when words have multiple meanings, and further reduction to a single meaning is not logically possible in a given context.
** '''Underspecificity''' occurs in the presence of "unwanted generality" or multiple interpretations, and reduction to a smaller set or even a single interpretation is not logically possible.
*Stochastic or statistical uncertainty - Stochastic or statistical uncertainty, which is sometimes known as [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Aleatoric uncertainty] as well as the subject of [https://en.wikipedia.org/wiki/Stochastic_control stochastic control] pertains to the parameters of a risk model, originates from sampling or measurement error, and can be quantified and mathematically expressed (e.g., using confidence intervals).
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== Uncertainty Taxonomies in a Legal Environment ==
From a legal perspective, a definition of taxonomy is dictionary-based, such as "the systematic distinguishing, ordering, and naming of type groups within a subject field." given by Walker (op. cit.). Such legal concepts of scientific or engineering taxonomies are not required to be epistemically complete. Still, they must cover "the most significant aspects of ...(current, good discipline practices) ... and of the descriptive information commonly encountered by decision-makers" in a logically distinct manner. (Walker, 1991, p. 571) These taxonomies must be sufficient to catalog the types of uncertainty associated with descriptive assertion, including the critical subset of cause and effect assertions. This legal/decision-maker framework has presented descriptive uncertainty as having six components (Walker, 1991). Although not part of the Walker taxonomy, underlying the scheme is linguistic uncertainty, or "vagueness," as a source of uncertainty, including uncertainties due to context dependence, ambiguity, and under-specificity. '''note-33 [26]]''' Arguably, these uncertainties underlie the most basic element in the taxonomy.
* '''Linguistic '''- or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. (Regan, 2002)
* '''Conceptual'''- the definition by choice and design of descriptive concepts or variables to be used as [https://en.wikipedia.org/wiki/Predicate_(grammar) predicates] where the subject of an assertion identifies what is being discussed and the predicate provides information about the topic or characterizations such as what the subject is, what the subject is doing, or what the subject is like (Wikipedia).
* "Conceptual uncertainty, or the potential for conceptual error, arises whenever predication occurs. Whenever a concept is used to describe something, using certain concepts instead of others begins to structure how we understand the object, event, or instance under discussion. Predication or conceptualization generates useful information about things, but it also can inhibit our ability to think about those things with concepts other than those selected. The concepts used may not be the most fruitful or the best designed- either for scientific purposes or for making wise, fair, effective, and efficient decisions." (Edited for reading, Walker, 1991)
* Similarly, choice and design of descriptive variables enhance and restrict data set membership, creating potentially incomplete, incompatible, or inconsistent data sets, "...the classification categories employed, and the relationships among those categories (nominal, ordinal or scalar)." '''note-34 [27]]'''
* Concept uncertainty '''note-35 [28]''' like systematic error discussed above is notoriously difficult to recognize, except on theoretical grounds, and the magnitude and direction of the bias are known.
Both terms could be analogized to that of [https://en.wikipedia.org/wiki/Accuracy_and_precision#Terminology_of_ISO_5725 accuracy] in [https://www.bipm.org/en/publications/guides/ ISO 5725] which looks at the ability of the concept or system's ability to produce results that are proximate to the "true" value or state of the phenomenon. Conceptual uncertainty and systemic bias are related to another measure of uncertainty: validity. Walker has defined validity as the ability of a concept to quantitatively describe and explain what the phenomenon objective "truly" is. Walker also ties the concepts together when he states, "Validity concerns the "accuracy" of the measurement data, not its precision." (Walker, 1998) 
Additionally, conceptual uncertainty and associated validity issues could be propagated throughout this taxonomy. Every component of action and decision in the face of uncertainty raises validity issues. Walker recognized this when the author defined epistemic choice for concepts used throughout the other uncertainty components or layers.
* '''Measurement''' uncertainty or Misclassification error- the application of the underlying concepts or variables to specific, individual cases and the uncertainty of the reliability (Walker, 1998) of the resultant data. (It is important to remember that assessment observation and measurement are used interchangeably in this analysis.) Thus, an assessment method or procedure is considered "reliable" or "precise" in the scientific terms of ISO 5715 if it repeatedly produces consistent results.
* Measurement uncertainty is logically distinct from conceptual uncertainty, which can only occur after any conceptual uncertainty has been established. Still, misclassification can occur even when the conceptual uncertainty has been minimized. Conversely, underlying errors or errors made in measurement or classification propagate thru the reasoning chain and reduce the quality or value of the final result.
* While uncertainties associated with quantitative measurements or assessments can be statistically evaluated and described, uncertainties related to qualitative assessments are more problematic.
**Working through the taxonomic chain from base concept to causal explanation requires amassing and integrating qualitative information and quantitative data into input parameters for mathematical models. Developing procedures for producing consistent, repeatable sets of information from assessments that minimize conceptual error or systemic bias requires professional efforts to "validate" such methods, models, and judgments at all layers in the process. Validation as a process is something that civil engineering as a practice needs to embrace more frequently.
* '''[https://en.wikipedia.org/wiki/Sampling Sampling]'''- in the classical statistics sense, is the selection of a limited subset of a larger population to use as a basis for making estimates about the population in the form of attributes, proprieties, or parameters. The resulting information would be determinative in making forecasts about future population sampling or possessing a desired amount of predictive power. The uncertainty about the ability of this information to correctly predict future samples is termed sampling error.
* '''Modeling'''- Modeling uncertainty arises whenever a claim is made that out of a class of candidate relationships and variable pairings, one variable pairing inclusive of constants is chosen that possesses a particular or persistent mathematical relationship to another variable X. Modeling errors arise in selecting the wrong variable pair and constants or by incorrectly specifying its constants. (Walker, 1991, pg. 599)
** '''Causal'''- In causal modeling and analysis, the relationship between causal variables is not strictly a mathematical function. Still, it makes testable predictions and explains "...how a system of variables works, why a system works the way it does, or why it makes sense to think of certain variables as a system" at all." (Walker, 1991, pg. 609)
** '''Epistemic'''- the choice of interpretations for fundamental, logical concepts used throughout the other components or layers.
The components of this uncertainty taxonomy can combine in different ways to "...produce different aggregate uncertainties." '''note-36 [29]'''
:''' Only the scientific and engineering professions possess the capacity to develop and validate procedures and models that combine the different kinds of qualitative '''note-37 [30]''' and quantitative information and their associated potential for error into an aggregate measure or "scalar variables" of uncertainty.'''note-38 [31]]
Scalar variables are quantitative variables whose categories are related by some measure of the relevant property's incremental frequency, degree, or amount. (Walker, 1991) Examples are Modulus of Elasticity, Compression stress in elastic materials, etc.

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=== Economics Uncertainty Taxonomies ===
Like scientific concepts of uncertainty, economics is environment-specific (in this case, read sector of economy or markets), phenomenon-oriented (in this case, read human activity and manufactured phenomena such as firms and markets guided by human judgment and decision), and model-centric.
:''' Economics is not interested in all forms of uncertainty, only specific, meaningful subsets possessing casual connections with material impacts, often labeled as 'risks'.'''
One of its earlier writers ([https://en.wikipedia.org/wiki/Frank_Knight#Life_and_career Knight], 1921, 1948, 1957) attempted to define "uncertainty" as the presence of "defects of managerial knowledge" (or [https://en.wikipedia.org/wiki/Knightian_uncertainty knightian uncertainty] and thus risk centric) as risk that is immeasurable, not possible to calculate.(Wikipedia) Risk in this sense was defined as "the ordinary risks of business activity which can ... be reduced... by applying the insurance principle." Knight does acknowledge that risk is a specific and unambiguous subset of uncertainty. (Risk, Uncertainty and Profit, 1957, pg. 19) For Knight, measuring uncertainty meant measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences, which Knight referred to as "the imperfection of knowledge". (Risk, Uncertainty and Profit, 1957, pg. 197) One of Knight's many contributions to this analysis was recognizing information and knowledge's role in economic activity.
:"''(W)e are concerned only to emphasize the fact that '''knowledge is in a sense variable in degree and that the practical problem may relate to the degree of knowledge rather than to its presence or absence in toto.''' We live only by knowing something about the future, while the problems of life, or conduct at least, arise from knowing so little. This is as true of business as of other spheres of activity. The situation's essence is action according to opinion, of greater or less foundation and value, neither entire ignorance nor complete and perfect information, but partial knowledge. If we are to understand the workings of the economic system, we must examine the meaning and significance of uncertainty; and to this end, some inquiry into the nature and function of knowledge itself is necessary.''" (Knight, pg. 199) Emphasis added
Knight also reinforced the difficulties in classifying uncertainties when he analyzed life insurance. In this case, accidental death was an uncertain phenomenon compared to sickness and accident, where an "...objective description and classification of cases was impossible..." (Knight, pg. 248) Another factor that made uncertainties ineligible as risks and, therefore, uninsurable because they were unclassifiable was the exercise of judgment in making decisions by the businessman. (Knight, pg. 251)
This is a simple classification of economic uncertainty and, as an idealization, is philosophically controversial. '''note-39 [32]'''
* Uncertainty and information about the economic environment are distinct from uncertainty about others’ behavior or choices.
* Risk as a specific subset of uncertainty implies that "...all possible acts are known, all possible outcomes arising from each act are known, and it is possible to assign probabilities to each act." '''note-40 [33]'''
Like the economic [https://en.wikipedia.org/wiki/Theory_of_the_firm theory of the firm], an economic theory of uncertainty seeks to explain and predict the nature of a specific subset of uncertainty (in this case, risk relating to physical phenomena and human activity), including its behavior, structure, and relationship to the universal uncertainty of physical phenomena. In this sense, uncertainty, like firms and markets, can establish price equilibriums under the right circumstances different from those that might have occurred in either of the other two. Such a theory of uncertainty offers a partitioning scheme that decomposes uncertainty into distinct and meaningful subsets, of which risk is a significant one. It also explains how the presence and variability of knowledge cause or " drive" economic consequences. Unlike physical phenomena, economic activity creates artificial objects such as products, firms, mediums of exchange, markets, economies, and knowledge about those activities. The economics of uncertainty exists as an alternative to "certainty" models that assume away imperfect knowledge and unknown choice/judgment preferences when it is more efficient for economic decision-making. This also allows the introduction of the logical concept of "hazard" versus "risk". Frank used hazard extensively in Risk, Uncertainty, and Profit, but not to the extent that one could say it was interchangeable. An example of this is in the term "[https://en.wikipedia.org/wiki/Moral_hazard Moral hazard]," where a firm could engage in "riskier" and potentially "uninsurable" behavior if the information were transparent, and, therefore, insurance coverage of losses is limited. note-41 [34]], note-42 [35]]

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=== Uncertainty and Risk in Policy and Regulatory Environment ===
:''"Hume's 'just reasoner', when faced with a difficult public policy decision in current times, would probably commission a risk analysis. But how could they incorporate 'a degree of doubt, caution, and modesty?"'' '''note-43 [36]'''
The problem of "decision-making in the face of uncertainty" is how to regulate based on incomplete information that has the potential to be materially inaccurate. A contextual assumption is that we need to evaluate decision rules for dealing with uncertainty. As a social enterprise, risk regulation, whatever its substantive objectives, should be as '''effective, efficient, and equitable''' as practically possible. These "three E's" form a set of "process objectives" or "meta-goals." From the uncertainty standpoint, causal information can be usefully divided into two major categories: information about groups and information about individuals.

Each category, group, and individual has its distinctive types of inherent uncertainty. These are logically distinct, generally independent, and cumulative, contributing to '''(?????)'''

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=== Engineering Uncertainty Taxonomies ===
Like scientific and economic concepts of uncertainty, engineering is environment-specific, phenomena-oriented, model-centric, external choice, and driven by decision-making in the face of uncertainty. Like economics and unlike science, engineering is not interested in all forms of uncertainty, only those with determinable and material consequences. Like economics, engineering conceives risk as a specific and unambiguous subset of uncertainty determined by measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences. Also, engineering recognizes the role information and, by extension, knowledge plays in constructing the built environment.
'''note-44 [37]]''' Like economics, engineering differs from economics and scientific uncertainty in its interest in behavioral uncertainty.
*"Behavioral or interaction uncertainty is how individuals or organizations act or interact. Behavioral uncertainty arises from four sources: design uncertainty, requirement uncertainty, volitional uncertainty, and human errors." '''note-45 [38]]'''
** A design uncertainty is a choice among alternatives over which an individual or group of individuals exercises direct control but has not yet decided upon.
** Requirement uncertainty includes parameters of interest to and determined by the stakeholder, independent of the engineer or designer.
** Volitional uncertainty is uncertainty about what the subject him/herself will decide. Other people’s future actions and conduct are not entirely predictable, particularly when dealing with other organizations.
** Human errors occur during the development of a system or project due to blunders or mistakes by an individual or individuals.

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==Practice Frameworks for Uncertainty and Risk in Civil Engineering Practice==
===PMIBoK===
PMI does not define uncertainty, but the PMI Body of Knowledge uses the term in 45 places (uncertainty) and as describing properties in 8 places (uncertainties). [See Note 8] PMI does define "risk" and uses the term over 1,300 times in the same document. In its glossary, PMI also defines terms such as "threat" and "opportunity" but not events.[391 The BoK contextually defines uncertainty when it states that project risk has its origins in the uncertainty present in all projects. (Op. Cit., pg 309) 
'''Uncertainty''' is contextually defined and taken as having the property of affecting project execution and the ability to meet stakeholder expectations. Uncertainty is more than the sum of all individual risks (known and unknown). The nature of this uncertainty is not defined, only its capacity to affect something else or its properties. 
'''Risk''' is defined as " ... an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives." (Ibid.) Beyond this formal definition, PMI adds context when, in its risk management section (Sec. 11 ), the BoK states that risk is a caused event or condition with multiple causes and impacts. The BoK even offers specific techniques for mapping cause-and-effect relationships and influence diagrams. (Sec. 11.2.2.5) Not as evident but equally important is the recognition that not only is risk caused but the impact is "triggered". The risk trigger is an event or situation that signals that it is about to occur (Glossary, pg. 566). Risk conditions are factors that affect or otherwise contribute to risk, such as project stakeholders. Risk implicitly retains some residual element of probability in it, or a value of less than 1.0 with risk that approaches a level of near certainty is termed "issues" or "realized risk" .(Op. Cit., pg 309). PMI also links risk to underlying variations in project outcomes. (Ibid.) Risk is based upon data and information which can be assessed as to its quality (Sec. 11.3.2.3). This analysis looks to determine the value of the information and examines the degree to which the risk information is understood as well as its " ... accuracy, quality, reliability and integrity ... " (Ibid.) Part of that is understanding the relationship between occurrence and impact as outlined in PMl's Figure 11-10 Risk Impact Matrix and developing a model for prioritizing risk products and setting the threshold for action.
====Commentary:====
For defining and identifying risk, PMI notes in Sec. 11.2.2.4 that " ... Every project and its plan is conceived and developed based on a set of hypotheses, scenarios, or assumptions." Part of the risk analysis process is identifying risks to the project, such as inaccuracy, instability, inconsistency, or incompleteness of assumptions. Similarly, the quality of management plans, as well as their consistency with others and the project objectives and assumptions are 11 •• .indicators of risk in the project." (Sec. 11.2.2.1) 
Similarly, the role of engineering economics in risk is embedded or implied in the PMI definition of risk when the BoK discusses risk identification as a process of assessing which risks out of the total risk exposure for the project "may affect" the project and distilling their characteristics. Embedded in this statement is the concept that not all risks can cause economic impacts (positive and negative) to the project. Also embedded is the requirement to understand risk characteristics to identify project risk (Cost and schedule). Lastly, risk identification is an iterative process of incrementing project risk documentation, much like project scope, cost, and schedule documentation. 
Lastly, PMI acknowledges the complex constitution of risk aggregates or the "sum" of all risks in its material, but is it a sum? Risk information has descriptive and explanatory content. These are knowledge and meta-knowledge components. Understanding project assumptions is project-specific knowledge, but identifying risks from assumption inaccuracy, instability, inconsistency, or incompleteness requires professional or program meta-knowledge. 
'''Uncertainty exists in all projects, but risk, a subset of uncertainty with material economic impacts, is a key meta-knowledge element for civil engineering practice.'''
===Software Development Process- Risk Focused===
The Unified Process requires the project team to focus on addressing the most critical risks early in the project life
cycle. The deliverables of each iteration, especially in the Elaboration phase, must be selected to ensure that the greatest risks are addressed first. <ref> Booch, Grady, Ivar Jacobson, and James Rumbaugh. "The unified software development process."
Reading: Addison Wesley (1999).</ref>
===American Society of Civil Engineers (ASCE)===
ASCE does not define uncertainty, but the ASCE CE Body of Knowledge (CEBoK) uses the term in 22 places (uncertainty) and describes properties in 13 places (uncertainties). 
The CEBoK does not use the phrase "uncertainty and risk" as PMI and AACE do in their publication. ASCE reverses the two (Risk and uncertainty) in twelve places regarding technical outcomes (Outcome 12). A fundamental difference between PMI and ISO is that risk is considered a subset of uncertainty. ASCE does not define "risk" but uses the term 25 times in the same document. Almost all are in the context of variation of design parameters and not in the classical context of cost, schedule uncertainty, and risk.
====Commentary:====
...
=== American Association of Cost Engineers (AACE) ===
AACE defined uncertainty, risk, and other related terms in its Risk Management Dictionary. <ref>Risk Management Committee uRisk Management Dictionary, Cost Engineering Vol. 37/No. 10, OCTOBER 1995. </ref>
AACE's overall strategy was to define risk and other terms that stem from base uncertainty. AACE first defined uncertainty as .... "(t)he total range of events that may happen and produce risks (including both threats and opportunities) affecting a project (see opportunities, events, conditions, risk, and threats" where the following sub-definitions apply:
** Biases--A lack of objectivity based on the individual's position or perspective. Systematic and predictable relationships between a person's opinion or statement and his/her underlying knowledge or circumstances. Note: there may be "system biases" as well as "individual biases."
** Condition (Uncertain Condition)-Any specific identifiable circumstance (such as the rate of inflation or the quality of labor available) that might affect the outcome of the project
** Event (Uncertain Condition)-is a specific identifiable action (such as a large government project being started in the same labor area as your project) or an act of nature that might happen and that (if it does happen) could affect the outcome of the project.
** Opportunities are Uncertain events that could improve the results or improve the probability that the desired outcome will happen
** Threats are Uncertain events that are potentially negative or reduce the probability that the desired outcome will happen.
*** AACE defines Risk as an "ambiguous term" (sic) that is synonymous with uncertainty or a negative subset such as "threats", or an overall negative impact of all possible uncertainties.
====Commentary:====
...
==Limitations of the definition==
...
==Working definition==
...
==Beneficial outcomes==
...
==See also==
Wiki articles on the project.

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==Notes==
<references group="note" />
<li> Implicit in this definition of uncertainty as a "state of' is ... </li>
: " ... a conceptualization of uncertainty as a subjective, cognitive experience of people--a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one's lack of knowledge, without which one could not feel uncertain; '''uncertainty is a form of "meta-cognition" ... ( or alternatively, its main component, meta-knowledge) ... -a knowing about knowing.'''" (Han, 2011, op. cit., Emphasis added)
</ol>
<ol start="5">
<li> Cox wanted his system to satisfy the following conditions:</li>
: Divisibility and comparability- The plausibility of a statement is a real number and depends on the information we have related to the statement.
: Common sense - Plausibilities should vary sensibly with the assessment of plausibilities in the model.
: Consistency - If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). -preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf
</ol>
<ol start="6">
<li> The utility of existing taxonomies of uncertainty in civil engineering and, by extension, "risk" has been unsatisfactory for this very reason. This is particularly true in cases where the source of the uncertainty is conflicting versus incomplete information. See Regan, et. al.,"A taxonomy and treatment of uncertainty for ecology and conservation biology" (2002)
for a survey of scientific taxonomies for uncertainty.</li>
<li> Regan et al. argue that " ... genuine examples of this kind of uncertainty are hard to find. Even classic cases of random experiments like coin tosses and the throwing of dice are deterministic; it is just that we do not have enough information about the dynamic processes and initial conditions to make any sensible estimates about the outcomes. Such processes are, for all intents and purposes, inherently random, but they are not genuinely inherently random. For similar reasons, complex systems such as ecosystems and weather patterns are unlikely to be inherently random. Similarly, chaotic systems are entirely deterministic. They are unpredictable because the deterministic processes generating them and the relevant initial conditions are hard
to fully specify (see Stewart 1989, Sugihara et al. 1990)." (Regan (2002) </li>
<li> The CEBoK uses the phrase "uncertainty and risk" twice in reference to scheduling (Section 6.5,2) and cost estimating (Section 7.2,2) as well as reversing the two (Risk and Uncertainty) also in two places in discussing project life cycle (both in Section 2.4.1) </li>
</ol>

==References==
<references />

Uncertainty and Risk in Civil Engineering Practice

2024-11-24T16:53:30Z

WikiSysop: /* Logical Framework for Specific Risk Taxonomies */

'''Life is short and the art long; the occasion instant, experiment perilous, decision difficult.''' (Hippocrates as quoted in Fox,)
:''See also [[Project Definition]] , [[Project Life Cycle and Phase Models]] , [[Project Delivery Methods]]''
:''See also [[Technical Outcomes for Civil Engineering:Risk and Uncertainty]]''
==Basic Considerations==
As human beings,'' ... being alive means seeking opportunities and taking risks.'' As knowledge professionals
living in the 21st century, this means coping with an increasingly complex number of uncertainties for humans
living in this environment. We seek to understand better how these uncertainties can be characterized and
managed. The essence of this article and the experience for engineers in general and civil engineers in particular is
that manageable uncertainty is, by definition, termed risk, and its kindred cousin is termed hazard. This causes us to experience the
:" ''... human dread of and fascination for risk and the increasingly important role of risk analysis within societies ...'' " <ref name="McDaniels"> McDaniels, Timothy, and Mitchell Small. Risk analysis and society: an interdisciplinary characterization of the field. Cambridge University Press, 2004, page 1 </ref> (Ibid.)
and, by extension, civil engineering. McDaniels et al. argue that risk management has been
fundamental to our social and governance development for the past 10,000 years. The scale and shape of the
uncertainties faced in this period shaped the societies that have developed today. The central thrust of this effort
over the centuries has been to reshape and re-frame our understanding and conception of uncertainty from one of
complete unknowing and simple acceptance as our fate in life to one of management (cf "''Against the Gods''"
concept in Bernstein's book <ref name = Bernstein">Bernstein, Peter L., and Jesse Boggs. Against the gods. Simon & Schuster 1997.</ref>.
: ''All the knowledge professions and disciplines have struggled with managing uncertainty, for it is impossible to manage unbounded uncertainty.'' <ref group="note">See McDaniels et al. 2004 for an extensive discussion and bibliography on the historical development of Risk Analysis and some key milestones in risk analysis in the 20th century.</ref>
As outlined below, professions such as civil engineering have successfully acted in the face of uncertainty. It lies in the profession's ability to reduce a wide variety of uncertainties into increasingly smaller and crucially bounded subsets that can be managed. These are called 'risks. More recently, this process has evolved, and individual disciplines such as Civil Engineering (CE) have developed their knowledge and models to perform risk analysis. Risk is also taught as a distinct discipline and specialty practice within civil engineering. Still, it has some unique features that set it apart from other more classical practices within CE. As such, it is one of the first of some very specialized CE practices that use knowledge about civil engineering knowledge, or meta-knowledge. Other examples of civil engineering metaknowledge are project controls and quality controls.
==Semantic, Epistemic and Logical frameworks==
The semantic, epistemic, and logical frameworks for uncertainty and risk have several dimensions and layers of logical frameworks. The semantics problems include interchangeable usages for risk and uncertainty and hazard, uncertain and imperfect, and a lack of definitional material or context. The semantical scheme for this article will be to proceed from the distinction between certainty and uncertainty through marginal refinements and reductions of uncertainty up to the point of causal uncertainties. Beyond this point of semantics will be the logic frameworks or the subject of taxonomies of professional knowledge. First, it is about simple, testable phenomena, and then, it moves on to complex taxonomy schemes for artificially constructed phenomena or engineering projects.
<ref group="note">TBD</ref>
==Semantics framework==
===Certainty and Uncertainty===
''''' There is no such thing as absolute certainty, but there is assurance sufficient for the purposes of human life.''''' ([https://en.wikipedia.org/wiki/John_Stuart_Mill John Stuart Mill]) 
'''''If you tried to doubt everything, you would not get as far as doubting anything. The game of doubting itself presupposes certainty.''''' ([https://en.wikipedia.org/wiki/Ludwig_Wittgenstein Wittgenstein] # 115 from On [https://en.wikipedia.org/wiki/On_Certainty Certainty]) 
It is important to note that the references to epistemic or epidemiological knowledge in this article are assumed to relate to three forms of knowledge, namely:
* Knowledge that ( [https://en.wikipedia.org/wiki/Descriptive_knowledge descriptive or declarative or propositional knowledge])
* Knowledge how ( or "[https://en.wikipedia.org/wiki/Procedural_knowledge know-how]"), and
* [https://en.wikipedia.org/wiki/Knowledge_by_acquaintance Knowledge by acquaintance].
'''Certainty''' has been defined as "an epistemic property" of knowledge in all its forms and the state of our beliefs about that knowledge.
<ref>Certainty, Stanford Encyclopedia of Philosophy, accessed on September 12, 2015, at http://plato.stanford.edu/entries/certainty/#ConCer </ref>
Certainty about any belief about knowledge implies that it is not subject to doubt or skepticism. This immunity to criticism can be dogmatically based, emphasizing the importance of a propositional-based sense of truth over experiential, sensory perceptions. <ref>Definition of [https://en.wikipedia.org/wiki/Dogmatic_theology Dogmatic theology or belief], accessed at Wikipedia</ref>
: '''The demarcation line between dogmatic and non-dogmatic beliefs lies in the presence and recognition of specific criteria and information that would make the believer change their beliefs.'''
An empirical framework of [https://en.wikipedia.org/wiki/Testability testability] and [https://en.wikipedia.org/wiki/Falsifiability falsification] is required to recognize this and limit dogmatism. For example, one could argue that people would change their minds if God asked them to. Similarly, one could construct a concept of epistemic as opposed to dogmatic belief certainty as the ability to know anything that one chooses to know and can be known or [https://en.wikipedia.org/wiki/Omniscience inherent omniscience].

A second kind of certainty is epistemic, when conviction reflects the highest possible support for a belief. In this sense, knowledge is separate from beliefs, although someone may have beliefs about a property, such as certainty of knowledge. Logically, it has been shown that there will be unprovable statements within the system for any such knowledge system. Secondly, the knowledge system cannot demonstrate its own consistency. ([https://en.wikipedia.org/wiki/Gödel%27s_incompleteness_theorems Gödel's incompleteness theorems])
: '''Certainty, in real life, is useless or often damaging (the idea is that "total security from error" is impossible in practice, and a complete "lack of doubt" is undesirable)''' ([[https://en.wikipedia.org/wiki/Certainty Physicist Carlo Rovelli]])
'''Uncertainty''', on the other hand, arises immediately in the slightest amount of doubt or criticism.
<ref group="note"> The Merriam-Webster dictionary defines uncertainty as “the quality or state of being uncertain,” which is something of a circular definition. Likewise, one could define uncertainty as the state of not being certain.

Synonyms are distrust, doubt, misgiving, mistrust, reservation, skepticism, and suspicion. Another writer added indefinite, indeterminate, not certain to occur, problematical, unreliable, untrustworthy, unknown beyond doubt, dubious, doubtful, not clearly identified or defined, not constant, variable, and fitful to the list.
:(Han, Paul KJ, William MP Klein, and Neeraj K. Arora. "Varieties of Uncertainty in Health Care: A Conceptual Taxonomy." Medical Decision Making 31.6 (2011): 828-838.)
Han et al. noted that any definition of uncertainty clearly encompasses "...numerous types, sources, and manifestations of uncertainty, and ...(any) ...useful working definition of uncertainty needs to specify the concept underlying these varied meanings of the term."</ref>
Implicit in this definition of uncertainty as a “state of” is ...
"...a conceptualization of uncertainty as a subjective, cognitive experience of people—a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one’s lack of knowledge, without which one could not feel uncertain; uncertainty is a form of “meta-cognition” ...(or its main component, meta-knowledge)... —a knowing about knowing." (Han, 2011, op. cit., Emphasis added)
Similarly, uncertainty could be defined as "...any departure from the unachievable goal of complete [https://en.wikipedia.org/wiki/Determinism determinism]."
<ref name="Walker1">Walker, W. E., Harremoës, P., Rotmans, J., Van Der Sluijs, J. P., Van Asselt, M. B., Janssen, P., & Krayer von Krauss, M. P. (2003). Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support. Integrated assessment, 4(1), 5-17.</ref>
Reasoning under uncertainty is very different than performing the same under certainty. In reasoning under certainty, one has complete knowledge and deduces without doubt and equally important, without limitation, thereby concluding free from error. Reasoning under uncertainty, one works in a state of incomplete, inconsistent, and limited knowledge; doubts cloud any statements or assertions and thereby taint any deductions/inferences resulting in the potential for error. <ref group="note">Implicit in this definition of uncertainty as a “state of” is ...
"...a conceptualization of uncertainty as a subjective, cognitive experience of people—a state of mind rather than a feature of the objective world. The defining feature of this state, furthermore, appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one’s lack of knowledge, without which one could not feel uncertain; uncertainty is a form of “meta-cognition” ...(or alternatively, its main component, meta-knowledge)... —a knowing about knowing." (Han, 2011, op. cit., Emphasis added)</ref>
:'''Knowledge professions reason in a state of incomplete, inconsistent and limited knowledge with doubts that cloud any statements or assertions and taint any deductions/inferences resulting in the potential for error.'''
:''' It is also important to note the semantical and logical correlation between 'reasoning under uncertainty' or 'acting in the face of uncertainty' with the 'potential for' or 'presence of' error. The presence of uncertainty is invariably linked to the potential presence of error.''' The [https://en.wikipedia.org/wiki/Contingency_(philosophy) contingent] nature of uncertainty logically implies the contingent nature of error.
:'''Knowledge Professions use error as a proxy for uncertainty such that within discipline knowledge frameworks, uncertainty can be managed.''' The rationale for this belief is that error can be reliably described, quantified, explained, and ultimately reduced in ways that uncertainty cannot.

'''Uncertainty''' implies a range of variation that is impossible in certainty. Concepts and definitions of uncertainty are unbounded and vast but start from the point of complete and total ignorance. In describing uncertainty, statements can range from complete ignorance to relatively high degrees of belief that there is less potential for error in our reasoning or action. The latter is based upon confidence in justified knowledge, favorable past experience, and reliable parameters/models. One can't make that statement about ranges in certainty. If certainty, by definition, precludes doubt of any type or nature, then how could that be graduated to any degree? 
Uncertainty, on the other hand, can be reduced through disciplined efforts to acquire knowledge. In short, we can reduce or even manage uncertainty (to a degree); how can certainty be improved? Are there higher degrees of perfection? The answer is no. We reason from an uncertain starting point. Yet this effort presumes, as Mill and Wittgenstein postulated, that we can develop or acquire an increasing degree of belief or conviction that there is less potential for error in our reasoning or action. This position assumes that some way exists to identify what a lesser degree of uncertainty would look like.
:'''Knowledge professions acquire an increasing belief or conviction that there is less potential for error in their reasoning or action based on justified knowledge, favorable past experience, and reliable parameters/models.'''
Uncertainty is, in some form, amenable to description and explanation through observation and analysis. It is explainable and predictable to such a degree that it interests professionals such as scientists and engineers. Professional knowledge of this type has explanatory or predictive power, albeit limited in scope and application to relevant subjects such as physical objects or phenomena and functionality.
:'''The very act of reducing the scope of uncertainty to a limited set of phenomena implies choice and, ultimately, the decision to act in the face of such uncertainty, but acting or judging in this manner adds more value than doing the same under relative ignorance.'''
'''Uncertainty''', as described above, implies a hybrid nature. Examples of this are economic or decision-theoretic applications. In economics, [https://en.wikipedia.org/wiki/Perfect_information perfect information] allows one within the limiting framework of perfect competition in economic games to make decisions with 'perfect knowledge.' The crucial difference here is the distinction between '[https://en.wikipedia.org/wiki/Perfection perfection],' broadly, a state of completeness and lawlessness, and 'perfect, which can be thought of as an analytical limit that is approachable but can never be attained. Arbitrarily delimiting the realm of knowledge into defined, finite boundaries allows the simulated production of perfect choice, assumed crucially, to be free from error. The hybrid nature of this form of 'simulated' certainty produced within boundaries defined by assumptions and, therefore, clouded by doubt is itself a form of uncertainty. The importance of this is the semantical and logical association that exists between certainty and perfection versus the hybrid concept of a perfect anything, whether knowledge, infraction, competition, etc., or in the case of engineering, elastic, permeable, conductive, etc., being contained within an admittedly imperfect matrix of uncertainty.
:'''Simply put, a finite, heavily bounded piece of uncertain knowledge can be improved through the simulated use of [https://www.merriam-webster.com/dictionary/assumed assumedly] perfect information, but the perfection of knowledge cannot be attained or simulated.'''
Engineering and Economics, for example, simulate finite elements of perfect knowledge or information, heavily bounded with assumptions and a range of applications. By doing so, these professions reduce the bounds and variability of uncertainty and thereby manage it. Using these hybrid models to acquire knowledge takes place in an economy (regulated or institutional) and environment (transparent and structured), which puts pressure on the various professions to recognize and use qualitative and quantitative knowledge better to reduce uncertainties in their activities. The challenges in this reflect the nature of the profession's or discipline's knowledge (e.g., scientific, engineering, or medical), the policies and structure of the relevant professional knowledge bodies, process objectives, constraints, and "the elusive demands of politics." <ref>Walker, Vern R. "The siren songs of science: toward a taxonomy of scientific uncertainty for decisionmakers." Conn. L. Rev. 23 (1990): 567.</ref>
One common theme runs through all of these efforts to address the presence of uncertainty in professional activities. 
:"The available scientific information upon which a decision must be made is almost always a mixture of engineering knowledge and uncertainty. Regardless of the information or methodology, there is the potential for error. This is true regardless of the relevant science, whether archaeology or aeronautics, economics or engineering, pharmacology or toxicology or epidemiology. Achieving the best social decisions requires not only understanding and using what we know but also appreciating and weighing the extent of our uncertainty. In making the best use of ... information in ... decision-making, it is still true that the beginning of wisdom is knowing what it is we do not know." (Vern, Op. cit., Reformatted and engineering substituted for scientific) 
'''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty to develop explanations, calculate, and make predictions. This professional knowledge converges on but never reaches certainty, producing a "legitimacy" from the fruitfulness of its use.''' <ref>Randomness Is Unpredictability, Antony Eagle, The British Journal for the Philosophy of Science, Vol. 56, No. 4 (Dec., 2005), pp. 749-790 </ref> 
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=== Bias and Uncertainty ===
Up to this point, the concept of error, as outlined in uncertainty, could easily be simulated by a truly random variable. There should be no detectable differences around the unseen or specified statistical mean. However, several instances in experience point to a tendency towards one extreme or a pattern of error, a collective that is in itself an error of errors, namely [https://en.wikipedia.org/wiki/Bias bias].

Arguably, this could be part of the choice uncertainty discussion below, but it makes more sense to be looked at on its own. Acting in the face of uncertainty is an exercise of personal knowledge or experience. Whether using professional knowledge or individual experience, the potential is there to make a series of choices that, in some instances, make errors more likely than they would be if considered in the context of a statistical analysis.

This tendency for an individual to make the same error in a predictable or repeatable manner is termed bias. Personal experience, beliefs, knowledge, data, and models all have inherent built-in factors, errors, or defects that create predictable error patterns when applied in decision-making. Walker (1998) argues implicitly that such bias can also be considered systemic error. This type of uncertainty is difficult to identify except on theoretical grounds when the magnitude and direction of the bias are known. Most of these biases, whether systemic or knowledge-based, individual or practice-based, require disciplined efforts to "validate" such methods, models, and judgments at all layers while bounding the uncertainty.
: This is the challenge to professional knowledge that must be overcome, namely, developing and validating models and methods for managing bounded uncertainty that minimize this tendency towards repeatable groups of error or bias. Therefore, from this point forward, the discussion will refer to 'uncertainty and its proxies, error, and bias.'
===Propagating Error and Uncertainty===
Partitioning uncertainty and its proxies, error, and bias into logically distinct subsets involves or is associated with multiple-step execution or choice frameworks. Combining those measurements or choices into single parameters or choices involves, in some cases, assembling the data/information in a unique or limited range of sequences. This implies that associated errors in observing, calculating, or choosing will have unequal impacts or consequences depending on where in the chain the error occurs. Also implied in this is the possibility that subsequent errors will be causally linked to some degree. (In statistical practice, this depends upon whether the errors are independent of each other or correlated, specifically, co-variant.)
:'''Professions develop knowledge of how uncertainty and its proxies, error, and bias propagate through an ordered and normative series of observations or choices. This allows the profession to prioritize error and bias reduction to achieve an optimized reduction of uncertainty.'''
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===Cognition, Metacognition and Uncertainty===
The concept of choice and the resultant error in uncertainty can be further delimited depending on the role of [https://en.wikipedia.org/wiki/Cognition cognition], [https://en.wikipedia.org/wiki/Metacognition meta-cognition], and independent external elements. The rationale for this argument is that such choice activity in the face of uncertainty is a conscious, cognitive act.

An argument could be made that such cognition is a prerequisite for exercising choice and introducing error, as presented and discussed above. The counterfactual to this argument is the example of uninformed choice. Choices introduce as much as errors or more as informed choices could be made. (???) Choice, in this sense, is indifferent to knowledge. Purposeful choice in an environment of objectives requires knowledge, method, and experience, but uninformed, speculative choice does not.
:"''' Cognition''' is the set of all mental abilities and processes related to knowledge, attention, memory and working memory, judgment and evaluation, reasoning and "computation," problem-solving and decision making, comprehension and production of language, etc. Human cognition is conscious and unconscious, concrete or abstract, as well as intuitive (like knowledge of a language) and conceptual (like a model of a language). Cognitive processes use existing knowledge and generate new knowledge." (Wikipedia)
:"'''Metacognition''' is "cognition about cognition", or "knowing about knowing" and can take many forms. It includes knowledge about when and how to use particular strategies for learning or problem-solving. There are generally two aspects of [https://en.wikipedia.org/wiki/Metacognition metacognition]: knowledge about cognition and regulation of cognition."
Some types of metacognition knowledge are:
:'''Person knowledge''' (declarative knowledge), which is understanding one's own capabilities.
:'''Task knowledge''' (procedural knowledge), which is how one perceives the difficulty of a task, which is the content, length, and type of assignment.
:'''Strategic knowledge''' (conditional knowledge) is one's capability to use strategies to learn information. (Accessed at Wikipedia)
Like metacognitive knowledge, metacognitive regulation or "cognitive control" contains three essential skills.
:'''Planning:''' refers to the appropriate selection of strategies and the correct allocation of resources that affect task performance.
:'''Monitoring:''' refers to one's awareness of comprehension and task performance.
:'''Evaluating:''' refers to appraising the final product of a task and the efficiency at which the task was performed. This can include re-evaluating strategies that were used. (Accessed at Wikipedia)
Nothing has been said up to this point that limits this structure to an individual, or a collective, or two entities making cognitive choices that are incompatible or inconsistent to varying degrees. Interpretations of this scheme include an individual professional exercising judgment in a decision that, in turn, relies upon the collective judgment and decision of the discipline as an underlying basis versus the decision of another individual to oppose/protest or otherwise contest that decision. Thus, the framework of cognitive/meta-cognitive applies to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, economics of regulatory practice, and game theory.
:By solving useful problems in the face of inherent uncertainty, discipline-specific frameworks of cognitive/meta-cognitive activity apply to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, the economics of regulatory practice, and game theory.
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===Probability and Uncertainty===
Probability is a [https://en.wikipedia.org/wiki/Coherence_theory_of_truth coherent] approach to uncertainty in the physical and mathematics or "classical" domains such as engineering. <ref>See Colyvan for a critique of this claim, Colyvan Mark. "Is probability the only coherent approach to uncertainty?." Risk Analysis 28.3 (2008): 645-652.</ref> and is the "...is certainly the best-known and most widely used formalism for quantifying uncertainty.<ref>Morgan, Millett Granger, Max Henrion, and Mitchell Small. Uncertainty: a guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge University Press, 1992.</ref> [https://en.wikipedia.org/wiki/Cox%27s_theorem Cox’s theorem] ("Any measure of belief is [https://en.wikipedia.org/wiki/Isomorphism isomorphic] (but not necessarily equal) to a probability measure") is a well-known argument for the validity of that argument.
<ref group="note">Cox wanted his system to satisfy the following conditions: 
Divisibility and comparability – The plausibility of a statement is a real number and is dependent on the information we have related to the statement.
:Common sense – Plausibilities should vary sensibly with the assessment of plausibilities in the model.
:Consistency – If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). — preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf</ref>

Another writer, [https://en.wikipedia.org/wiki/Frank_Knight Knight] (1921,1956), presented the following taxonomy of probabilities:
<ref>As presented and discussed in Runde, Jochen. "Clarifying Frank Knight's discussion of the meaning of risk and uncertainty." Cambridge Journal of Economics 22.5 (1998): 539-546.</ref>
: '''Classical 'a priori' probability:''' As an idealized model, numerical probabilities are computed based on generalized principles of assigning equal likelihoods and mutually exhaustive possible outcomes (Runde, 1998) to all set members. Such probabilities are assigned to "...outcomes based on a judgment of indifference between those outcomes, that is, based on the absence of any evidence of real influences in play that may render any one outcome more or less probable than any other." (Runde, op. cit.) This builds upon Knights's concept of an 'absolutely homogeneous classification of instances that are completely identical except for [https://en.wikipedia.org/wiki/Indeterminate_(variable) indeterminate] factors. This judgment of probability or logical probability is on the same logical plane as the propositions of mathematics and ultimately [https://en.wikipedia.org/wiki/Inductivism inductions] from experience.'(Knight, 1956, pg.225)
: '''Bayesian 'a priori' probability:''' In contrast to interpreting probability as the "frequency" or "propensity" of some phenomenon, [https://en.wikipedia.org/wiki/Bayesian_probability Bayesian probability] is a quantity that we assign to represent a state of knowledge or a state of belief. In this view, probability is assigned to a hypothesis, often using the basis of past or prior experience. In contrast, under the frequentist view, a hypothesis is typically tested without being assigned a probability based on prior experience. The Bayesian interpretation of probability can be seen as an extension of propositional logic that enables reasoning with hypotheses, i.e., propositions whose truth or falsity is uncertain. Bayesian probability belongs to the category of evidential probabilities; to evaluate the probability of a hypothesis, the Bayesian probabilist specifies some prior probability, which is then updated in the light of new, relevant data (evidence). The Bayesian interpretation provides a standard set of procedures and formulae to perform this calculation. <ref>See also Larvor, B. "After Popper, Kuhn, and Feyerabend: Recent Issues in Theories of Scientific Method." Metascience (2002).</ref>
: '''Statistical or 'a posterior probability:''' Empirical evaluation of the frequency of association between predicates, not analyzable into varying combinations of equally probable or logical, 'a priori' alternatives. Knight argued that any "high degree of confidence" that the judgment that experience will remain valid for future predictions is "...still based on an 'a priori" judgment of indeterminateness." (Knight, Op. Cit.) Knight's argument to support this is what will be discussed further below that first, "...the impossibility of eliminating all factors not really indeterminate; and, second, the impossibility of enumerating the equally probable alternatives involved and determining their mode of combination so as to evaluate the probability by a priori calculation." (Knight, Op. Cit.) Knight noted that the main difference between this form and that of logical or inductive probability was the presence of empirical information, which allowed the analyst to identify propensity and direction in the observed phenomena. In this case, 'a priori' probability values may be derived from first principles and "statistical probabilities are determined a posteriori by the empirical method of counting instances." (Runde [1998] quoting Knight, op. cit.)
:'''Estimates:''' The distinction here is that there is no valid basis ('a priori' or 'a posteriori') for any classifying phenomena. For Knight, this form of probability presented the greatest logical difficulties of all ... but its distinction from the other types must be emphasized, and some of its complicated relations indicated..." (pp. 224-5, emphasis in the original)

Knight's concept of probability can be viewed as "..a continuum of probability situations, depending on the degree of homogeneity of the 'instances' in question" <ref>Runde, Jochen. "Clarifying Frank Knight's discussion of the meaning of risk and uncertainty." Cambridge Journal of Economics 22.5 (1998): 539-546.</ref>; i.e. going from a logically distinct but equally likely set of elements to a unique set with one member with statistical frequency in the middle. Knight's main motivation for distinguishing between a priori probability and statistical probability underscores his opinion that "(i) the 'mathematical or a priori type of probability is practically never met with in business, while the second is extremely common'; and (ii) 'the statistical treatment never gives closely accurate quantitative results' (Runde quoting Knight pp. 215-16). Runde argues that Knight couldn't conceive of (empirically tabulated) occurrences that are used in daily commerce classes of instances that we have to make do within the course of everyday economic life could be divided into subclasses of instances that are sufficiently homogeneous to permit the determination of what he calls 'real' probability (p. 217).
:'''In modeling degrees of uncertainty, any measure of belief is isomorphic but not necessarily equal to a probability or statistics measure'''.
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===Explanation, Prediction and Uncertainty===
It should be clear that the general notion of uncertainty synonymous with complete ignorance must be bounded as part of a scheme to produce a useful concept of uncertainty in professional practice such as civil engineering.
:The first principle that must be introduced is that such uncertainty must be bounded by reliable and valuable knowledge gained from past experience combined with analytical and computational capabilities to address an immediate and real problem of interest. This could be viewed as the economic interest argument. Only uncertainties associated with physical phenomena and economic scarcity will be addressed.
:The second principle is that the knowledge model is capable of producing statements or assertions in the form of hypotheses that themselves are testable. [https://en.wikipedia.org/wiki/Testability Testability], in this sense, is defined as the property applying to an empirical hypothesis and involves two components:
::The logical property that is variously described as contingency, defeasibility, or falsifiability, which means that counterexamples to the hypothesis are logically possible. Contrast this to a [https://en.wikipedia.org/wiki/Tautology_(logic) logical tautology], which is always true that is true in every possible interpretation. The practical feasibility of observing a reproducible series of such counterexamples if they do exist. In short, a hypothesis is testable if there is some real, non-zero expectation of deciding whether it is true or false of verifiable, reproducible experience. Upon this property of its constituent hypotheses rests the ability to decide whether a theory can be supported or falsified by actual experience data. (Source Wikipedia.)
'''Knowledge models must be capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria described above in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error and bias.'''
===Variability and Aleatory Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge referred to as epistemic uncertainty. Acting in the face of epistemic uncertainty introduces choice uncertainty (discussed below) and results in the production of explanations and predictions. It is implied that there will be testable hypotheses in the form of outcomes of interest to the knowledge professions. Inherent in nature is a variation of results, which may appear to uncertain but isn't. Observations of phenomena will not be the same, and part of the experimental process is to narrow those results down to within an acceptable level of tolerance. Statistical measures seek to identify underlying and unobserved measures of central tendency. These parameters are not directly observed but are derived to a degree of confidence.
In some cases, variation around a mean or within a statistical range may be perfectly acceptable to the knowledge profession. Examples of this are in the physical sciences and medicine. In Engineering and, to a lesser extent, economics, the process or phenomenon may be required to be more controlled to a narrower range than what a natural or "unmanaged" amount of variation would allow. Several courses of action present themselves.
:The first would be to ignore the aleatory phenomenon and choice, which is rejected outright.
:The second would be to assume that, in effect, such variation is itself a form of minimally managed uncertainty and treat it the same as epistemic uncertainty. This is not a desirable outcome as reducing Aleatory error and bias requires
:A third approach would be to acknowledge that process variation is, in some forms, manageable. This means making choices and acting in a way that presumes that the natural occurrence of error can be reduced, or more importantly, bias can be reduced. There is ample precedent for the third approach in civil engineering design and project management. In this sense, the argument is made that the aleatory response to managed efforts results in an elastic variation range. Presumably, an effective management effort either "shifts" the central tendency of the process results or reduces the variation range of occurrence or some other population parameter. Choosing to act and accept that narrower range rather than the broader natural variation may result in an outcome that, while well within the bounds of the expected variation, is outside the arbitrary control limits established by the choice and action. While this phenomenon mimics the broader uncertainty and its proxy error, it is not an error. The information on the variation was known, and the ability to produce narrower results was an error, albeit a pseudo-error, when compared to the broader classes of uncertainty discussed above, such as total ignorance.
::Looking at uncertainty using this third approach means that the error in choosing a new target parameter for this process may contain epistemic errors (our knowledge of the phenomenon may be erroneous, or the model flawed), aleatory uncertainties as discussed, or, more importantly, choice error or bias in thinking that we can move the process results to meet the requirements.
:'''In civil engineering practice, this may mean applying professional judgment in developing partitioning schemes or allocating uncertainty between the three dimensions (Epistemic, Aleatory, and Behavioral) of uncertainty.'''
Lastly, raising the topic like this brings the questions of effectiveness and efficiency to the fore. Advancing knowledge models that reduce error and bias at first are largely choice models resulting in justifications and favorable experiences. At some point in the process, the knowledge profession advances explanations and predictions that require reliable mathematical or logical concepts that are reduced to models and parameters. The question of efficiency becomes important; the broader uncertainty may have been reduced, but the process is economically inefficient. The profession is urged to become more frugal or, for example, reduce the variability of the outcomes. Knowledge professions often operate under conditions that require them to acquire knowledge and reduce uncertainty effectively and efficiently. Doing so requires understanding both epistemic uncertainty, as discussed above, and statistical process variation or aleatory variation.
:'''Knowledge professions manage and reduce uncertainty in the form of error and bias in an economically effective and efficient manner. This effort requires understanding both epistemic and aleatory uncertainty.'''
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===Choice and Behavioral Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge, referred to as epistemic uncertainty, as well as the inherent variations in results, or what was termed aleatory uncertainty. Acting in the face of that epistemic and aleatory uncertainty introduces yet another uncertainty into the process, namely choice uncertainty. This uncertainty reflects uncertainties associated with first-person and third-person choice. This is particularly relevant for engineering with project stakeholders.
===Summary of Semantics Framework for Uncertainty===
Professional disciplines have gradually re-framed their understanding of uncertainty and, thru experience and analysis, have arrived at the following meta-knowledge concepts of uncertainty:
* '''Uncertainty and its proxy, error, have three components: [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic epistemic uncertainty], [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic aleatory uncertainty], and behavioral uncertainty.'''
* '''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty using non-dogmatic beliefs, which recognize the existence of specific criteria and information that would make the believer change their beliefs using coherent approaches such as probability.'''
* '''Knowledge professions develop knowledge of how uncertainty and its proxy, error, propagate through an ordered and normative series of observations or choices that meet testability criteria in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error. An error can then be described, explained, and reduced in ways that uncertainty cannot. This allows the professions to prioritize error reduction to achieve an optimized reduction of uncertainty and produce a "legitimacy" from the fruitfulness of its use.'''
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===Uncertainty versus Risk/Hazard===
Up to this point, nothing has been said about risk or hazard. Now, risk can be defined. Simply put risk or hazard are finite, logically distinct subsets of the broader uncertainty. Risk and hazard are currently defined interchangeably. First and foremost, risk is a specific subset of general uncertainty. Any arbitrary subset of general uncertainty can be defined as a risk if it meets the following criteria:
* Uncertainty is associated with the potential for material error when acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena.
* Uncertainty and its proxy, error, can be partitioned into logically distinct subsets associated with multiple-step execution or choice frameworks in physical environments linked to a unique or limited range of sequences.
**Subcriteria 1:Resulting errors will have varying impacts or consequences depending on where the error occurs and the degree of causal linkage or influence in the chain.
**Subcriteria 2:Potential errors can be sequenced and prioritized to achieve an optimized reduction of error or bias and, by proxy, uncertainty over time.
* Uncertainty and its proxies, error bias, and environments are isomorphic to coherent approaches such as probability.
* Uncertainty and its proxies, error, and bias are isomorphic to coherent approaches such as knowledge models capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria in 'reasoning under uncertainty.'
This is a definition of general risk. There is no differentiation for engineering or economics, medicine or insurance. There is no difference between a 'good' risk consequence and a 'negative' one. Redefining general risk into simpler terms gives the following:
:'''General Risk is a subset of general uncertainty that is associated with the potential for material errors and biases when sequentially acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena; isomorphic to probability measures and methods and capable of testability.'''
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==Logical Framework for Specific Risk Taxonomies==
As noted above, at the most fundamental level, uncertainty is ".....the subjective perception of ignorance."
<ref name="Han">Han, Paul KJ, William MP Klein, and Neeraj K. Arora. "Varieties of Uncertainty in Health Care A Conceptual Taxonomy." Medical Decision Making 31.6 (2011): 828-838.</ref>
At this point in the discussion, the focus has moved from discussing uncertainty to its framed subset, risk.
:'''Further, the discussion moves from general risk to a professional or discipline-specific risk [https://en.wikipedia.org/wiki/Taxonomy taxonomy]. Beyond this point, risk will be used in place of uncertainty.'''
Han et al. note that, in general, "(t) taxonomies are valuable not only in their comprehensiveness but in their coherent reduction of uncertainty to conceptually discrete elements."
Uncertainty (and, by implication, risk or hazard) taxonomies offer an approach or tool to more precisely identify and define ontological uncertainty so that it can be quantified, analyzed, and communicated. Viewed this way, uncertainty is not a single, monolithic phenomenon but "...multi-dimensional with theoretically distinct domains and constructs that are potentially measurable and related to different outcomes, mechanisms of action, and management strategies." <ref name="Han"/> Taxonomic schemes for classifying the different kinds of scientific and, by extension, engineering uncertainty require identifying the various kinds of potential error associated with descriptive scientific or engineering information and knowledge. An example of this is the following quote from a legal authority:
:"I would especially stress the need for an agency to disclose the uncertainty that surrounds its determinations. '''And by uncertainty, I mean the agency's ignorance as well as its quantitative estimates of error."''' (Emphasis added) [15]
'''Given the necessity of acting in the face of enormous uncertainties [16], discipline taxonomies must offer a clearly coherent and probabilistic approach to uncertainty that is sufficiently general in nature, logically distinct and exhaustive in scope and "...provide decision-makers with a foundation for understanding the nature of discipline information..."''' [17]
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=== Scientific Risk Taxonomies ===
The [https://en.wikipedia.org/wiki/Scientific_method scientific method] is a body of techniques for investigating phenomena in its environment, acquiring new knowledge, or correcting and integrating previous knowledge. Inherent in that process are uncertainties of many forms. Any one taxonomy of [https://en.wikipedia.org/wiki/Uncertainty scientific uncertainty] has largely been focused on statistical models used to assess and quantify sampling, errors, and parameters, although several different taxonomies are conceivable: note-24 [18]
====Environment-specific, Phenomena oriented, model-centric taxonomy of uncertainties====
** [https://en.wikipedia.org/wiki/Uncertainty_quantification#Sources_of_uncertainty '''Parameter uncertainty'''] the source of which is model parameters that are inputs to the computer model (mathematical model) but whose exact values are unknown and cannot be controlled, or whose values cannot be exactly inferred by statistical methods. Subsets of this type of uncertainty include but are not limited to:
***'''Experimental uncertainty''' is also known as observation error, which comes from the variability of experimental measurements. An example is repeating an experiment measurement several times using exactly the same settings for all inputs/variables and recording the variability. It is a subset of the larger uncertainty component, parameter uncertainty.
*** '''Parametric variability''' comes from the variability of the input variables of the phenomena model and is also a subset of the larger uncertainty component, parameter uncertainty.
** '''Structural uncertainty''', or model inadequacy, model [https://en.wikipedia.org/wiki/bias bias] or "[https://en.wikipedia.org/wiki/Systemic_bias systemic bias]", or model discrepancy, which comes from the lack of knowledge of the underlying true state of the phenomenon or its environment. It depends on how accurately a mathematical model describes the true state of the phenomenon or what is termed "model fitness". Due to the inherent nature of uncertainty in any knowledge body (scientific or engineering), models are only an approximation to reality. Therefore, any conclusions drawn from the model can be very misleading when the underlying basis is not plausible or lacks validity. '''note-25 [19]]'''
*** What is missing in this context is any concept of output variability due to the model itself. Part of this is due to the lack of recognition of "process" in scientific investigation. The concept of the scientific method started with a single investigator, such as Galileo or Michael Faraday, working in their laboratories. It has grown into planet and solar system scale experiments such as data gathered on planetary flybys such as Mars, Pluto, and Ceres to the hunt for the Higgs Boson. Ultimately, science has become more like engineering in the scale and complexity of its investigations and theory aggregates, such as the "Unified theory" of particle physics. Subsets of this type of uncertainty include but are not limited to:
*** '''Algorithmic uncertainty''', or numerical uncertainty, comes from numerical errors and numerical approximations per implementation of the computer model. Most models are too complicated to solve exactly. For example, the [https://en.wikipedia.org/wiki/Finite_element_method finite element method] or [https://en.wikipedia.org/wiki/Finite_difference_method finite difference method] [may be used to approximate a solution [https://en.wikipedia.org/wiki/Partial_differential_equation partial differential equation], which introduces numerical errors. Other examples are numerical integration and infinite sum truncation, which are necessary approximations in numerical implementation.
*** '''Interpolation uncertainty''' comes from a lack of available data collected from computer model simulations or experimental measurements. For other input settings that don't have simulation data or experimental measurements, one must interpolate or extrapolate to predict the corresponding responses.
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====Commentary====
A more comprehensive taxonomy of uncertainties that acknowledges knowledge, data, and linguistic uncertainties is:
*'''Epistemic uncertainty''' -[https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Epistemic uncertainty] or systematic uncertainty, in contrast, reflects limitations in the current “state of knowledge” underlying models themselves, originates from competing theories or models, is not readily quantifiable, and is manifest by subjective confusion or indecision. It includes uncertainty due to measurement limitations, insufficient data, extrapolations and interpolations, and variability over time and space. Epistemic uncertainty is uncertainty about "...some determinate fact ... because of a lack of complete information. '''cite_note-26 [20]]''' Epistemic uncertainty can be classified into six main types:'''cite_note-27 [21]]'''
*''' Measurement error''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Observational_error Measurement error] is uncertainty that manifests itself as (apparently) random variation in the measurement of a quantity. Repeated measurements will vary and demonstrate classic statistical behavior.
* '''Systematic error''' - [https://en.wikipedia.org/wiki/Observational_error Systematic error] occurs due to bias in the measuring equipment, model, analysis, observation, or sampling procedure. It is formally defined as the difference between the true value of the quantity of interest and the value to which the mean of the results converges as sample or data sizes increase. Unlike measurement error, it is not (apparently) random, and therefore, results subject to systematic error alone do not vary about a true value. Systematic error can result from deliberate judgment to exclude (or include) data, parameters, or models that ought not to be excluded (or included).
** "The only way to deal with systematic error is to recognize a bias in the process, model, or procedure and remove it. Systematic error, however, is notoriously difficult to recognize except on theoretical grounds. Corrections may only be applied when the magnitude and direction of the bias are known. Such corrections underlie the application of double-sampling methods in environmental science." (Regan, et. al., op. cit.)
*'''Natural variation''' - [https://en.wikipedia.org/wiki/Natural_process_variation Natural variation] or underlying process variation occurs in systems that "...change (concerning time, space, or other variables) in ways that are difficult to predict...across the full range of temporal and spatial values (or other related variables)." '''note-28 [22]'''
** This form of uncertainty is reduceable utilizing tools such as [https://en.wikipedia.org/wiki/Statistical_process_control statistical process control] and replication of experiments or observations.
*'''Inherent randomness''' - [https://en.wikipedia.org/wiki/Randomness randomness] or [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty aleatory uncertainty] exists because the system is"...in principle, irreducible to a deterministic one (the most well-known case is described by [https://en.wikipedia.org/wiki/Uncertainty_principle Heisenberg’s uncertainty principle] in quantum mechanics)." '''note-29 [23], note-30 [Note 7]'''
** Even if the argument is accepted that it is unlikely that any given physical system at some level is inherently random, it is important for any taxonomy to distinguish between systems that appear random due to incomplete or inconsistent information and those that are intrinsically random. (Again, see Regan (2002)).
*'''Model uncertainty''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling Model uncertainty] occurs in the process of generating a model as a conceptual representation of a particular phenomenon to create a [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling#Overview simplified reflection of reality]. This selectivity of factors and variables creates uncertainty in at least three ways. (Regan (2002))
** '''Variables and processes''' that are regarded as relevant often represent a trade-off between system knowledge and assumed states and model objectives;
** '''Models''' depict observed processes using logical or mathematical constructs based on underlying theories about system states or dynamics using continuous equations to describe discrete processes.
** '''Curve fitting''' (including interpolation and extrapolation) with mathematical expressions using model variables where inputs are discrete data points.
** Regan (2002) argues that '''model uncertainty''' is "...notoriously difficult to quantify and impossible to eliminate ...(and)...(t)he only reliable way of determining how appropriate a model is for prediction is to perform validation studies."
** Subjective judgment occurs due to the interpretation of scarce or error-prone data.
*** The only way to address this type of uncertainty is to "...assign a degree of belief about an event in the form of a subjective probability." (Regan (2002))
*Linguistic uncertainty - Linguistic uncertainty, or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. '''note-31 [24]], note-32 [25]]'''
** '''Context dependence''' is uncertainty concerning the context in which a statement is to be understood;
** '''Ambiguity''' occurs when words have multiple meanings, and further reduction to a single meaning is not logically possible in a given context.
** '''Underspecificity''' occurs in the presence of "unwanted generality" or multiple interpretations, and reduction to a smaller set or even a single interpretation is not logically possible.
*Stochastic or statistical uncertainty - Stochastic or statistical uncertainty, which is sometimes known as [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Aleatoric uncertainty] as well as the subject of [https://en.wikipedia.org/wiki/Stochastic_control stochastic control] pertains to the parameters of a risk model, originates from sampling or measurement error, and can be quantified and mathematically expressed (e.g., using confidence intervals).
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== Uncertainty Taxonomies in a Legal Environment ==
From a legal perspective, a definition of taxonomy is dictionary-based, such as "the systematic distinguishing, ordering, and naming of type groups within a subject field." given by Walker (op. cit.). Such legal concepts of scientific or engineering taxonomies are not required to be epistemically complete. Still, they must cover "the most significant aspects of ...(current, good discipline practices) ... and of the descriptive information commonly encountered by decision-makers" in a logically distinct manner. (Walker, 1991, p. 571) These taxonomies must be sufficient to catalog the types of uncertainty associated with descriptive assertion, including the critical subset of cause and effect assertions. This legal/decision-maker framework has presented descriptive uncertainty as having six components (Walker, 1991). Although not part of the Walker taxonomy, underlying the scheme is linguistic uncertainty, or "vagueness," as a source of uncertainty, including uncertainties due to context dependence, ambiguity, and under-specificity. '''note-33 [26]]''' Arguably, these uncertainties underlie the most basic element in the taxonomy.
* '''Linguistic '''- or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. (Regan, 2002)
* '''Conceptual'''- the definition by choice and design of descriptive concepts or variables to be used as [https://en.wikipedia.org/wiki/Predicate_(grammar) predicates] where the subject of an assertion identifies what is being discussed and the predicate provides information about the topic or characterizations such as what the subject is, what the subject is doing, or what the subject is like (Wikipedia).
* "Conceptual uncertainty, or the potential for conceptual error, arises whenever predication occurs. Whenever a concept is used to describe something, using certain concepts instead of others begins to structure how we understand the object, event, or instance under discussion. Predication or conceptualization generates useful information about things, but it also can inhibit our ability to think about those things with concepts other than those selected. The concepts used may not be the most fruitful or the best designed- either for scientific purposes or for making wise, fair, effective, and efficient decisions." (Edited for reading, Walker, 1991)
* Similarly, choice and design of descriptive variables enhance and restrict data set membership, creating potentially incomplete, incompatible, or inconsistent data sets, "...the classification categories employed, and the relationships among those categories (nominal, ordinal or scalar)." '''note-34 [27]]'''
* Concept uncertainty '''note-35 [28]''' like systematic error discussed above is notoriously difficult to recognize, except on theoretical grounds, and the magnitude and direction of the bias are known.
Both terms could be analogized to that of [https://en.wikipedia.org/wiki/Accuracy_and_precision#Terminology_of_ISO_5725 accuracy] in [https://www.bipm.org/en/publications/guides/ ISO 5725] which looks at the ability of the concept or system's ability to produce results that are proximate to the "true" value or state of the phenomenon. Conceptual uncertainty and systemic bias are related to another measure of uncertainty: validity. Walker has defined validity as the ability of a concept to quantitatively describe and explain what the phenomenon objective "truly" is. Walker also ties the concepts together when he states, "Validity concerns the "accuracy" of the measurement data, not its precision." (Walker, 1998) 
Additionally, conceptual uncertainty and associated validity issues could be propagated throughout this taxonomy. Every component of action and decision in the face of uncertainty raises validity issues. Walker recognized this when the author defined epistemic choice for concepts used throughout the other uncertainty components or layers.
* '''Measurement''' uncertainty or Misclassification error- the application of the underlying concepts or variables to specific, individual cases and the uncertainty of the reliability (Walker, 1998) of the resultant data. (It is important to remember that assessment observation and measurement are used interchangeably in this analysis.) Thus, an assessment method or procedure is considered "reliable" or "precise" in the scientific terms of ISO 5715 if it repeatedly produces consistent results.
* Measurement uncertainty is logically distinct from conceptual uncertainty, which can only occur after any conceptual uncertainty has been established. Still, misclassification can occur even when the conceptual uncertainty has been minimized. Conversely, underlying errors or errors made in measurement or classification propagate thru the reasoning chain and reduce the quality or value of the final result.
* While uncertainties associated with quantitative measurements or assessments can be statistically evaluated and described, uncertainties related to qualitative assessments are more problematic.
**Working through the taxonomic chain from base concept to causal explanation requires amassing and integrating qualitative information and quantitative data into input parameters for mathematical models. Developing procedures for producing consistent, repeatable sets of information from assessments that minimize conceptual error or systemic bias requires professional efforts to "validate" such methods, models, and judgments at all layers in the process. Validation as a process is something that civil engineering as a practice needs to embrace more frequently.
* '''[https://en.wikipedia.org/wiki/Sampling Sampling]'''- in the classical statistics sense, is the selection of a limited subset of a larger population to use as a basis for making estimates about the population in the form of attributes, proprieties, or parameters. The resulting information would be determinative in making forecasts about future population sampling or possessing a desired amount of predictive power. The uncertainty about the ability of this information to correctly predict future samples is termed sampling error.
* '''Modeling'''- Modeling uncertainty arises whenever a claim is made that out of a class of candidate relationships and variable pairings, one variable pairing inclusive of constants is chosen that possesses a particular or persistent mathematical relationship to another variable X. Modeling errors arise in selecting the wrong variable pair and constants or by incorrectly specifying its constants. (Walker, 1991, pg. 599)
** '''Causal'''- In causal modeling and analysis, the relationship between causal variables is not strictly a mathematical function. Still, it makes testable predictions and explains "...how a system of variables works, why a system works the way it does, or why it makes sense to think of certain variables as a system" at all." (Walker, 1991, pg. 609)
** '''Epistemic'''- the choice of interpretations for fundamental, logical concepts used throughout the other components or layers.
The components of this uncertainty taxonomy can combine in different ways to "...produce different aggregate uncertainties." '''note-36 [29]'''
:''' Only the scientific and engineering professions possess the capacity to develop and validate procedures and models that combine the different kinds of qualitative '''note-37 [30]''' and quantitative information and their associated potential for error into an aggregate measure or "scalar variables" of uncertainty.'''note-38 [31]]
Scalar variables are quantitative variables whose categories are related by some measure of the relevant property's incremental frequency, degree, or amount. (Walker, 1991) Examples are Modulus of Elasticity, Compression stress in elastic materials, etc.

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=== Economics Uncertainty Taxonomies ===
Like scientific concepts of uncertainty, economics is environment-specific (in this case, read sector of economy or markets), phenomenon-oriented (in this case, read human activity and manufactured phenomena such as firms and markets guided by human judgment and decision), and model-centric.
:''' Economics is not interested in all forms of uncertainty, only specific, meaningful subsets possessing casual connections with material impacts, often labeled as 'risks'.'''
One of its earlier writers ([https://en.wikipedia.org/wiki/Frank_Knight#Life_and_career Knight], 1921, 1948, 1957) attempted to define "uncertainty" as the presence of "defects of managerial knowledge" (or [https://en.wikipedia.org/wiki/Knightian_uncertainty knightian uncertainty] and thus risk centric) as risk that is immeasurable, not possible to calculate.(Wikipedia) Risk in this sense was defined as "the ordinary risks of business activity which can ... be reduced... by applying the insurance principle." Knight does acknowledge that risk is a specific and unambiguous subset of uncertainty. (Risk, Uncertainty and Profit, 1957, pg. 19) For Knight, measuring uncertainty meant measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences, which Knight referred to as "the imperfection of knowledge". (Risk, Uncertainty and Profit, 1957, pg. 197) One of Knight's many contributions to this analysis was recognizing information and knowledge's role in economic activity.
:"''(W)e are concerned only to emphasize the fact that '''knowledge is in a sense variable in degree and that the practical problem may relate to the degree of knowledge rather than to its presence or absence in toto.''' We live only by knowing something about the future, while the problems of life, or conduct at least, arise from knowing so little. This is as true of business as of other spheres of activity. The situation's essence is action according to opinion, of greater or less foundation and value, neither entire ignorance nor complete and perfect information, but partial knowledge. If we are to understand the workings of the economic system, we must examine the meaning and significance of uncertainty; and to this end, some inquiry into the nature and function of knowledge itself is necessary.''" (Knight, pg. 199) Emphasis added
Knight also reinforced the difficulties in classifying uncertainties when he analyzed life insurance. In this case, accidental death was an uncertain phenomenon compared to sickness and accident, where an "...objective description and classification of cases was impossible..." (Knight, pg. 248) Another factor that made uncertainties ineligible as risks and, therefore, uninsurable because they were unclassifiable was the exercise of judgment in making decisions by the businessman. (Knight, pg. 251)
This is a simple classification of economic uncertainty and, as an idealization, is philosophically controversial. '''note-39 [32]'''
* Uncertainty and information about the economic environment are distinct from uncertainty about others’ behavior or choices.
* Risk as a specific subset of uncertainty implies that "...all possible acts are known, all possible outcomes arising from each act are known, and it is possible to assign probabilities to each act." '''note-40 [33]'''
Like the economic [https://en.wikipedia.org/wiki/Theory_of_the_firm theory of the firm], an economic theory of uncertainty seeks to explain and predict the nature of a specific subset of uncertainty (in this case, risk relating to physical phenomena and human activity), including its behavior, structure, and relationship to the universal uncertainty of physical phenomena. In this sense, uncertainty, like firms and markets, can establish price equilibriums under the right circumstances different from those that might have occurred in either of the other two. Such a theory of uncertainty offers a partitioning scheme that decomposes uncertainty into distinct and meaningful subsets, of which risk is a significant one. It also explains how the presence and variability of knowledge cause or " drive" economic consequences. Unlike physical phenomena, economic activity creates artificial objects such as products, firms, mediums of exchange, markets, economies, and knowledge about those activities. The economics of uncertainty exists as an alternative to "certainty" models that assume away imperfect knowledge and unknown choice/judgment preferences when it is more efficient for economic decision-making. This also allows the introduction of the logical concept of "hazard" versus "risk". Frank used hazard extensively in Risk, Uncertainty, and Profit, but not to the extent that one could say it was interchangeable. An example of this is in the term "[https://en.wikipedia.org/wiki/Moral_hazard Moral hazard]," where a firm could engage in "riskier" and potentially "uninsurable" behavior if the information were transparent, and, therefore, insurance coverage of losses is limited. note-41 [34]], note-42 [35]]

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=== Uncertainty and Risk in Policy and Regulatory Environment ===
:''"Hume's 'just reasoner', when faced with a difficult public policy decision in current times, would probably commission a risk analysis. But how could they incorporate 'a degree of doubt, caution, and modesty?"'' '''note-43 [36]'''
The problem of "decision-making in the face of uncertainty" is how to regulate based on incomplete information that has the potential to be materially inaccurate. A contextual assumption is that we need to evaluate decision rules for dealing with uncertainty. As a social enterprise, risk regulation, whatever its substantive objectives, should be as '''effective, efficient, and equitable''' as practically possible. These "three E's" form a set of "process objectives" or "meta-goals." From the uncertainty standpoint, causal information can be usefully divided into two major categories: information about groups and information about individuals.

Each category, group, and individual has its distinctive types of inherent uncertainty. These are logically distinct, generally independent, and cumulative, contributing to '''(?????)'''

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=== Engineering Uncertainty Taxonomies ===
Like scientific and economic concepts of uncertainty, engineering is environment-specific, phenomena-oriented, model-centric, external choice, and driven by decision-making in the face of uncertainty. Like economics and unlike science, engineering is not interested in all forms of uncertainty, only those with determinable and material consequences. Like economics, engineering conceives risk as a specific and unambiguous subset of uncertainty determined by measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences. Also, engineering recognizes the role information and, by extension, knowledge plays in constructing the built environment.
'''note-44 [37]]''' Like economics, engineering differs from economics and scientific uncertainty in its interest in behavioral uncertainty.
*"Behavioral or interaction uncertainty is how individuals or organizations act or interact. Behavioral uncertainty arises from four sources: design uncertainty, requirement uncertainty, volitional uncertainty, and human errors." '''note-45 [38]]'''
** A design uncertainty is a choice among alternatives over which an individual or group of individuals exercises direct control but has not yet decided upon.
** Requirement uncertainty includes parameters of interest to and determined by the stakeholder, independent of the engineer or designer.
** Volitional uncertainty is uncertainty about what the subject him/herself will decide. Other people’s future actions and conduct are not entirely predictable, particularly when dealing with other organizations.
** Human errors occur during the development of a system or project due to blunders or mistakes by an individual or individuals.

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==Practice Frameworks for Uncertainty and Risk in Civil Engineering Practice==
===PMIBoK===
PMI does not define uncertainty, but the PMI Body of Knowledge uses the term in 45 places (uncertainty) and as describing properties in 8 places (uncertainties). [See Note 8] PMI does define "risk" and uses the term over 1,300 times in the same document. In its glossary, PMI also defines terms such as "threat" and "opportunity" but not events.[391 The BoK contextually defines uncertainty when it states that project risk has its origins in the uncertainty present in all projects. (Op. Cit., pg 309) 
'''Uncertainty''' is contextually defined and taken as having the property of affecting project execution and the ability to meet stakeholder expectations. Uncertainty is more than the sum of all individual risks (known and unknown). The nature of this uncertainty is not defined, only its capacity to affect something else or its properties. 
'''Risk''' is defined as " ... an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives." (Ibid.) Beyond this formal definition, PMI adds context when, in its risk management section (Sec. 11 ), the BoK states that risk is a caused event or condition with multiple causes and impacts. The BoK even offers specific techniques for mapping cause-and-effect relationships and influence diagrams. (Sec. 11.2.2.5) Not as evident but equally important is the recognition that not only is risk caused but the impact is "triggered". The risk trigger is an event or situation that signals that it is about to occur (Glossary, pg. 566). Risk conditions are factors that affect or otherwise contribute to risk, such as project stakeholders. Risk implicitly retains some residual element of probability in it, or a value of less than 1.0 with risk that approaches a level of near certainty is termed "issues" or "realized risk" .(Op. Cit., pg 309). PMI also links risk to underlying variations in project outcomes. (Ibid.) Risk is based upon data and information which can be assessed as to its quality (Sec. 11.3.2.3). This analysis looks to determine the value of the information and examines the degree to which the risk information is understood as well as its " ... accuracy, quality, reliability and integrity ... " (Ibid.) Part of that is understanding the relationship between occurrence and impact as outlined in PMl's Figure 11-10 Risk Impact Matrix and developing a model for prioritizing risk products and setting the threshold for action.
====Commentary:====
For defining and identifying risk, PMI notes in Sec. 11.2.2.4 that " ... Every project and its plan is conceived and developed based on a set of hypotheses, scenarios, or assumptions." Part of the risk analysis process is identifying risks to the project, such as inaccuracy, instability, inconsistency, or incompleteness of assumptions. Similarly, the quality of management plans, as well as their consistency with others and the project objectives and assumptions are 11 •• .indicators of risk in the project." (Sec. 11.2.2.1) 
Similarly, the role of engineering economics in risk is embedded or implied in the PMI definition of risk when the BoK discusses risk identification as a process of assessing which risks out of the total risk exposure for the project "may affect" the project and distilling their characteristics. Embedded in this statement is the concept that not all risks can cause economic impacts (positive and negative) to the project. Also embedded is the requirement to understand risk characteristics to identify project risk (Cost and schedule). Lastly, risk identification is an iterative process of incrementing project risk documentation, much like project scope, cost, and schedule documentation. 
Lastly, PMI acknowledges the complex constitution of risk aggregates or the "sum" of all risks in its material, but is it a sum? Risk information has descriptive and explanatory content. These are knowledge and meta-knowledge components. Understanding project assumptions is project-specific knowledge, but identifying risks from assumption inaccuracy, instability, inconsistency, or incompleteness requires professional or program meta-knowledge. 
'''Uncertainty exists in all projects, but risk, a subset of uncertainty with material economic impacts, is a key meta-knowledge element for civil engineering practice.'''
===Software Development Process- Risk Focused===
The Unified Process requires the project team to focus on addressing the most critical risks early in the project life
cycle. The deliverables of each iteration, especially in the Elaboration phase, must be selected to ensure that the greatest risks are addressed first. <ref> Booch, Grady, Ivar Jacobson, and James Rumbaugh. "The unified software development process."
Reading: Addison Wesley (1999).</ref>
===American Society of Civil Engineers (ASCE)===
ASCE does not define uncertainty, but the ASCE CE Body of Knowledge (CEBoK) uses the term in 22 places (uncertainty) and describes properties in 13 places (uncertainties). 
The CEBoK does not use the phrase "uncertainty and risk" as PMI and AACE do in their publication. ASCE reverses the two (Risk and uncertainty) in twelve places regarding technical outcomes (Outcome 12). A fundamental difference between PMI and ISO is that risk is considered a subset of uncertainty. ASCE does not define "risk" but uses the term 25 times in the same document. Almost all are in the context of variation of design parameters and not in the classical context of cost, schedule uncertainty, and risk.
====Commentary:====
...
=== American Association of Cost Engineers (AACE) ===
AACE defined uncertainty, risk, and other related terms in its Risk Management Dictionary. <ref>Risk Management Committee uRisk Management Dictionary, Cost Engineering Vol. 37/No. 10, OCTOBER 1995. </ref>
AACE's overall strategy was to define risk and other terms that stem from base uncertainty. AACE first defined uncertainty as .... "(t)he total range of events that may happen and produce risks (including both threats and opportunities) affecting a project (see opportunities, events, conditions, risk, and threats" where the following sub-definitions apply:
** Biases--A lack of objectivity based on the individual's position or perspective. Systematic and predictable relationships between a person's opinion or statement and his/her underlying knowledge or circumstances. Note: there may be "system biases" as well as "individual biases."
** Condition (Uncertain Condition)-Any specific identifiable circumstance (such as the rate of inflation or the quality of labor available) that might affect the outcome of the project
** Event (Uncertain Condition)-is a specific identifiable action (such as a large government project being started in the same labor area as your project) or an act of nature that might happen and that (if it does happen) could affect the outcome of the project.
** Opportunities are Uncertain events that could improve the results or improve the probability that the desired outcome will happen
** Threats are Uncertain events that are potentially negative or reduce the probability that the desired outcome will happen.
*** AACE defines Risk as an "ambiguous term" (sic) that is synonymous with uncertainty or a negative subset such as "threats", or an overall negative impact of all possible uncertainties.
====Commentary:====
...
==Limitations of the definition==
...
==Working definition==
...
==Beneficial outcomes==
...
==See also==
Wiki articles on the project.

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==Notes==
<references group="note" />
<li> Implicit in this definition of uncertainty as a "state of' is ... </li>
: " ... a conceptualization of uncertainty as a subjective, cognitive experience of people--a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one's lack of knowledge, without which one could not feel uncertain; '''uncertainty is a form of "meta-cognition" ... ( or alternatively, its main component, meta-knowledge) ... -a knowing about knowing.'''" (Han, 2011, op. cit., Emphasis added)
</ol>
<ol start="5">
<li> Cox wanted his system to satisfy the following conditions:</li>
: Divisibility and comparability- The plausibility of a statement is a real number and depends on the information we have related to the statement.
: Common sense - Plausibilities should vary sensibly with the assessment of plausibilities in the model.
: Consistency - If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). -preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf
</ol>
<ol start="6">
<li> The utility of existing taxonomies of uncertainty in civil engineering and, by extension, "risk" has been unsatisfactory for this very reason. This is particularly true in cases where the source of the uncertainty is conflicting versus incomplete information. See Regan, et. al.,"A taxonomy and treatment of uncertainty for ecology and conservation biology" (2002)
for a survey of scientific taxonomies for uncertainty.</li>
<li> Regan et al. argue that " ... genuine examples of this kind of uncertainty are hard to find. Even classic cases of random experiments like coin tosses and the throwing of dice are deterministic; it is just that we do not have enough information about the dynamic processes and initial conditions to make any sensible estimates about the outcomes. Such processes are, for all intents and purposes, inherently random, but they are not genuinely inherently random. For similar reasons, complex systems such as ecosystems and weather patterns are unlikely to be inherently random. Similarly, chaotic systems are entirely deterministic. They are unpredictable because the deterministic processes generating them and the relevant initial conditions are hard
to fully specify (see Stewart 1989, Sugihara et al. 1990)." (Regan (2002) </li>
<li> The CEBoK uses the phrase "uncertainty and risk" twice in reference to scheduling (Section 6.5,2) and cost estimating (Section 7.2,2) as well as reversing the two (Risk and Uncertainty) also in two places in discussing project life cycle (both in Section 2.4.1) </li>
</ol>

==References==
<references />

Uncertainty and Risk in Civil Engineering Practice

2024-11-24T16:49:31Z

WikiSysop: /* Probability and Uncertainty */

'''Life is short and the art long; the occasion instant, experiment perilous, decision difficult.''' (Hippocrates as quoted in Fox,)
:''See also [[Project Definition]] , [[Project Life Cycle and Phase Models]] , [[Project Delivery Methods]]''
:''See also [[Technical Outcomes for Civil Engineering:Risk and Uncertainty]]''
==Basic Considerations==
As human beings,'' ... being alive means seeking opportunities and taking risks.'' As knowledge professionals
living in the 21st century, this means coping with an increasingly complex number of uncertainties for humans
living in this environment. We seek to understand better how these uncertainties can be characterized and
managed. The essence of this article and the experience for engineers in general and civil engineers in particular is
that manageable uncertainty is, by definition, termed risk, and its kindred cousin is termed hazard. This causes us to experience the
:" ''... human dread of and fascination for risk and the increasingly important role of risk analysis within societies ...'' " <ref name="McDaniels"> McDaniels, Timothy, and Mitchell Small. Risk analysis and society: an interdisciplinary characterization of the field. Cambridge University Press, 2004, page 1 </ref> (Ibid.)
and, by extension, civil engineering. McDaniels et al. argue that risk management has been
fundamental to our social and governance development for the past 10,000 years. The scale and shape of the
uncertainties faced in this period shaped the societies that have developed today. The central thrust of this effort
over the centuries has been to reshape and re-frame our understanding and conception of uncertainty from one of
complete unknowing and simple acceptance as our fate in life to one of management (cf "''Against the Gods''"
concept in Bernstein's book <ref name = Bernstein">Bernstein, Peter L., and Jesse Boggs. Against the gods. Simon & Schuster 1997.</ref>.
: ''All the knowledge professions and disciplines have struggled with managing uncertainty, for it is impossible to manage unbounded uncertainty.'' <ref group="note">See McDaniels et al. 2004 for an extensive discussion and bibliography on the historical development of Risk Analysis and some key milestones in risk analysis in the 20th century.</ref>
As outlined below, professions such as civil engineering have successfully acted in the face of uncertainty. It lies in the profession's ability to reduce a wide variety of uncertainties into increasingly smaller and crucially bounded subsets that can be managed. These are called 'risks. More recently, this process has evolved, and individual disciplines such as Civil Engineering (CE) have developed their knowledge and models to perform risk analysis. Risk is also taught as a distinct discipline and specialty practice within civil engineering. Still, it has some unique features that set it apart from other more classical practices within CE. As such, it is one of the first of some very specialized CE practices that use knowledge about civil engineering knowledge, or meta-knowledge. Other examples of civil engineering metaknowledge are project controls and quality controls.
==Semantic, Epistemic and Logical frameworks==
The semantic, epistemic, and logical frameworks for uncertainty and risk have several dimensions and layers of logical frameworks. The semantics problems include interchangeable usages for risk and uncertainty and hazard, uncertain and imperfect, and a lack of definitional material or context. The semantical scheme for this article will be to proceed from the distinction between certainty and uncertainty through marginal refinements and reductions of uncertainty up to the point of causal uncertainties. Beyond this point of semantics will be the logic frameworks or the subject of taxonomies of professional knowledge. First, it is about simple, testable phenomena, and then, it moves on to complex taxonomy schemes for artificially constructed phenomena or engineering projects.
<ref group="note">TBD</ref>
==Semantics framework==
===Certainty and Uncertainty===
''''' There is no such thing as absolute certainty, but there is assurance sufficient for the purposes of human life.''''' ([https://en.wikipedia.org/wiki/John_Stuart_Mill John Stuart Mill]) 
'''''If you tried to doubt everything, you would not get as far as doubting anything. The game of doubting itself presupposes certainty.''''' ([https://en.wikipedia.org/wiki/Ludwig_Wittgenstein Wittgenstein] # 115 from On [https://en.wikipedia.org/wiki/On_Certainty Certainty]) 
It is important to note that the references to epistemic or epidemiological knowledge in this article are assumed to relate to three forms of knowledge, namely:
* Knowledge that ( [https://en.wikipedia.org/wiki/Descriptive_knowledge descriptive or declarative or propositional knowledge])
* Knowledge how ( or "[https://en.wikipedia.org/wiki/Procedural_knowledge know-how]"), and
* [https://en.wikipedia.org/wiki/Knowledge_by_acquaintance Knowledge by acquaintance].
'''Certainty''' has been defined as "an epistemic property" of knowledge in all its forms and the state of our beliefs about that knowledge.
<ref>Certainty, Stanford Encyclopedia of Philosophy, accessed on September 12, 2015, at http://plato.stanford.edu/entries/certainty/#ConCer </ref>
Certainty about any belief about knowledge implies that it is not subject to doubt or skepticism. This immunity to criticism can be dogmatically based, emphasizing the importance of a propositional-based sense of truth over experiential, sensory perceptions. <ref>Definition of [https://en.wikipedia.org/wiki/Dogmatic_theology Dogmatic theology or belief], accessed at Wikipedia</ref>
: '''The demarcation line between dogmatic and non-dogmatic beliefs lies in the presence and recognition of specific criteria and information that would make the believer change their beliefs.'''
An empirical framework of [https://en.wikipedia.org/wiki/Testability testability] and [https://en.wikipedia.org/wiki/Falsifiability falsification] is required to recognize this and limit dogmatism. For example, one could argue that people would change their minds if God asked them to. Similarly, one could construct a concept of epistemic as opposed to dogmatic belief certainty as the ability to know anything that one chooses to know and can be known or [https://en.wikipedia.org/wiki/Omniscience inherent omniscience].

A second kind of certainty is epistemic, when conviction reflects the highest possible support for a belief. In this sense, knowledge is separate from beliefs, although someone may have beliefs about a property, such as certainty of knowledge. Logically, it has been shown that there will be unprovable statements within the system for any such knowledge system. Secondly, the knowledge system cannot demonstrate its own consistency. ([https://en.wikipedia.org/wiki/Gödel%27s_incompleteness_theorems Gödel's incompleteness theorems])
: '''Certainty, in real life, is useless or often damaging (the idea is that "total security from error" is impossible in practice, and a complete "lack of doubt" is undesirable)''' ([[https://en.wikipedia.org/wiki/Certainty Physicist Carlo Rovelli]])
'''Uncertainty''', on the other hand, arises immediately in the slightest amount of doubt or criticism.
<ref group="note"> The Merriam-Webster dictionary defines uncertainty as “the quality or state of being uncertain,” which is something of a circular definition. Likewise, one could define uncertainty as the state of not being certain.

Synonyms are distrust, doubt, misgiving, mistrust, reservation, skepticism, and suspicion. Another writer added indefinite, indeterminate, not certain to occur, problematical, unreliable, untrustworthy, unknown beyond doubt, dubious, doubtful, not clearly identified or defined, not constant, variable, and fitful to the list.
:(Han, Paul KJ, William MP Klein, and Neeraj K. Arora. "Varieties of Uncertainty in Health Care: A Conceptual Taxonomy." Medical Decision Making 31.6 (2011): 828-838.)
Han et al. noted that any definition of uncertainty clearly encompasses "...numerous types, sources, and manifestations of uncertainty, and ...(any) ...useful working definition of uncertainty needs to specify the concept underlying these varied meanings of the term."</ref>
Implicit in this definition of uncertainty as a “state of” is ...
"...a conceptualization of uncertainty as a subjective, cognitive experience of people—a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one’s lack of knowledge, without which one could not feel uncertain; uncertainty is a form of “meta-cognition” ...(or its main component, meta-knowledge)... —a knowing about knowing." (Han, 2011, op. cit., Emphasis added)
Similarly, uncertainty could be defined as "...any departure from the unachievable goal of complete [https://en.wikipedia.org/wiki/Determinism determinism]."
<ref name="Walker1">Walker, W. E., Harremoës, P., Rotmans, J., Van Der Sluijs, J. P., Van Asselt, M. B., Janssen, P., & Krayer von Krauss, M. P. (2003). Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support. Integrated assessment, 4(1), 5-17.</ref>
Reasoning under uncertainty is very different than performing the same under certainty. In reasoning under certainty, one has complete knowledge and deduces without doubt and equally important, without limitation, thereby concluding free from error. Reasoning under uncertainty, one works in a state of incomplete, inconsistent, and limited knowledge; doubts cloud any statements or assertions and thereby taint any deductions/inferences resulting in the potential for error. <ref group="note">Implicit in this definition of uncertainty as a “state of” is ...
"...a conceptualization of uncertainty as a subjective, cognitive experience of people—a state of mind rather than a feature of the objective world. The defining feature of this state, furthermore, appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one’s lack of knowledge, without which one could not feel uncertain; uncertainty is a form of “meta-cognition” ...(or alternatively, its main component, meta-knowledge)... —a knowing about knowing." (Han, 2011, op. cit., Emphasis added)</ref>
:'''Knowledge professions reason in a state of incomplete, inconsistent and limited knowledge with doubts that cloud any statements or assertions and taint any deductions/inferences resulting in the potential for error.'''
:''' It is also important to note the semantical and logical correlation between 'reasoning under uncertainty' or 'acting in the face of uncertainty' with the 'potential for' or 'presence of' error. The presence of uncertainty is invariably linked to the potential presence of error.''' The [https://en.wikipedia.org/wiki/Contingency_(philosophy) contingent] nature of uncertainty logically implies the contingent nature of error.
:'''Knowledge Professions use error as a proxy for uncertainty such that within discipline knowledge frameworks, uncertainty can be managed.''' The rationale for this belief is that error can be reliably described, quantified, explained, and ultimately reduced in ways that uncertainty cannot.

'''Uncertainty''' implies a range of variation that is impossible in certainty. Concepts and definitions of uncertainty are unbounded and vast but start from the point of complete and total ignorance. In describing uncertainty, statements can range from complete ignorance to relatively high degrees of belief that there is less potential for error in our reasoning or action. The latter is based upon confidence in justified knowledge, favorable past experience, and reliable parameters/models. One can't make that statement about ranges in certainty. If certainty, by definition, precludes doubt of any type or nature, then how could that be graduated to any degree? 
Uncertainty, on the other hand, can be reduced through disciplined efforts to acquire knowledge. In short, we can reduce or even manage uncertainty (to a degree); how can certainty be improved? Are there higher degrees of perfection? The answer is no. We reason from an uncertain starting point. Yet this effort presumes, as Mill and Wittgenstein postulated, that we can develop or acquire an increasing degree of belief or conviction that there is less potential for error in our reasoning or action. This position assumes that some way exists to identify what a lesser degree of uncertainty would look like.
:'''Knowledge professions acquire an increasing belief or conviction that there is less potential for error in their reasoning or action based on justified knowledge, favorable past experience, and reliable parameters/models.'''
Uncertainty is, in some form, amenable to description and explanation through observation and analysis. It is explainable and predictable to such a degree that it interests professionals such as scientists and engineers. Professional knowledge of this type has explanatory or predictive power, albeit limited in scope and application to relevant subjects such as physical objects or phenomena and functionality.
:'''The very act of reducing the scope of uncertainty to a limited set of phenomena implies choice and, ultimately, the decision to act in the face of such uncertainty, but acting or judging in this manner adds more value than doing the same under relative ignorance.'''
'''Uncertainty''', as described above, implies a hybrid nature. Examples of this are economic or decision-theoretic applications. In economics, [https://en.wikipedia.org/wiki/Perfect_information perfect information] allows one within the limiting framework of perfect competition in economic games to make decisions with 'perfect knowledge.' The crucial difference here is the distinction between '[https://en.wikipedia.org/wiki/Perfection perfection],' broadly, a state of completeness and lawlessness, and 'perfect, which can be thought of as an analytical limit that is approachable but can never be attained. Arbitrarily delimiting the realm of knowledge into defined, finite boundaries allows the simulated production of perfect choice, assumed crucially, to be free from error. The hybrid nature of this form of 'simulated' certainty produced within boundaries defined by assumptions and, therefore, clouded by doubt is itself a form of uncertainty. The importance of this is the semantical and logical association that exists between certainty and perfection versus the hybrid concept of a perfect anything, whether knowledge, infraction, competition, etc., or in the case of engineering, elastic, permeable, conductive, etc., being contained within an admittedly imperfect matrix of uncertainty.
:'''Simply put, a finite, heavily bounded piece of uncertain knowledge can be improved through the simulated use of [https://www.merriam-webster.com/dictionary/assumed assumedly] perfect information, but the perfection of knowledge cannot be attained or simulated.'''
Engineering and Economics, for example, simulate finite elements of perfect knowledge or information, heavily bounded with assumptions and a range of applications. By doing so, these professions reduce the bounds and variability of uncertainty and thereby manage it. Using these hybrid models to acquire knowledge takes place in an economy (regulated or institutional) and environment (transparent and structured), which puts pressure on the various professions to recognize and use qualitative and quantitative knowledge better to reduce uncertainties in their activities. The challenges in this reflect the nature of the profession's or discipline's knowledge (e.g., scientific, engineering, or medical), the policies and structure of the relevant professional knowledge bodies, process objectives, constraints, and "the elusive demands of politics." <ref>Walker, Vern R. "The siren songs of science: toward a taxonomy of scientific uncertainty for decisionmakers." Conn. L. Rev. 23 (1990): 567.</ref>
One common theme runs through all of these efforts to address the presence of uncertainty in professional activities. 
:"The available scientific information upon which a decision must be made is almost always a mixture of engineering knowledge and uncertainty. Regardless of the information or methodology, there is the potential for error. This is true regardless of the relevant science, whether archaeology or aeronautics, economics or engineering, pharmacology or toxicology or epidemiology. Achieving the best social decisions requires not only understanding and using what we know but also appreciating and weighing the extent of our uncertainty. In making the best use of ... information in ... decision-making, it is still true that the beginning of wisdom is knowing what it is we do not know." (Vern, Op. cit., Reformatted and engineering substituted for scientific) 
'''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty to develop explanations, calculate, and make predictions. This professional knowledge converges on but never reaches certainty, producing a "legitimacy" from the fruitfulness of its use.''' <ref>Randomness Is Unpredictability, Antony Eagle, The British Journal for the Philosophy of Science, Vol. 56, No. 4 (Dec., 2005), pp. 749-790 </ref> 
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=== Bias and Uncertainty ===
Up to this point, the concept of error, as outlined in uncertainty, could easily be simulated by a truly random variable. There should be no detectable differences around the unseen or specified statistical mean. However, several instances in experience point to a tendency towards one extreme or a pattern of error, a collective that is in itself an error of errors, namely [https://en.wikipedia.org/wiki/Bias bias].

Arguably, this could be part of the choice uncertainty discussion below, but it makes more sense to be looked at on its own. Acting in the face of uncertainty is an exercise of personal knowledge or experience. Whether using professional knowledge or individual experience, the potential is there to make a series of choices that, in some instances, make errors more likely than they would be if considered in the context of a statistical analysis.

This tendency for an individual to make the same error in a predictable or repeatable manner is termed bias. Personal experience, beliefs, knowledge, data, and models all have inherent built-in factors, errors, or defects that create predictable error patterns when applied in decision-making. Walker (1998) argues implicitly that such bias can also be considered systemic error. This type of uncertainty is difficult to identify except on theoretical grounds when the magnitude and direction of the bias are known. Most of these biases, whether systemic or knowledge-based, individual or practice-based, require disciplined efforts to "validate" such methods, models, and judgments at all layers while bounding the uncertainty.
: This is the challenge to professional knowledge that must be overcome, namely, developing and validating models and methods for managing bounded uncertainty that minimize this tendency towards repeatable groups of error or bias. Therefore, from this point forward, the discussion will refer to 'uncertainty and its proxies, error, and bias.'
===Propagating Error and Uncertainty===
Partitioning uncertainty and its proxies, error, and bias into logically distinct subsets involves or is associated with multiple-step execution or choice frameworks. Combining those measurements or choices into single parameters or choices involves, in some cases, assembling the data/information in a unique or limited range of sequences. This implies that associated errors in observing, calculating, or choosing will have unequal impacts or consequences depending on where in the chain the error occurs. Also implied in this is the possibility that subsequent errors will be causally linked to some degree. (In statistical practice, this depends upon whether the errors are independent of each other or correlated, specifically, co-variant.)
:'''Professions develop knowledge of how uncertainty and its proxies, error, and bias propagate through an ordered and normative series of observations or choices. This allows the profession to prioritize error and bias reduction to achieve an optimized reduction of uncertainty.'''
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===Cognition, Metacognition and Uncertainty===
The concept of choice and the resultant error in uncertainty can be further delimited depending on the role of [https://en.wikipedia.org/wiki/Cognition cognition], [https://en.wikipedia.org/wiki/Metacognition meta-cognition], and independent external elements. The rationale for this argument is that such choice activity in the face of uncertainty is a conscious, cognitive act.

An argument could be made that such cognition is a prerequisite for exercising choice and introducing error, as presented and discussed above. The counterfactual to this argument is the example of uninformed choice. Choices introduce as much as errors or more as informed choices could be made. (???) Choice, in this sense, is indifferent to knowledge. Purposeful choice in an environment of objectives requires knowledge, method, and experience, but uninformed, speculative choice does not.
:"''' Cognition''' is the set of all mental abilities and processes related to knowledge, attention, memory and working memory, judgment and evaluation, reasoning and "computation," problem-solving and decision making, comprehension and production of language, etc. Human cognition is conscious and unconscious, concrete or abstract, as well as intuitive (like knowledge of a language) and conceptual (like a model of a language). Cognitive processes use existing knowledge and generate new knowledge." (Wikipedia)
:"'''Metacognition''' is "cognition about cognition", or "knowing about knowing" and can take many forms. It includes knowledge about when and how to use particular strategies for learning or problem-solving. There are generally two aspects of [https://en.wikipedia.org/wiki/Metacognition metacognition]: knowledge about cognition and regulation of cognition."
Some types of metacognition knowledge are:
:'''Person knowledge''' (declarative knowledge), which is understanding one's own capabilities.
:'''Task knowledge''' (procedural knowledge), which is how one perceives the difficulty of a task, which is the content, length, and type of assignment.
:'''Strategic knowledge''' (conditional knowledge) is one's capability to use strategies to learn information. (Accessed at Wikipedia)
Like metacognitive knowledge, metacognitive regulation or "cognitive control" contains three essential skills.
:'''Planning:''' refers to the appropriate selection of strategies and the correct allocation of resources that affect task performance.
:'''Monitoring:''' refers to one's awareness of comprehension and task performance.
:'''Evaluating:''' refers to appraising the final product of a task and the efficiency at which the task was performed. This can include re-evaluating strategies that were used. (Accessed at Wikipedia)
Nothing has been said up to this point that limits this structure to an individual, or a collective, or two entities making cognitive choices that are incompatible or inconsistent to varying degrees. Interpretations of this scheme include an individual professional exercising judgment in a decision that, in turn, relies upon the collective judgment and decision of the discipline as an underlying basis versus the decision of another individual to oppose/protest or otherwise contest that decision. Thus, the framework of cognitive/meta-cognitive applies to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, economics of regulatory practice, and game theory.
:By solving useful problems in the face of inherent uncertainty, discipline-specific frameworks of cognitive/meta-cognitive activity apply to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, the economics of regulatory practice, and game theory.
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===Probability and Uncertainty===
Probability is a [https://en.wikipedia.org/wiki/Coherence_theory_of_truth coherent] approach to uncertainty in the physical and mathematics or "classical" domains such as engineering. <ref>See Colyvan for a critique of this claim, Colyvan Mark. "Is probability the only coherent approach to uncertainty?." Risk Analysis 28.3 (2008): 645-652.</ref> and is the "...is certainly the best-known and most widely used formalism for quantifying uncertainty.<ref>Morgan, Millett Granger, Max Henrion, and Mitchell Small. Uncertainty: a guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge University Press, 1992.</ref> [https://en.wikipedia.org/wiki/Cox%27s_theorem Cox’s theorem] ("Any measure of belief is [https://en.wikipedia.org/wiki/Isomorphism isomorphic] (but not necessarily equal) to a probability measure") is a well-known argument for the validity of that argument.
<ref group="note">Cox wanted his system to satisfy the following conditions: 
Divisibility and comparability – The plausibility of a statement is a real number and is dependent on the information we have related to the statement.
:Common sense – Plausibilities should vary sensibly with the assessment of plausibilities in the model.
:Consistency – If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). — preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf</ref>

Another writer, [https://en.wikipedia.org/wiki/Frank_Knight Knight] (1921,1956), presented the following taxonomy of probabilities:
<ref>As presented and discussed in Runde, Jochen. "Clarifying Frank Knight's discussion of the meaning of risk and uncertainty." Cambridge Journal of Economics 22.5 (1998): 539-546.</ref>
: '''Classical 'a priori' probability:''' As an idealized model, numerical probabilities are computed based on generalized principles of assigning equal likelihoods and mutually exhaustive possible outcomes (Runde, 1998) to all set members. Such probabilities are assigned to "...outcomes based on a judgment of indifference between those outcomes, that is, based on the absence of any evidence of real influences in play that may render any one outcome more or less probable than any other." (Runde, op. cit.) This builds upon Knights's concept of an 'absolutely homogeneous classification of instances that are completely identical except for [https://en.wikipedia.org/wiki/Indeterminate_(variable) indeterminate] factors. This judgment of probability or logical probability is on the same logical plane as the propositions of mathematics and ultimately [https://en.wikipedia.org/wiki/Inductivism inductions] from experience.'(Knight, 1956, pg.225)
: '''Bayesian 'a priori' probability:''' In contrast to interpreting probability as the "frequency" or "propensity" of some phenomenon, [https://en.wikipedia.org/wiki/Bayesian_probability Bayesian probability] is a quantity that we assign to represent a state of knowledge or a state of belief. In this view, probability is assigned to a hypothesis, often using the basis of past or prior experience. In contrast, under the frequentist view, a hypothesis is typically tested without being assigned a probability based on prior experience. The Bayesian interpretation of probability can be seen as an extension of propositional logic that enables reasoning with hypotheses, i.e., propositions whose truth or falsity is uncertain. Bayesian probability belongs to the category of evidential probabilities; to evaluate the probability of a hypothesis, the Bayesian probabilist specifies some prior probability, which is then updated in the light of new, relevant data (evidence). The Bayesian interpretation provides a standard set of procedures and formulae to perform this calculation. <ref>See also Larvor, B. "After Popper, Kuhn, and Feyerabend: Recent Issues in Theories of Scientific Method." Metascience (2002).</ref>
: '''Statistical or 'a posterior probability:''' Empirical evaluation of the frequency of association between predicates, not analyzable into varying combinations of equally probable or logical, 'a priori' alternatives. Knight argued that any "high degree of confidence" that the judgment that experience will remain valid for future predictions is "...still based on an 'a priori" judgment of indeterminateness." (Knight, Op. Cit.) Knight's argument to support this is what will be discussed further below that first, "...the impossibility of eliminating all factors not really indeterminate; and, second, the impossibility of enumerating the equally probable alternatives involved and determining their mode of combination so as to evaluate the probability by a priori calculation." (Knight, Op. Cit.) Knight noted that the main difference between this form and that of logical or inductive probability was the presence of empirical information, which allowed the analyst to identify propensity and direction in the observed phenomena. In this case, 'a priori' probability values may be derived from first principles and "statistical probabilities are determined a posteriori by the empirical method of counting instances." (Runde [1998] quoting Knight, op. cit.)
:'''Estimates:''' The distinction here is that there is no valid basis ('a priori' or 'a posteriori') for any classifying phenomena. For Knight, this form of probability presented the greatest logical difficulties of all ... but its distinction from the other types must be emphasized, and some of its complicated relations indicated..." (pp. 224-5, emphasis in the original)

Knight's concept of probability can be viewed as "..a continuum of probability situations, depending on the degree of homogeneity of the 'instances' in question" <ref>Runde, Jochen. "Clarifying Frank Knight's discussion of the meaning of risk and uncertainty." Cambridge Journal of Economics 22.5 (1998): 539-546.</ref>; i.e. going from a logically distinct but equally likely set of elements to a unique set with one member with statistical frequency in the middle. Knight's main motivation for distinguishing between a priori probability and statistical probability underscores his opinion that "(i) the 'mathematical or a priori type of probability is practically never met with in business, while the second is extremely common'; and (ii) 'the statistical treatment never gives closely accurate quantitative results' (Runde quoting Knight pp. 215-16). Runde argues that Knight couldn't conceive of (empirically tabulated) occurrences that are used in daily commerce classes of instances that we have to make do within the course of everyday economic life could be divided into subclasses of instances that are sufficiently homogeneous to permit the determination of what he calls 'real' probability (p. 217).
:'''In modeling degrees of uncertainty, any measure of belief is isomorphic but not necessarily equal to a probability or statistics measure'''.
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===Explanation, Prediction and Uncertainty===
It should be clear that the general notion of uncertainty synonymous with complete ignorance must be bounded as part of a scheme to produce a useful concept of uncertainty in professional practice such as civil engineering.
:The first principle that must be introduced is that such uncertainty must be bounded by reliable and valuable knowledge gained from past experience combined with analytical and computational capabilities to address an immediate and real problem of interest. This could be viewed as the economic interest argument. Only uncertainties associated with physical phenomena and economic scarcity will be addressed.
:The second principle is that the knowledge model is capable of producing statements or assertions in the form of hypotheses that themselves are testable. [https://en.wikipedia.org/wiki/Testability Testability], in this sense, is defined as the property applying to an empirical hypothesis and involves two components:
::The logical property that is variously described as contingency, defeasibility, or falsifiability, which means that counterexamples to the hypothesis are logically possible. Contrast this to a [https://en.wikipedia.org/wiki/Tautology_(logic) logical tautology], which is always true that is true in every possible interpretation. The practical feasibility of observing a reproducible series of such counterexamples if they do exist. In short, a hypothesis is testable if there is some real, non-zero expectation of deciding whether it is true or false of verifiable, reproducible experience. Upon this property of its constituent hypotheses rests the ability to decide whether a theory can be supported or falsified by actual experience data. (Source Wikipedia.)
'''Knowledge models must be capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria described above in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error and bias.'''
===Variability and Aleatory Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge referred to as epistemic uncertainty. Acting in the face of epistemic uncertainty introduces choice uncertainty (discussed below) and results in the production of explanations and predictions. It is implied that there will be testable hypotheses in the form of outcomes of interest to the knowledge professions. Inherent in nature is a variation of results, which may appear to uncertain but isn't. Observations of phenomena will not be the same, and part of the experimental process is to narrow those results down to within an acceptable level of tolerance. Statistical measures seek to identify underlying and unobserved measures of central tendency. These parameters are not directly observed but are derived to a degree of confidence.
In some cases, variation around a mean or within a statistical range may be perfectly acceptable to the knowledge profession. Examples of this are in the physical sciences and medicine. In Engineering and, to a lesser extent, economics, the process or phenomenon may be required to be more controlled to a narrower range than what a natural or "unmanaged" amount of variation would allow. Several courses of action present themselves.
:The first would be to ignore the aleatory phenomenon and choice, which is rejected outright.
:The second would be to assume that, in effect, such variation is itself a form of minimally managed uncertainty and treat it the same as epistemic uncertainty. This is not a desirable outcome as reducing Aleatory error and bias requires
:A third approach would be to acknowledge that process variation is, in some forms, manageable. This means making choices and acting in a way that presumes that the natural occurrence of error can be reduced, or more importantly, bias can be reduced. There is ample precedent for the third approach in civil engineering design and project management. In this sense, the argument is made that the aleatory response to managed efforts results in an elastic variation range. Presumably, an effective management effort either "shifts" the central tendency of the process results or reduces the variation range of occurrence or some other population parameter. Choosing to act and accept that narrower range rather than the broader natural variation may result in an outcome that, while well within the bounds of the expected variation, is outside the arbitrary control limits established by the choice and action. While this phenomenon mimics the broader uncertainty and its proxy error, it is not an error. The information on the variation was known, and the ability to produce narrower results was an error, albeit a pseudo-error, when compared to the broader classes of uncertainty discussed above, such as total ignorance.
::Looking at uncertainty using this third approach means that the error in choosing a new target parameter for this process may contain epistemic errors (our knowledge of the phenomenon may be erroneous, or the model flawed), aleatory uncertainties as discussed, or, more importantly, choice error or bias in thinking that we can move the process results to meet the requirements.
:'''In civil engineering practice, this may mean applying professional judgment in developing partitioning schemes or allocating uncertainty between the three dimensions (Epistemic, Aleatory, and Behavioral) of uncertainty.'''
Lastly, raising the topic like this brings the questions of effectiveness and efficiency to the fore. Advancing knowledge models that reduce error and bias at first are largely choice models resulting in justifications and favorable experiences. At some point in the process, the knowledge profession advances explanations and predictions that require reliable mathematical or logical concepts that are reduced to models and parameters. The question of efficiency becomes important; the broader uncertainty may have been reduced, but the process is economically inefficient. The profession is urged to become more frugal or, for example, reduce the variability of the outcomes. Knowledge professions often operate under conditions that require them to acquire knowledge and reduce uncertainty effectively and efficiently. Doing so requires understanding both epistemic uncertainty, as discussed above, and statistical process variation or aleatory variation.
:'''Knowledge professions manage and reduce uncertainty in the form of error and bias in an economically effective and efficient manner. This effort requires understanding both epistemic and aleatory uncertainty.'''
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===Choice and Behavioral Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge, referred to as epistemic uncertainty, as well as the inherent variations in results, or what was termed aleatory uncertainty. Acting in the face of that epistemic and aleatory uncertainty introduces yet another uncertainty into the process, namely choice uncertainty. This uncertainty reflects uncertainties associated with first-person and third-person choice. This is particularly relevant for engineering with project stakeholders.
===Summary of Semantics Framework for Uncertainty===
Professional disciplines have gradually re-framed their understanding of uncertainty and, thru experience and analysis, have arrived at the following meta-knowledge concepts of uncertainty:
* '''Uncertainty and its proxy, error, have three components: [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic epistemic uncertainty], [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic aleatory uncertainty], and behavioral uncertainty.'''
* '''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty using non-dogmatic beliefs, which recognize the existence of specific criteria and information that would make the believer change their beliefs using coherent approaches such as probability.'''
* '''Knowledge professions develop knowledge of how uncertainty and its proxy, error, propagate through an ordered and normative series of observations or choices that meet testability criteria in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error. An error can then be described, explained, and reduced in ways that uncertainty cannot. This allows the professions to prioritize error reduction to achieve an optimized reduction of uncertainty and produce a "legitimacy" from the fruitfulness of its use.'''
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===Uncertainty versus Risk/Hazard===
Up to this point, nothing has been said about risk or hazard. Now, risk can be defined. Simply put risk or hazard are finite, logically distinct subsets of the broader uncertainty. Risk and hazard are currently defined interchangeably. First and foremost, risk is a specific subset of general uncertainty. Any arbitrary subset of general uncertainty can be defined as a risk if it meets the following criteria:
* Uncertainty is associated with the potential for material error when acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena.
* Uncertainty and its proxy, error, can be partitioned into logically distinct subsets associated with multiple-step execution or choice frameworks in physical environments linked to a unique or limited range of sequences.
**Subcriteria 1:Resulting errors will have varying impacts or consequences depending on where the error occurs and the degree of causal linkage or influence in the chain.
**Subcriteria 2:Potential errors can be sequenced and prioritized to achieve an optimized reduction of error or bias and, by proxy, uncertainty over time.
* Uncertainty and its proxies, error bias, and environments are isomorphic to coherent approaches such as probability.
* Uncertainty and its proxies, error, and bias are isomorphic to coherent approaches such as knowledge models capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria in 'reasoning under uncertainty.'
This is a definition of general risk. There is no differentiation for engineering or economics, medicine or insurance. There is no difference between a 'good' risk consequence and a 'negative' one. Redefining general risk into simpler terms gives the following:
:'''General Risk is a subset of general uncertainty that is associated with the potential for material errors and biases when sequentially acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena; isomorphic to probability measures and methods and capable of testability.'''
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==Logical Framework for Specific Risk Taxonomies==
As noted above, at the most fundamental level, uncertainty is ".....the subjective perception of ignorance." [13] [Note 6] At this point in the discussion, the focus has moved from discussing uncertainty to its framed subset, risk.
:'''Further, the discussion moves from general risk to a professional or discipline-specific risk [https://en.wikipedia.org/wiki/Taxonomy taxonomy]. Beyond this point, risk will be used in place of uncertainty.'''
Han et al. note that, in general, "(t) taxonomies are valuable not only in their comprehensiveness but in their coherent reduction of uncertainty to conceptually discrete elements."
Uncertainty (and, by implication, risk or hazard) taxonomies offer an approach or tool to more precisely identify and define ontological uncertainty so that it can be quantified, analyzed, and communicated. Viewed this way, uncertainty is not a single, monolithic phenomenon but "...multi-dimensional with theoretically distinct domains and constructs that are potentially measurable and related to different outcomes, mechanisms of action, and management strategies." [14] Taxonomic schemes for classifying the different kinds of scientific and, by extension, engineering uncertainty require identifying the various kinds of potential error associated with descriptive scientific or engineering information and knowledge. An example of this is the following quote from a legal authority:
:"I would especially stress the need for an agency to disclose the uncertainty that surrounds its determinations. '''And by uncertainty, I mean the agency's ignorance as well as its quantitative estimates of error."''' (Emphasis added) [15]
'''Given the necessity of acting in the face of enormous uncertainties [16], discipline taxonomies must offer a clearly coherent and probabilistic approach to uncertainty that is sufficiently general in nature, logically distinct and exhaustive in scope and "...provide decision-makers with a foundation for understanding the nature of discipline information..."''' [17]
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=== Scientific Risk Taxonomies ===
The [https://en.wikipedia.org/wiki/Scientific_method scientific method] is a body of techniques for investigating phenomena in its environment, acquiring new knowledge, or correcting and integrating previous knowledge. Inherent in that process are uncertainties of many forms. Any one taxonomy of [https://en.wikipedia.org/wiki/Uncertainty scientific uncertainty] has largely been focused on statistical models used to assess and quantify sampling, errors, and parameters, although several different taxonomies are conceivable: note-24 [18]
====Environment-specific, Phenomena oriented, model-centric taxonomy of uncertainties====
** [https://en.wikipedia.org/wiki/Uncertainty_quantification#Sources_of_uncertainty '''Parameter uncertainty'''] the source of which is model parameters that are inputs to the computer model (mathematical model) but whose exact values are unknown and cannot be controlled, or whose values cannot be exactly inferred by statistical methods. Subsets of this type of uncertainty include but are not limited to:
***'''Experimental uncertainty''' is also known as observation error, which comes from the variability of experimental measurements. An example is repeating an experiment measurement several times using exactly the same settings for all inputs/variables and recording the variability. It is a subset of the larger uncertainty component, parameter uncertainty.
*** '''Parametric variability''' comes from the variability of the input variables of the phenomena model and is also a subset of the larger uncertainty component, parameter uncertainty.
** '''Structural uncertainty''', or model inadequacy, model [https://en.wikipedia.org/wiki/bias bias] or "[https://en.wikipedia.org/wiki/Systemic_bias systemic bias]", or model discrepancy, which comes from the lack of knowledge of the underlying true state of the phenomenon or its environment. It depends on how accurately a mathematical model describes the true state of the phenomenon or what is termed "model fitness". Due to the inherent nature of uncertainty in any knowledge body (scientific or engineering), models are only an approximation to reality. Therefore, any conclusions drawn from the model can be very misleading when the underlying basis is not plausible or lacks validity. '''note-25 [19]]'''
*** What is missing in this context is any concept of output variability due to the model itself. Part of this is due to the lack of recognition of "process" in scientific investigation. The concept of the scientific method started with a single investigator, such as Galileo or Michael Faraday, working in their laboratories. It has grown into planet and solar system scale experiments such as data gathered on planetary flybys such as Mars, Pluto, and Ceres to the hunt for the Higgs Boson. Ultimately, science has become more like engineering in the scale and complexity of its investigations and theory aggregates, such as the "Unified theory" of particle physics. Subsets of this type of uncertainty include but are not limited to:
*** '''Algorithmic uncertainty''', or numerical uncertainty, comes from numerical errors and numerical approximations per implementation of the computer model. Most models are too complicated to solve exactly. For example, the [https://en.wikipedia.org/wiki/Finite_element_method finite element method] or [https://en.wikipedia.org/wiki/Finite_difference_method finite difference method] [may be used to approximate a solution [https://en.wikipedia.org/wiki/Partial_differential_equation partial differential equation], which introduces numerical errors. Other examples are numerical integration and infinite sum truncation, which are necessary approximations in numerical implementation.
*** '''Interpolation uncertainty''' comes from a lack of available data collected from computer model simulations or experimental measurements. For other input settings that don't have simulation data or experimental measurements, one must interpolate or extrapolate to predict the corresponding responses.
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====Commentary====
A more comprehensive taxonomy of uncertainties that acknowledges knowledge, data, and linguistic uncertainties is:
*'''Epistemic uncertainty''' -[https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Epistemic uncertainty] or systematic uncertainty, in contrast, reflects limitations in the current “state of knowledge” underlying models themselves, originates from competing theories or models, is not readily quantifiable, and is manifest by subjective confusion or indecision. It includes uncertainty due to measurement limitations, insufficient data, extrapolations and interpolations, and variability over time and space. Epistemic uncertainty is uncertainty about "...some determinate fact ... because of a lack of complete information. '''cite_note-26 [20]]''' Epistemic uncertainty can be classified into six main types:'''cite_note-27 [21]]'''
*''' Measurement error''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Observational_error Measurement error] is uncertainty that manifests itself as (apparently) random variation in the measurement of a quantity. Repeated measurements will vary and demonstrate classic statistical behavior.
* '''Systematic error''' - [https://en.wikipedia.org/wiki/Observational_error Systematic error] occurs due to bias in the measuring equipment, model, analysis, observation, or sampling procedure. It is formally defined as the difference between the true value of the quantity of interest and the value to which the mean of the results converges as sample or data sizes increase. Unlike measurement error, it is not (apparently) random, and therefore, results subject to systematic error alone do not vary about a true value. Systematic error can result from deliberate judgment to exclude (or include) data, parameters, or models that ought not to be excluded (or included).
** "The only way to deal with systematic error is to recognize a bias in the process, model, or procedure and remove it. Systematic error, however, is notoriously difficult to recognize except on theoretical grounds. Corrections may only be applied when the magnitude and direction of the bias are known. Such corrections underlie the application of double-sampling methods in environmental science." (Regan, et. al., op. cit.)
*'''Natural variation''' - [https://en.wikipedia.org/wiki/Natural_process_variation Natural variation] or underlying process variation occurs in systems that "...change (concerning time, space, or other variables) in ways that are difficult to predict...across the full range of temporal and spatial values (or other related variables)." '''note-28 [22]'''
** This form of uncertainty is reduceable utilizing tools such as [https://en.wikipedia.org/wiki/Statistical_process_control statistical process control] and replication of experiments or observations.
*'''Inherent randomness''' - [https://en.wikipedia.org/wiki/Randomness randomness] or [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty aleatory uncertainty] exists because the system is"...in principle, irreducible to a deterministic one (the most well-known case is described by [https://en.wikipedia.org/wiki/Uncertainty_principle Heisenberg’s uncertainty principle] in quantum mechanics)." '''note-29 [23], note-30 [Note 7]'''
** Even if the argument is accepted that it is unlikely that any given physical system at some level is inherently random, it is important for any taxonomy to distinguish between systems that appear random due to incomplete or inconsistent information and those that are intrinsically random. (Again, see Regan (2002)).
*'''Model uncertainty''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling Model uncertainty] occurs in the process of generating a model as a conceptual representation of a particular phenomenon to create a [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling#Overview simplified reflection of reality]. This selectivity of factors and variables creates uncertainty in at least three ways. (Regan (2002))
** '''Variables and processes''' that are regarded as relevant often represent a trade-off between system knowledge and assumed states and model objectives;
** '''Models''' depict observed processes using logical or mathematical constructs based on underlying theories about system states or dynamics using continuous equations to describe discrete processes.
** '''Curve fitting''' (including interpolation and extrapolation) with mathematical expressions using model variables where inputs are discrete data points.
** Regan (2002) argues that '''model uncertainty''' is "...notoriously difficult to quantify and impossible to eliminate ...(and)...(t)he only reliable way of determining how appropriate a model is for prediction is to perform validation studies."
** Subjective judgment occurs due to the interpretation of scarce or error-prone data.
*** The only way to address this type of uncertainty is to "...assign a degree of belief about an event in the form of a subjective probability." (Regan (2002))
*Linguistic uncertainty - Linguistic uncertainty, or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. '''note-31 [24]], note-32 [25]]'''
** '''Context dependence''' is uncertainty concerning the context in which a statement is to be understood;
** '''Ambiguity''' occurs when words have multiple meanings, and further reduction to a single meaning is not logically possible in a given context.
** '''Underspecificity''' occurs in the presence of "unwanted generality" or multiple interpretations, and reduction to a smaller set or even a single interpretation is not logically possible.
*Stochastic or statistical uncertainty - Stochastic or statistical uncertainty, which is sometimes known as [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Aleatoric uncertainty] as well as the subject of [https://en.wikipedia.org/wiki/Stochastic_control stochastic control] pertains to the parameters of a risk model, originates from sampling or measurement error, and can be quantified and mathematically expressed (e.g., using confidence intervals).
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== Uncertainty Taxonomies in a Legal Environment ==
From a legal perspective, a definition of taxonomy is dictionary-based, such as "the systematic distinguishing, ordering, and naming of type groups within a subject field." given by Walker (op. cit.). Such legal concepts of scientific or engineering taxonomies are not required to be epistemically complete. Still, they must cover "the most significant aspects of ...(current, good discipline practices) ... and of the descriptive information commonly encountered by decision-makers" in a logically distinct manner. (Walker, 1991, p. 571) These taxonomies must be sufficient to catalog the types of uncertainty associated with descriptive assertion, including the critical subset of cause and effect assertions. This legal/decision-maker framework has presented descriptive uncertainty as having six components (Walker, 1991). Although not part of the Walker taxonomy, underlying the scheme is linguistic uncertainty, or "vagueness," as a source of uncertainty, including uncertainties due to context dependence, ambiguity, and under-specificity. '''note-33 [26]]''' Arguably, these uncertainties underlie the most basic element in the taxonomy.
* '''Linguistic '''- or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. (Regan, 2002)
* '''Conceptual'''- the definition by choice and design of descriptive concepts or variables to be used as [https://en.wikipedia.org/wiki/Predicate_(grammar) predicates] where the subject of an assertion identifies what is being discussed and the predicate provides information about the topic or characterizations such as what the subject is, what the subject is doing, or what the subject is like (Wikipedia).
* "Conceptual uncertainty, or the potential for conceptual error, arises whenever predication occurs. Whenever a concept is used to describe something, using certain concepts instead of others begins to structure how we understand the object, event, or instance under discussion. Predication or conceptualization generates useful information about things, but it also can inhibit our ability to think about those things with concepts other than those selected. The concepts used may not be the most fruitful or the best designed- either for scientific purposes or for making wise, fair, effective, and efficient decisions." (Edited for reading, Walker, 1991)
* Similarly, choice and design of descriptive variables enhance and restrict data set membership, creating potentially incomplete, incompatible, or inconsistent data sets, "...the classification categories employed, and the relationships among those categories (nominal, ordinal or scalar)." '''note-34 [27]]'''
* Concept uncertainty '''note-35 [28]''' like systematic error discussed above is notoriously difficult to recognize, except on theoretical grounds, and the magnitude and direction of the bias are known.
Both terms could be analogized to that of [https://en.wikipedia.org/wiki/Accuracy_and_precision#Terminology_of_ISO_5725 accuracy] in [https://www.bipm.org/en/publications/guides/ ISO 5725] which looks at the ability of the concept or system's ability to produce results that are proximate to the "true" value or state of the phenomenon. Conceptual uncertainty and systemic bias are related to another measure of uncertainty: validity. Walker has defined validity as the ability of a concept to quantitatively describe and explain what the phenomenon objective "truly" is. Walker also ties the concepts together when he states, "Validity concerns the "accuracy" of the measurement data, not its precision." (Walker, 1998) 
Additionally, conceptual uncertainty and associated validity issues could be propagated throughout this taxonomy. Every component of action and decision in the face of uncertainty raises validity issues. Walker recognized this when the author defined epistemic choice for concepts used throughout the other uncertainty components or layers.
* '''Measurement''' uncertainty or Misclassification error- the application of the underlying concepts or variables to specific, individual cases and the uncertainty of the reliability (Walker, 1998) of the resultant data. (It is important to remember that assessment observation and measurement are used interchangeably in this analysis.) Thus, an assessment method or procedure is considered "reliable" or "precise" in the scientific terms of ISO 5715 if it repeatedly produces consistent results.
* Measurement uncertainty is logically distinct from conceptual uncertainty, which can only occur after any conceptual uncertainty has been established. Still, misclassification can occur even when the conceptual uncertainty has been minimized. Conversely, underlying errors or errors made in measurement or classification propagate thru the reasoning chain and reduce the quality or value of the final result.
* While uncertainties associated with quantitative measurements or assessments can be statistically evaluated and described, uncertainties related to qualitative assessments are more problematic.
**Working through the taxonomic chain from base concept to causal explanation requires amassing and integrating qualitative information and quantitative data into input parameters for mathematical models. Developing procedures for producing consistent, repeatable sets of information from assessments that minimize conceptual error or systemic bias requires professional efforts to "validate" such methods, models, and judgments at all layers in the process. Validation as a process is something that civil engineering as a practice needs to embrace more frequently.
* '''[https://en.wikipedia.org/wiki/Sampling Sampling]'''- in the classical statistics sense, is the selection of a limited subset of a larger population to use as a basis for making estimates about the population in the form of attributes, proprieties, or parameters. The resulting information would be determinative in making forecasts about future population sampling or possessing a desired amount of predictive power. The uncertainty about the ability of this information to correctly predict future samples is termed sampling error.
* '''Modeling'''- Modeling uncertainty arises whenever a claim is made that out of a class of candidate relationships and variable pairings, one variable pairing inclusive of constants is chosen that possesses a particular or persistent mathematical relationship to another variable X. Modeling errors arise in selecting the wrong variable pair and constants or by incorrectly specifying its constants. (Walker, 1991, pg. 599)
** '''Causal'''- In causal modeling and analysis, the relationship between causal variables is not strictly a mathematical function. Still, it makes testable predictions and explains "...how a system of variables works, why a system works the way it does, or why it makes sense to think of certain variables as a system" at all." (Walker, 1991, pg. 609)
** '''Epistemic'''- the choice of interpretations for fundamental, logical concepts used throughout the other components or layers.
The components of this uncertainty taxonomy can combine in different ways to "...produce different aggregate uncertainties." '''note-36 [29]'''
:''' Only the scientific and engineering professions possess the capacity to develop and validate procedures and models that combine the different kinds of qualitative '''note-37 [30]''' and quantitative information and their associated potential for error into an aggregate measure or "scalar variables" of uncertainty.'''note-38 [31]]
Scalar variables are quantitative variables whose categories are related by some measure of the relevant property's incremental frequency, degree, or amount. (Walker, 1991) Examples are Modulus of Elasticity, Compression stress in elastic materials, etc.

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=== Economics Uncertainty Taxonomies ===
Like scientific concepts of uncertainty, economics is environment-specific (in this case, read sector of economy or markets), phenomenon-oriented (in this case, read human activity and manufactured phenomena such as firms and markets guided by human judgment and decision), and model-centric.
:''' Economics is not interested in all forms of uncertainty, only specific, meaningful subsets possessing casual connections with material impacts, often labeled as 'risks'.'''
One of its earlier writers ([https://en.wikipedia.org/wiki/Frank_Knight#Life_and_career Knight], 1921, 1948, 1957) attempted to define "uncertainty" as the presence of "defects of managerial knowledge" (or [https://en.wikipedia.org/wiki/Knightian_uncertainty knightian uncertainty] and thus risk centric) as risk that is immeasurable, not possible to calculate.(Wikipedia) Risk in this sense was defined as "the ordinary risks of business activity which can ... be reduced... by applying the insurance principle." Knight does acknowledge that risk is a specific and unambiguous subset of uncertainty. (Risk, Uncertainty and Profit, 1957, pg. 19) For Knight, measuring uncertainty meant measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences, which Knight referred to as "the imperfection of knowledge". (Risk, Uncertainty and Profit, 1957, pg. 197) One of Knight's many contributions to this analysis was recognizing information and knowledge's role in economic activity.
:"''(W)e are concerned only to emphasize the fact that '''knowledge is in a sense variable in degree and that the practical problem may relate to the degree of knowledge rather than to its presence or absence in toto.''' We live only by knowing something about the future, while the problems of life, or conduct at least, arise from knowing so little. This is as true of business as of other spheres of activity. The situation's essence is action according to opinion, of greater or less foundation and value, neither entire ignorance nor complete and perfect information, but partial knowledge. If we are to understand the workings of the economic system, we must examine the meaning and significance of uncertainty; and to this end, some inquiry into the nature and function of knowledge itself is necessary.''" (Knight, pg. 199) Emphasis added
Knight also reinforced the difficulties in classifying uncertainties when he analyzed life insurance. In this case, accidental death was an uncertain phenomenon compared to sickness and accident, where an "...objective description and classification of cases was impossible..." (Knight, pg. 248) Another factor that made uncertainties ineligible as risks and, therefore, uninsurable because they were unclassifiable was the exercise of judgment in making decisions by the businessman. (Knight, pg. 251)
This is a simple classification of economic uncertainty and, as an idealization, is philosophically controversial. '''note-39 [32]'''
* Uncertainty and information about the economic environment are distinct from uncertainty about others’ behavior or choices.
* Risk as a specific subset of uncertainty implies that "...all possible acts are known, all possible outcomes arising from each act are known, and it is possible to assign probabilities to each act." '''note-40 [33]'''
Like the economic [https://en.wikipedia.org/wiki/Theory_of_the_firm theory of the firm], an economic theory of uncertainty seeks to explain and predict the nature of a specific subset of uncertainty (in this case, risk relating to physical phenomena and human activity), including its behavior, structure, and relationship to the universal uncertainty of physical phenomena. In this sense, uncertainty, like firms and markets, can establish price equilibriums under the right circumstances different from those that might have occurred in either of the other two. Such a theory of uncertainty offers a partitioning scheme that decomposes uncertainty into distinct and meaningful subsets, of which risk is a significant one. It also explains how the presence and variability of knowledge cause or " drive" economic consequences. Unlike physical phenomena, economic activity creates artificial objects such as products, firms, mediums of exchange, markets, economies, and knowledge about those activities. The economics of uncertainty exists as an alternative to "certainty" models that assume away imperfect knowledge and unknown choice/judgment preferences when it is more efficient for economic decision-making. This also allows the introduction of the logical concept of "hazard" versus "risk". Frank used hazard extensively in Risk, Uncertainty, and Profit, but not to the extent that one could say it was interchangeable. An example of this is in the term "[https://en.wikipedia.org/wiki/Moral_hazard Moral hazard]," where a firm could engage in "riskier" and potentially "uninsurable" behavior if the information were transparent, and, therefore, insurance coverage of losses is limited. note-41 [34]], note-42 [35]]

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=== Uncertainty and Risk in Policy and Regulatory Environment ===
:''"Hume's 'just reasoner', when faced with a difficult public policy decision in current times, would probably commission a risk analysis. But how could they incorporate 'a degree of doubt, caution, and modesty?"'' '''note-43 [36]'''
The problem of "decision-making in the face of uncertainty" is how to regulate based on incomplete information that has the potential to be materially inaccurate. A contextual assumption is that we need to evaluate decision rules for dealing with uncertainty. As a social enterprise, risk regulation, whatever its substantive objectives, should be as '''effective, efficient, and equitable''' as practically possible. These "three E's" form a set of "process objectives" or "meta-goals." From the uncertainty standpoint, causal information can be usefully divided into two major categories: information about groups and information about individuals.

Each category, group, and individual has its distinctive types of inherent uncertainty. These are logically distinct, generally independent, and cumulative, contributing to '''(?????)'''

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=== Engineering Uncertainty Taxonomies ===
Like scientific and economic concepts of uncertainty, engineering is environment-specific, phenomena-oriented, model-centric, external choice, and driven by decision-making in the face of uncertainty. Like economics and unlike science, engineering is not interested in all forms of uncertainty, only those with determinable and material consequences. Like economics, engineering conceives risk as a specific and unambiguous subset of uncertainty determined by measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences. Also, engineering recognizes the role information and, by extension, knowledge plays in constructing the built environment.
'''note-44 [37]]''' Like economics, engineering differs from economics and scientific uncertainty in its interest in behavioral uncertainty.
*"Behavioral or interaction uncertainty is how individuals or organizations act or interact. Behavioral uncertainty arises from four sources: design uncertainty, requirement uncertainty, volitional uncertainty, and human errors." '''note-45 [38]]'''
** A design uncertainty is a choice among alternatives over which an individual or group of individuals exercises direct control but has not yet decided upon.
** Requirement uncertainty includes parameters of interest to and determined by the stakeholder, independent of the engineer or designer.
** Volitional uncertainty is uncertainty about what the subject him/herself will decide. Other people’s future actions and conduct are not entirely predictable, particularly when dealing with other organizations.
** Human errors occur during the development of a system or project due to blunders or mistakes by an individual or individuals.

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==Practice Frameworks for Uncertainty and Risk in Civil Engineering Practice==
===PMIBoK===
PMI does not define uncertainty, but the PMI Body of Knowledge uses the term in 45 places (uncertainty) and as describing properties in 8 places (uncertainties). [See Note 8] PMI does define "risk" and uses the term over 1,300 times in the same document. In its glossary, PMI also defines terms such as "threat" and "opportunity" but not events.[391 The BoK contextually defines uncertainty when it states that project risk has its origins in the uncertainty present in all projects. (Op. Cit., pg 309) 
'''Uncertainty''' is contextually defined and taken as having the property of affecting project execution and the ability to meet stakeholder expectations. Uncertainty is more than the sum of all individual risks (known and unknown). The nature of this uncertainty is not defined, only its capacity to affect something else or its properties. 
'''Risk''' is defined as " ... an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives." (Ibid.) Beyond this formal definition, PMI adds context when, in its risk management section (Sec. 11 ), the BoK states that risk is a caused event or condition with multiple causes and impacts. The BoK even offers specific techniques for mapping cause-and-effect relationships and influence diagrams. (Sec. 11.2.2.5) Not as evident but equally important is the recognition that not only is risk caused but the impact is "triggered". The risk trigger is an event or situation that signals that it is about to occur (Glossary, pg. 566). Risk conditions are factors that affect or otherwise contribute to risk, such as project stakeholders. Risk implicitly retains some residual element of probability in it, or a value of less than 1.0 with risk that approaches a level of near certainty is termed "issues" or "realized risk" .(Op. Cit., pg 309). PMI also links risk to underlying variations in project outcomes. (Ibid.) Risk is based upon data and information which can be assessed as to its quality (Sec. 11.3.2.3). This analysis looks to determine the value of the information and examines the degree to which the risk information is understood as well as its " ... accuracy, quality, reliability and integrity ... " (Ibid.) Part of that is understanding the relationship between occurrence and impact as outlined in PMl's Figure 11-10 Risk Impact Matrix and developing a model for prioritizing risk products and setting the threshold for action.
====Commentary:====
For defining and identifying risk, PMI notes in Sec. 11.2.2.4 that " ... Every project and its plan is conceived and developed based on a set of hypotheses, scenarios, or assumptions." Part of the risk analysis process is identifying risks to the project, such as inaccuracy, instability, inconsistency, or incompleteness of assumptions. Similarly, the quality of management plans, as well as their consistency with others and the project objectives and assumptions are 11 •• .indicators of risk in the project." (Sec. 11.2.2.1) 
Similarly, the role of engineering economics in risk is embedded or implied in the PMI definition of risk when the BoK discusses risk identification as a process of assessing which risks out of the total risk exposure for the project "may affect" the project and distilling their characteristics. Embedded in this statement is the concept that not all risks can cause economic impacts (positive and negative) to the project. Also embedded is the requirement to understand risk characteristics to identify project risk (Cost and schedule). Lastly, risk identification is an iterative process of incrementing project risk documentation, much like project scope, cost, and schedule documentation. 
Lastly, PMI acknowledges the complex constitution of risk aggregates or the "sum" of all risks in its material, but is it a sum? Risk information has descriptive and explanatory content. These are knowledge and meta-knowledge components. Understanding project assumptions is project-specific knowledge, but identifying risks from assumption inaccuracy, instability, inconsistency, or incompleteness requires professional or program meta-knowledge. 
'''Uncertainty exists in all projects, but risk, a subset of uncertainty with material economic impacts, is a key meta-knowledge element for civil engineering practice.'''
===Software Development Process- Risk Focused===
The Unified Process requires the project team to focus on addressing the most critical risks early in the project life
cycle. The deliverables of each iteration, especially in the Elaboration phase, must be selected to ensure that the greatest risks are addressed first. <ref> Booch, Grady, Ivar Jacobson, and James Rumbaugh. "The unified software development process."
Reading: Addison Wesley (1999).</ref>
===American Society of Civil Engineers (ASCE)===
ASCE does not define uncertainty, but the ASCE CE Body of Knowledge (CEBoK) uses the term in 22 places (uncertainty) and describes properties in 13 places (uncertainties). 
The CEBoK does not use the phrase "uncertainty and risk" as PMI and AACE do in their publication. ASCE reverses the two (Risk and uncertainty) in twelve places regarding technical outcomes (Outcome 12). A fundamental difference between PMI and ISO is that risk is considered a subset of uncertainty. ASCE does not define "risk" but uses the term 25 times in the same document. Almost all are in the context of variation of design parameters and not in the classical context of cost, schedule uncertainty, and risk.
====Commentary:====
...
=== American Association of Cost Engineers (AACE) ===
AACE defined uncertainty, risk, and other related terms in its Risk Management Dictionary. <ref>Risk Management Committee uRisk Management Dictionary, Cost Engineering Vol. 37/No. 10, OCTOBER 1995. </ref>
AACE's overall strategy was to define risk and other terms that stem from base uncertainty. AACE first defined uncertainty as .... "(t)he total range of events that may happen and produce risks (including both threats and opportunities) affecting a project (see opportunities, events, conditions, risk, and threats" where the following sub-definitions apply:
** Biases--A lack of objectivity based on the individual's position or perspective. Systematic and predictable relationships between a person's opinion or statement and his/her underlying knowledge or circumstances. Note: there may be "system biases" as well as "individual biases."
** Condition (Uncertain Condition)-Any specific identifiable circumstance (such as the rate of inflation or the quality of labor available) that might affect the outcome of the project
** Event (Uncertain Condition)-is a specific identifiable action (such as a large government project being started in the same labor area as your project) or an act of nature that might happen and that (if it does happen) could affect the outcome of the project.
** Opportunities are Uncertain events that could improve the results or improve the probability that the desired outcome will happen
** Threats are Uncertain events that are potentially negative or reduce the probability that the desired outcome will happen.
*** AACE defines Risk as an "ambiguous term" (sic) that is synonymous with uncertainty or a negative subset such as "threats", or an overall negative impact of all possible uncertainties.
====Commentary:====
...
==Limitations of the definition==
...
==Working definition==
...
==Beneficial outcomes==
...
==See also==
Wiki articles on the project.

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==Notes==
<references group="note" />
<li> Implicit in this definition of uncertainty as a "state of' is ... </li>
: " ... a conceptualization of uncertainty as a subjective, cognitive experience of people--a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one's lack of knowledge, without which one could not feel uncertain; '''uncertainty is a form of "meta-cognition" ... ( or alternatively, its main component, meta-knowledge) ... -a knowing about knowing.'''" (Han, 2011, op. cit., Emphasis added)
</ol>
<ol start="5">
<li> Cox wanted his system to satisfy the following conditions:</li>
: Divisibility and comparability- The plausibility of a statement is a real number and depends on the information we have related to the statement.
: Common sense - Plausibilities should vary sensibly with the assessment of plausibilities in the model.
: Consistency - If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). -preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf
</ol>
<ol start="6">
<li> The utility of existing taxonomies of uncertainty in civil engineering and, by extension, "risk" has been unsatisfactory for this very reason. This is particularly true in cases where the source of the uncertainty is conflicting versus incomplete information. See Regan, et. al.,"A taxonomy and treatment of uncertainty for ecology and conservation biology" (2002)
for a survey of scientific taxonomies for uncertainty.</li>
<li> Regan et al. argue that " ... genuine examples of this kind of uncertainty are hard to find. Even classic cases of random experiments like coin tosses and the throwing of dice are deterministic; it is just that we do not have enough information about the dynamic processes and initial conditions to make any sensible estimates about the outcomes. Such processes are, for all intents and purposes, inherently random, but they are not genuinely inherently random. For similar reasons, complex systems such as ecosystems and weather patterns are unlikely to be inherently random. Similarly, chaotic systems are entirely deterministic. They are unpredictable because the deterministic processes generating them and the relevant initial conditions are hard
to fully specify (see Stewart 1989, Sugihara et al. 1990)." (Regan (2002) </li>
<li> The CEBoK uses the phrase "uncertainty and risk" twice in reference to scheduling (Section 6.5,2) and cost estimating (Section 7.2,2) as well as reversing the two (Risk and Uncertainty) also in two places in discussing project life cycle (both in Section 2.4.1) </li>
</ol>

==References==
<references />

Uncertainty and Risk in Civil Engineering Practice

2024-11-24T16:46:38Z

WikiSysop: /* Probability and Uncertainty */

'''Life is short and the art long; the occasion instant, experiment perilous, decision difficult.''' (Hippocrates as quoted in Fox,)
:''See also [[Project Definition]] , [[Project Life Cycle and Phase Models]] , [[Project Delivery Methods]]''
:''See also [[Technical Outcomes for Civil Engineering:Risk and Uncertainty]]''
==Basic Considerations==
As human beings,'' ... being alive means seeking opportunities and taking risks.'' As knowledge professionals
living in the 21st century, this means coping with an increasingly complex number of uncertainties for humans
living in this environment. We seek to understand better how these uncertainties can be characterized and
managed. The essence of this article and the experience for engineers in general and civil engineers in particular is
that manageable uncertainty is, by definition, termed risk, and its kindred cousin is termed hazard. This causes us to experience the
:" ''... human dread of and fascination for risk and the increasingly important role of risk analysis within societies ...'' " <ref name="McDaniels"> McDaniels, Timothy, and Mitchell Small. Risk analysis and society: an interdisciplinary characterization of the field. Cambridge University Press, 2004, page 1 </ref> (Ibid.)
and, by extension, civil engineering. McDaniels et al. argue that risk management has been
fundamental to our social and governance development for the past 10,000 years. The scale and shape of the
uncertainties faced in this period shaped the societies that have developed today. The central thrust of this effort
over the centuries has been to reshape and re-frame our understanding and conception of uncertainty from one of
complete unknowing and simple acceptance as our fate in life to one of management (cf "''Against the Gods''"
concept in Bernstein's book <ref name = Bernstein">Bernstein, Peter L., and Jesse Boggs. Against the gods. Simon & Schuster 1997.</ref>.
: ''All the knowledge professions and disciplines have struggled with managing uncertainty, for it is impossible to manage unbounded uncertainty.'' <ref group="note">See McDaniels et al. 2004 for an extensive discussion and bibliography on the historical development of Risk Analysis and some key milestones in risk analysis in the 20th century.</ref>
As outlined below, professions such as civil engineering have successfully acted in the face of uncertainty. It lies in the profession's ability to reduce a wide variety of uncertainties into increasingly smaller and crucially bounded subsets that can be managed. These are called 'risks. More recently, this process has evolved, and individual disciplines such as Civil Engineering (CE) have developed their knowledge and models to perform risk analysis. Risk is also taught as a distinct discipline and specialty practice within civil engineering. Still, it has some unique features that set it apart from other more classical practices within CE. As such, it is one of the first of some very specialized CE practices that use knowledge about civil engineering knowledge, or meta-knowledge. Other examples of civil engineering metaknowledge are project controls and quality controls.
==Semantic, Epistemic and Logical frameworks==
The semantic, epistemic, and logical frameworks for uncertainty and risk have several dimensions and layers of logical frameworks. The semantics problems include interchangeable usages for risk and uncertainty and hazard, uncertain and imperfect, and a lack of definitional material or context. The semantical scheme for this article will be to proceed from the distinction between certainty and uncertainty through marginal refinements and reductions of uncertainty up to the point of causal uncertainties. Beyond this point of semantics will be the logic frameworks or the subject of taxonomies of professional knowledge. First, it is about simple, testable phenomena, and then, it moves on to complex taxonomy schemes for artificially constructed phenomena or engineering projects.
<ref group="note">TBD</ref>
==Semantics framework==
===Certainty and Uncertainty===
''''' There is no such thing as absolute certainty, but there is assurance sufficient for the purposes of human life.''''' ([https://en.wikipedia.org/wiki/John_Stuart_Mill John Stuart Mill]) 
'''''If you tried to doubt everything, you would not get as far as doubting anything. The game of doubting itself presupposes certainty.''''' ([https://en.wikipedia.org/wiki/Ludwig_Wittgenstein Wittgenstein] # 115 from On [https://en.wikipedia.org/wiki/On_Certainty Certainty]) 
It is important to note that the references to epistemic or epidemiological knowledge in this article are assumed to relate to three forms of knowledge, namely:
* Knowledge that ( [https://en.wikipedia.org/wiki/Descriptive_knowledge descriptive or declarative or propositional knowledge])
* Knowledge how ( or "[https://en.wikipedia.org/wiki/Procedural_knowledge know-how]"), and
* [https://en.wikipedia.org/wiki/Knowledge_by_acquaintance Knowledge by acquaintance].
'''Certainty''' has been defined as "an epistemic property" of knowledge in all its forms and the state of our beliefs about that knowledge.
<ref>Certainty, Stanford Encyclopedia of Philosophy, accessed on September 12, 2015, at http://plato.stanford.edu/entries/certainty/#ConCer </ref>
Certainty about any belief about knowledge implies that it is not subject to doubt or skepticism. This immunity to criticism can be dogmatically based, emphasizing the importance of a propositional-based sense of truth over experiential, sensory perceptions. <ref>Definition of [https://en.wikipedia.org/wiki/Dogmatic_theology Dogmatic theology or belief], accessed at Wikipedia</ref>
: '''The demarcation line between dogmatic and non-dogmatic beliefs lies in the presence and recognition of specific criteria and information that would make the believer change their beliefs.'''
An empirical framework of [https://en.wikipedia.org/wiki/Testability testability] and [https://en.wikipedia.org/wiki/Falsifiability falsification] is required to recognize this and limit dogmatism. For example, one could argue that people would change their minds if God asked them to. Similarly, one could construct a concept of epistemic as opposed to dogmatic belief certainty as the ability to know anything that one chooses to know and can be known or [https://en.wikipedia.org/wiki/Omniscience inherent omniscience].

A second kind of certainty is epistemic, when conviction reflects the highest possible support for a belief. In this sense, knowledge is separate from beliefs, although someone may have beliefs about a property, such as certainty of knowledge. Logically, it has been shown that there will be unprovable statements within the system for any such knowledge system. Secondly, the knowledge system cannot demonstrate its own consistency. ([https://en.wikipedia.org/wiki/Gödel%27s_incompleteness_theorems Gödel's incompleteness theorems])
: '''Certainty, in real life, is useless or often damaging (the idea is that "total security from error" is impossible in practice, and a complete "lack of doubt" is undesirable)''' ([[https://en.wikipedia.org/wiki/Certainty Physicist Carlo Rovelli]])
'''Uncertainty''', on the other hand, arises immediately in the slightest amount of doubt or criticism.
<ref group="note"> The Merriam-Webster dictionary defines uncertainty as “the quality or state of being uncertain,” which is something of a circular definition. Likewise, one could define uncertainty as the state of not being certain.

Synonyms are distrust, doubt, misgiving, mistrust, reservation, skepticism, and suspicion. Another writer added indefinite, indeterminate, not certain to occur, problematical, unreliable, untrustworthy, unknown beyond doubt, dubious, doubtful, not clearly identified or defined, not constant, variable, and fitful to the list.
:(Han, Paul KJ, William MP Klein, and Neeraj K. Arora. "Varieties of Uncertainty in Health Care: A Conceptual Taxonomy." Medical Decision Making 31.6 (2011): 828-838.)
Han et al. noted that any definition of uncertainty clearly encompasses "...numerous types, sources, and manifestations of uncertainty, and ...(any) ...useful working definition of uncertainty needs to specify the concept underlying these varied meanings of the term."</ref>
Implicit in this definition of uncertainty as a “state of” is ...
"...a conceptualization of uncertainty as a subjective, cognitive experience of people—a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one’s lack of knowledge, without which one could not feel uncertain; uncertainty is a form of “meta-cognition” ...(or its main component, meta-knowledge)... —a knowing about knowing." (Han, 2011, op. cit., Emphasis added)
Similarly, uncertainty could be defined as "...any departure from the unachievable goal of complete [https://en.wikipedia.org/wiki/Determinism determinism]."
<ref name="Walker1">Walker, W. E., Harremoës, P., Rotmans, J., Van Der Sluijs, J. P., Van Asselt, M. B., Janssen, P., & Krayer von Krauss, M. P. (2003). Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support. Integrated assessment, 4(1), 5-17.</ref>
Reasoning under uncertainty is very different than performing the same under certainty. In reasoning under certainty, one has complete knowledge and deduces without doubt and equally important, without limitation, thereby concluding free from error. Reasoning under uncertainty, one works in a state of incomplete, inconsistent, and limited knowledge; doubts cloud any statements or assertions and thereby taint any deductions/inferences resulting in the potential for error. <ref group="note">Implicit in this definition of uncertainty as a “state of” is ...
"...a conceptualization of uncertainty as a subjective, cognitive experience of people—a state of mind rather than a feature of the objective world. The defining feature of this state, furthermore, appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one’s lack of knowledge, without which one could not feel uncertain; uncertainty is a form of “meta-cognition” ...(or alternatively, its main component, meta-knowledge)... —a knowing about knowing." (Han, 2011, op. cit., Emphasis added)</ref>
:'''Knowledge professions reason in a state of incomplete, inconsistent and limited knowledge with doubts that cloud any statements or assertions and taint any deductions/inferences resulting in the potential for error.'''
:''' It is also important to note the semantical and logical correlation between 'reasoning under uncertainty' or 'acting in the face of uncertainty' with the 'potential for' or 'presence of' error. The presence of uncertainty is invariably linked to the potential presence of error.''' The [https://en.wikipedia.org/wiki/Contingency_(philosophy) contingent] nature of uncertainty logically implies the contingent nature of error.
:'''Knowledge Professions use error as a proxy for uncertainty such that within discipline knowledge frameworks, uncertainty can be managed.''' The rationale for this belief is that error can be reliably described, quantified, explained, and ultimately reduced in ways that uncertainty cannot.

'''Uncertainty''' implies a range of variation that is impossible in certainty. Concepts and definitions of uncertainty are unbounded and vast but start from the point of complete and total ignorance. In describing uncertainty, statements can range from complete ignorance to relatively high degrees of belief that there is less potential for error in our reasoning or action. The latter is based upon confidence in justified knowledge, favorable past experience, and reliable parameters/models. One can't make that statement about ranges in certainty. If certainty, by definition, precludes doubt of any type or nature, then how could that be graduated to any degree? 
Uncertainty, on the other hand, can be reduced through disciplined efforts to acquire knowledge. In short, we can reduce or even manage uncertainty (to a degree); how can certainty be improved? Are there higher degrees of perfection? The answer is no. We reason from an uncertain starting point. Yet this effort presumes, as Mill and Wittgenstein postulated, that we can develop or acquire an increasing degree of belief or conviction that there is less potential for error in our reasoning or action. This position assumes that some way exists to identify what a lesser degree of uncertainty would look like.
:'''Knowledge professions acquire an increasing belief or conviction that there is less potential for error in their reasoning or action based on justified knowledge, favorable past experience, and reliable parameters/models.'''
Uncertainty is, in some form, amenable to description and explanation through observation and analysis. It is explainable and predictable to such a degree that it interests professionals such as scientists and engineers. Professional knowledge of this type has explanatory or predictive power, albeit limited in scope and application to relevant subjects such as physical objects or phenomena and functionality.
:'''The very act of reducing the scope of uncertainty to a limited set of phenomena implies choice and, ultimately, the decision to act in the face of such uncertainty, but acting or judging in this manner adds more value than doing the same under relative ignorance.'''
'''Uncertainty''', as described above, implies a hybrid nature. Examples of this are economic or decision-theoretic applications. In economics, [https://en.wikipedia.org/wiki/Perfect_information perfect information] allows one within the limiting framework of perfect competition in economic games to make decisions with 'perfect knowledge.' The crucial difference here is the distinction between '[https://en.wikipedia.org/wiki/Perfection perfection],' broadly, a state of completeness and lawlessness, and 'perfect, which can be thought of as an analytical limit that is approachable but can never be attained. Arbitrarily delimiting the realm of knowledge into defined, finite boundaries allows the simulated production of perfect choice, assumed crucially, to be free from error. The hybrid nature of this form of 'simulated' certainty produced within boundaries defined by assumptions and, therefore, clouded by doubt is itself a form of uncertainty. The importance of this is the semantical and logical association that exists between certainty and perfection versus the hybrid concept of a perfect anything, whether knowledge, infraction, competition, etc., or in the case of engineering, elastic, permeable, conductive, etc., being contained within an admittedly imperfect matrix of uncertainty.
:'''Simply put, a finite, heavily bounded piece of uncertain knowledge can be improved through the simulated use of [https://www.merriam-webster.com/dictionary/assumed assumedly] perfect information, but the perfection of knowledge cannot be attained or simulated.'''
Engineering and Economics, for example, simulate finite elements of perfect knowledge or information, heavily bounded with assumptions and a range of applications. By doing so, these professions reduce the bounds and variability of uncertainty and thereby manage it. Using these hybrid models to acquire knowledge takes place in an economy (regulated or institutional) and environment (transparent and structured), which puts pressure on the various professions to recognize and use qualitative and quantitative knowledge better to reduce uncertainties in their activities. The challenges in this reflect the nature of the profession's or discipline's knowledge (e.g., scientific, engineering, or medical), the policies and structure of the relevant professional knowledge bodies, process objectives, constraints, and "the elusive demands of politics." <ref>Walker, Vern R. "The siren songs of science: toward a taxonomy of scientific uncertainty for decisionmakers." Conn. L. Rev. 23 (1990): 567.</ref>
One common theme runs through all of these efforts to address the presence of uncertainty in professional activities. 
:"The available scientific information upon which a decision must be made is almost always a mixture of engineering knowledge and uncertainty. Regardless of the information or methodology, there is the potential for error. This is true regardless of the relevant science, whether archaeology or aeronautics, economics or engineering, pharmacology or toxicology or epidemiology. Achieving the best social decisions requires not only understanding and using what we know but also appreciating and weighing the extent of our uncertainty. In making the best use of ... information in ... decision-making, it is still true that the beginning of wisdom is knowing what it is we do not know." (Vern, Op. cit., Reformatted and engineering substituted for scientific) 
'''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty to develop explanations, calculate, and make predictions. This professional knowledge converges on but never reaches certainty, producing a "legitimacy" from the fruitfulness of its use.''' <ref>Randomness Is Unpredictability, Antony Eagle, The British Journal for the Philosophy of Science, Vol. 56, No. 4 (Dec., 2005), pp. 749-790 </ref> 
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=== Bias and Uncertainty ===
Up to this point, the concept of error, as outlined in uncertainty, could easily be simulated by a truly random variable. There should be no detectable differences around the unseen or specified statistical mean. However, several instances in experience point to a tendency towards one extreme or a pattern of error, a collective that is in itself an error of errors, namely [https://en.wikipedia.org/wiki/Bias bias].

Arguably, this could be part of the choice uncertainty discussion below, but it makes more sense to be looked at on its own. Acting in the face of uncertainty is an exercise of personal knowledge or experience. Whether using professional knowledge or individual experience, the potential is there to make a series of choices that, in some instances, make errors more likely than they would be if considered in the context of a statistical analysis.

This tendency for an individual to make the same error in a predictable or repeatable manner is termed bias. Personal experience, beliefs, knowledge, data, and models all have inherent built-in factors, errors, or defects that create predictable error patterns when applied in decision-making. Walker (1998) argues implicitly that such bias can also be considered systemic error. This type of uncertainty is difficult to identify except on theoretical grounds when the magnitude and direction of the bias are known. Most of these biases, whether systemic or knowledge-based, individual or practice-based, require disciplined efforts to "validate" such methods, models, and judgments at all layers while bounding the uncertainty.
: This is the challenge to professional knowledge that must be overcome, namely, developing and validating models and methods for managing bounded uncertainty that minimize this tendency towards repeatable groups of error or bias. Therefore, from this point forward, the discussion will refer to 'uncertainty and its proxies, error, and bias.'
===Propagating Error and Uncertainty===
Partitioning uncertainty and its proxies, error, and bias into logically distinct subsets involves or is associated with multiple-step execution or choice frameworks. Combining those measurements or choices into single parameters or choices involves, in some cases, assembling the data/information in a unique or limited range of sequences. This implies that associated errors in observing, calculating, or choosing will have unequal impacts or consequences depending on where in the chain the error occurs. Also implied in this is the possibility that subsequent errors will be causally linked to some degree. (In statistical practice, this depends upon whether the errors are independent of each other or correlated, specifically, co-variant.)
:'''Professions develop knowledge of how uncertainty and its proxies, error, and bias propagate through an ordered and normative series of observations or choices. This allows the profession to prioritize error and bias reduction to achieve an optimized reduction of uncertainty.'''
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===Cognition, Metacognition and Uncertainty===
The concept of choice and the resultant error in uncertainty can be further delimited depending on the role of [https://en.wikipedia.org/wiki/Cognition cognition], [https://en.wikipedia.org/wiki/Metacognition meta-cognition], and independent external elements. The rationale for this argument is that such choice activity in the face of uncertainty is a conscious, cognitive act.

An argument could be made that such cognition is a prerequisite for exercising choice and introducing error, as presented and discussed above. The counterfactual to this argument is the example of uninformed choice. Choices introduce as much as errors or more as informed choices could be made. (???) Choice, in this sense, is indifferent to knowledge. Purposeful choice in an environment of objectives requires knowledge, method, and experience, but uninformed, speculative choice does not.
:"''' Cognition''' is the set of all mental abilities and processes related to knowledge, attention, memory and working memory, judgment and evaluation, reasoning and "computation," problem-solving and decision making, comprehension and production of language, etc. Human cognition is conscious and unconscious, concrete or abstract, as well as intuitive (like knowledge of a language) and conceptual (like a model of a language). Cognitive processes use existing knowledge and generate new knowledge." (Wikipedia)
:"'''Metacognition''' is "cognition about cognition", or "knowing about knowing" and can take many forms. It includes knowledge about when and how to use particular strategies for learning or problem-solving. There are generally two aspects of [https://en.wikipedia.org/wiki/Metacognition metacognition]: knowledge about cognition and regulation of cognition."
Some types of metacognition knowledge are:
:'''Person knowledge''' (declarative knowledge), which is understanding one's own capabilities.
:'''Task knowledge''' (procedural knowledge), which is how one perceives the difficulty of a task, which is the content, length, and type of assignment.
:'''Strategic knowledge''' (conditional knowledge) is one's capability to use strategies to learn information. (Accessed at Wikipedia)
Like metacognitive knowledge, metacognitive regulation or "cognitive control" contains three essential skills.
:'''Planning:''' refers to the appropriate selection of strategies and the correct allocation of resources that affect task performance.
:'''Monitoring:''' refers to one's awareness of comprehension and task performance.
:'''Evaluating:''' refers to appraising the final product of a task and the efficiency at which the task was performed. This can include re-evaluating strategies that were used. (Accessed at Wikipedia)
Nothing has been said up to this point that limits this structure to an individual, or a collective, or two entities making cognitive choices that are incompatible or inconsistent to varying degrees. Interpretations of this scheme include an individual professional exercising judgment in a decision that, in turn, relies upon the collective judgment and decision of the discipline as an underlying basis versus the decision of another individual to oppose/protest or otherwise contest that decision. Thus, the framework of cognitive/meta-cognitive applies to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, economics of regulatory practice, and game theory.
:By solving useful problems in the face of inherent uncertainty, discipline-specific frameworks of cognitive/meta-cognitive activity apply to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, the economics of regulatory practice, and game theory.
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===Probability and Uncertainty===
Probability is a [https://en.wikipedia.org/wiki/Coherence_theory_of_truth coherent] approach to uncertainty in the physical and mathematics or "classical" domains such as engineering. <ref>See Colyvan for a critique of this claim, Colyvan Mark. "Is probability the only coherent approach to uncertainty?." Risk Analysis 28.3 (2008): 645-652.</ref> and is the "...is certainly the best-known and most widely used formalism for quantifying uncertainty.<ref>Morgan, Millett Granger, Max Henrion, and Mitchell Small. Uncertainty: a guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge University Press, 1992.</ref> [https://en.wikipedia.org/wiki/Cox%27s_theorem Cox’s theorem] ("Any measure of belief is [https://en.wikipedia.org/wiki/Isomorphism isomorphic] (but not necessarily equal) to a probability measure") is a well-known argument for the validity of that argument.
<ref group="note">Cox wanted his system to satisfy the following conditions: 
Divisibility and comparability – The plausibility of a statement is a real number and is dependent on the information we have related to the statement.
:Common sense – Plausibilities should vary sensibly with the assessment of plausibilities in the model.
:Consistency – If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). — preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf</ref>

Another writer, [https://en.wikipedia.org/wiki/Frank_Knight Knight] (1921,1956), presented the following taxonomy of probabilities:
<ref>As presented and discussed in Runde, Jochen. "Clarifying Frank Knight's discussion of the meaning of risk and uncertainty." Cambridge Journal of Economics 22.5 (1998): 539-546.</ref>
: '''Classical 'a priori' probability:''' As an idealized model, numerical probabilities are computed based on generalized principles of assigning equal likelihoods and mutually exhaustive possible outcomes (Runde, 1998) to all set members. Such probabilities are assigned to "...outcomes based on a judgment of indifference between those outcomes, that is, based on the absence of any evidence of real influences in play that may render any one outcome more or less probable than any other." (Runde, op. cit.) This builds upon Knights's concept of an 'absolutely homogeneous classification of instances that are completely identical except for [https://en.wikipedia.org/wiki/Indeterminate_(variable) indeterminate] factors. This judgment of probability or logical probability is on the same logical plane as the propositions of mathematics and ultimately [https://en.wikipedia.org/wiki/Inductivism inductions] from experience.'(Knight, 1956, pg.225)
: '''Bayesian 'a priori' probability:''' In contrast to interpreting probability as the "frequency" or "propensity" of some phenomenon, [https://en.wikipedia.org/wiki/Bayesian_probability Bayesian probability] is a quantity that we assign to represent a state of knowledge or a state of belief. In this view, probability is assigned to a hypothesis, often using the basis of past or prior experience. In contrast, under the frequentist view, a hypothesis is typically tested without being assigned a probability based on prior experience. The Bayesian interpretation of probability can be seen as an extension of propositional logic that enables reasoning with hypotheses, i.e., propositions whose truth or falsity is uncertain. Bayesian probability belongs to the category of evidential probabilities; to evaluate the probability of a hypothesis, the Bayesian probabilist specifies some prior probability, which is then updated in the light of new, relevant data (evidence). The Bayesian interpretation provides a standard set of procedures and formulae to perform this calculation. [11]
: '''Statistical or 'a posterior probability:''' Empirical evaluation of the frequency of association between predicates, not analyzable into varying combinations of equally probable or logical, 'a priori' alternatives. Knight argued that any "high degree of confidence" that the judgment that experience will remain valid for future predictions is "...still based on an 'a priori" judgment of indeterminateness." (Knight, Op. Cit.) Knight's argument to support this is what will be discussed further below that first, "...the impossibility of eliminating all factors not really indeterminate; and, second, the impossibility of enumerating the equally probable alternatives involved and determining their mode of combination so as to evaluate the probability by a priori calculation." (Knight, Op. Cit.) Knight noted that the main difference between this form and that of logical or inductive probability was the presence of empirical information, which allowed the analyst to identify propensity and direction in the observed phenomena. In this case, 'a priori' probability values may be derived from first principles and "statistical probabilities are determined a posteriori by the empirical method of counting instances." (Runde [1998] quoting Knight, op. cit.)
:'''Estimates:''' The distinction here is that there is no valid basis ('a priori' or 'a posteriori') for any classifying phenomena. For Knight, this form of probability presented the greatest logical difficulties of all ... but its distinction from the other types must be emphasized, and some of its complicated relations indicated..." (pp. 224-5, emphasis in the original)

Arguably, Knight's concept of probability can be viewed as "..a continuum of probability situations, depending on the degree of homogeneity of the 'instances' in question" [12] ; i.e. going from a logically distinct but equally likely set of elements to a unique set with one member with statistical frequency in the middle. Knight's main motivation for distinguishing between a priori probability and statistical probability underscores his opinion that "(i) the 'mathematical or a priori type of probability is practically never met with in business, while the second is extremely common'; and (ii) 'the statistical treatment never gives closely accurate quantitative results' (Runde quoting Knight pp. 215-16). Runde argues that Knight couldn't conceive of (empirically tabulated) occurrences that are used in daily commerce classes of instances that we have to make do within the course of everyday economic life could be divided into subclasses of instances that are sufficiently homogeneous to permit the determination of what he calls 'real' probability (p. 217).
:'''In modeling degrees of uncertainty, any measure of belief is isomorphic but not necessarily equal to a probability or statistics measure'''.
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===Explanation, Prediction and Uncertainty===
It should be clear that the general notion of uncertainty synonymous with complete ignorance must be bounded as part of a scheme to produce a useful concept of uncertainty in professional practice such as civil engineering.
:The first principle that must be introduced is that such uncertainty must be bounded by reliable and valuable knowledge gained from past experience combined with analytical and computational capabilities to address an immediate and real problem of interest. This could be viewed as the economic interest argument. Only uncertainties associated with physical phenomena and economic scarcity will be addressed.
:The second principle is that the knowledge model is capable of producing statements or assertions in the form of hypotheses that themselves are testable. [https://en.wikipedia.org/wiki/Testability Testability], in this sense, is defined as the property applying to an empirical hypothesis and involves two components:
::The logical property that is variously described as contingency, defeasibility, or falsifiability, which means that counterexamples to the hypothesis are logically possible. Contrast this to a [https://en.wikipedia.org/wiki/Tautology_(logic) logical tautology], which is always true that is true in every possible interpretation. The practical feasibility of observing a reproducible series of such counterexamples if they do exist. In short, a hypothesis is testable if there is some real, non-zero expectation of deciding whether it is true or false of verifiable, reproducible experience. Upon this property of its constituent hypotheses rests the ability to decide whether a theory can be supported or falsified by actual experience data. (Source Wikipedia.)
'''Knowledge models must be capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria described above in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error and bias.'''
===Variability and Aleatory Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge referred to as epistemic uncertainty. Acting in the face of epistemic uncertainty introduces choice uncertainty (discussed below) and results in the production of explanations and predictions. It is implied that there will be testable hypotheses in the form of outcomes of interest to the knowledge professions. Inherent in nature is a variation of results, which may appear to uncertain but isn't. Observations of phenomena will not be the same, and part of the experimental process is to narrow those results down to within an acceptable level of tolerance. Statistical measures seek to identify underlying and unobserved measures of central tendency. These parameters are not directly observed but are derived to a degree of confidence.
In some cases, variation around a mean or within a statistical range may be perfectly acceptable to the knowledge profession. Examples of this are in the physical sciences and medicine. In Engineering and, to a lesser extent, economics, the process or phenomenon may be required to be more controlled to a narrower range than what a natural or "unmanaged" amount of variation would allow. Several courses of action present themselves.
:The first would be to ignore the aleatory phenomenon and choice, which is rejected outright.
:The second would be to assume that, in effect, such variation is itself a form of minimally managed uncertainty and treat it the same as epistemic uncertainty. This is not a desirable outcome as reducing Aleatory error and bias requires
:A third approach would be to acknowledge that process variation is, in some forms, manageable. This means making choices and acting in a way that presumes that the natural occurrence of error can be reduced, or more importantly, bias can be reduced. There is ample precedent for the third approach in civil engineering design and project management. In this sense, the argument is made that the aleatory response to managed efforts results in an elastic variation range. Presumably, an effective management effort either "shifts" the central tendency of the process results or reduces the variation range of occurrence or some other population parameter. Choosing to act and accept that narrower range rather than the broader natural variation may result in an outcome that, while well within the bounds of the expected variation, is outside the arbitrary control limits established by the choice and action. While this phenomenon mimics the broader uncertainty and its proxy error, it is not an error. The information on the variation was known, and the ability to produce narrower results was an error, albeit a pseudo-error, when compared to the broader classes of uncertainty discussed above, such as total ignorance.
::Looking at uncertainty using this third approach means that the error in choosing a new target parameter for this process may contain epistemic errors (our knowledge of the phenomenon may be erroneous, or the model flawed), aleatory uncertainties as discussed, or, more importantly, choice error or bias in thinking that we can move the process results to meet the requirements.
:'''In civil engineering practice, this may mean applying professional judgment in developing partitioning schemes or allocating uncertainty between the three dimensions (Epistemic, Aleatory, and Behavioral) of uncertainty.'''
Lastly, raising the topic like this brings the questions of effectiveness and efficiency to the fore. Advancing knowledge models that reduce error and bias at first are largely choice models resulting in justifications and favorable experiences. At some point in the process, the knowledge profession advances explanations and predictions that require reliable mathematical or logical concepts that are reduced to models and parameters. The question of efficiency becomes important; the broader uncertainty may have been reduced, but the process is economically inefficient. The profession is urged to become more frugal or, for example, reduce the variability of the outcomes. Knowledge professions often operate under conditions that require them to acquire knowledge and reduce uncertainty effectively and efficiently. Doing so requires understanding both epistemic uncertainty, as discussed above, and statistical process variation or aleatory variation.
:'''Knowledge professions manage and reduce uncertainty in the form of error and bias in an economically effective and efficient manner. This effort requires understanding both epistemic and aleatory uncertainty.'''
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===Choice and Behavioral Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge, referred to as epistemic uncertainty, as well as the inherent variations in results, or what was termed aleatory uncertainty. Acting in the face of that epistemic and aleatory uncertainty introduces yet another uncertainty into the process, namely choice uncertainty. This uncertainty reflects uncertainties associated with first-person and third-person choice. This is particularly relevant for engineering with project stakeholders.
===Summary of Semantics Framework for Uncertainty===
Professional disciplines have gradually re-framed their understanding of uncertainty and, thru experience and analysis, have arrived at the following meta-knowledge concepts of uncertainty:
* '''Uncertainty and its proxy, error, have three components: [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic epistemic uncertainty], [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic aleatory uncertainty], and behavioral uncertainty.'''
* '''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty using non-dogmatic beliefs, which recognize the existence of specific criteria and information that would make the believer change their beliefs using coherent approaches such as probability.'''
* '''Knowledge professions develop knowledge of how uncertainty and its proxy, error, propagate through an ordered and normative series of observations or choices that meet testability criteria in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error. An error can then be described, explained, and reduced in ways that uncertainty cannot. This allows the professions to prioritize error reduction to achieve an optimized reduction of uncertainty and produce a "legitimacy" from the fruitfulness of its use.'''
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===Uncertainty versus Risk/Hazard===
Up to this point, nothing has been said about risk or hazard. Now, risk can be defined. Simply put risk or hazard are finite, logically distinct subsets of the broader uncertainty. Risk and hazard are currently defined interchangeably. First and foremost, risk is a specific subset of general uncertainty. Any arbitrary subset of general uncertainty can be defined as a risk if it meets the following criteria:
* Uncertainty is associated with the potential for material error when acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena.
* Uncertainty and its proxy, error, can be partitioned into logically distinct subsets associated with multiple-step execution or choice frameworks in physical environments linked to a unique or limited range of sequences.
**Subcriteria 1:Resulting errors will have varying impacts or consequences depending on where the error occurs and the degree of causal linkage or influence in the chain.
**Subcriteria 2:Potential errors can be sequenced and prioritized to achieve an optimized reduction of error or bias and, by proxy, uncertainty over time.
* Uncertainty and its proxies, error bias, and environments are isomorphic to coherent approaches such as probability.
* Uncertainty and its proxies, error, and bias are isomorphic to coherent approaches such as knowledge models capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria in 'reasoning under uncertainty.'
This is a definition of general risk. There is no differentiation for engineering or economics, medicine or insurance. There is no difference between a 'good' risk consequence and a 'negative' one. Redefining general risk into simpler terms gives the following:
:'''General Risk is a subset of general uncertainty that is associated with the potential for material errors and biases when sequentially acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena; isomorphic to probability measures and methods and capable of testability.'''
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==Logical Framework for Specific Risk Taxonomies==
As noted above, at the most fundamental level, uncertainty is ".....the subjective perception of ignorance." [13] [Note 6] At this point in the discussion, the focus has moved from discussing uncertainty to its framed subset, risk.
:'''Further, the discussion moves from general risk to a professional or discipline-specific risk [https://en.wikipedia.org/wiki/Taxonomy taxonomy]. Beyond this point, risk will be used in place of uncertainty.'''
Han et al. note that, in general, "(t) taxonomies are valuable not only in their comprehensiveness but in their coherent reduction of uncertainty to conceptually discrete elements."
Uncertainty (and, by implication, risk or hazard) taxonomies offer an approach or tool to more precisely identify and define ontological uncertainty so that it can be quantified, analyzed, and communicated. Viewed this way, uncertainty is not a single, monolithic phenomenon but "...multi-dimensional with theoretically distinct domains and constructs that are potentially measurable and related to different outcomes, mechanisms of action, and management strategies." [14] Taxonomic schemes for classifying the different kinds of scientific and, by extension, engineering uncertainty require identifying the various kinds of potential error associated with descriptive scientific or engineering information and knowledge. An example of this is the following quote from a legal authority:
:"I would especially stress the need for an agency to disclose the uncertainty that surrounds its determinations. '''And by uncertainty, I mean the agency's ignorance as well as its quantitative estimates of error."''' (Emphasis added) [15]
'''Given the necessity of acting in the face of enormous uncertainties [16], discipline taxonomies must offer a clearly coherent and probabilistic approach to uncertainty that is sufficiently general in nature, logically distinct and exhaustive in scope and "...provide decision-makers with a foundation for understanding the nature of discipline information..."''' [17]
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=== Scientific Risk Taxonomies ===
The [https://en.wikipedia.org/wiki/Scientific_method scientific method] is a body of techniques for investigating phenomena in its environment, acquiring new knowledge, or correcting and integrating previous knowledge. Inherent in that process are uncertainties of many forms. Any one taxonomy of [https://en.wikipedia.org/wiki/Uncertainty scientific uncertainty] has largely been focused on statistical models used to assess and quantify sampling, errors, and parameters, although several different taxonomies are conceivable: note-24 [18]
====Environment-specific, Phenomena oriented, model-centric taxonomy of uncertainties====
** [https://en.wikipedia.org/wiki/Uncertainty_quantification#Sources_of_uncertainty '''Parameter uncertainty'''] the source of which is model parameters that are inputs to the computer model (mathematical model) but whose exact values are unknown and cannot be controlled, or whose values cannot be exactly inferred by statistical methods. Subsets of this type of uncertainty include but are not limited to:
***'''Experimental uncertainty''' is also known as observation error, which comes from the variability of experimental measurements. An example is repeating an experiment measurement several times using exactly the same settings for all inputs/variables and recording the variability. It is a subset of the larger uncertainty component, parameter uncertainty.
*** '''Parametric variability''' comes from the variability of the input variables of the phenomena model and is also a subset of the larger uncertainty component, parameter uncertainty.
** '''Structural uncertainty''', or model inadequacy, model [https://en.wikipedia.org/wiki/bias bias] or "[https://en.wikipedia.org/wiki/Systemic_bias systemic bias]", or model discrepancy, which comes from the lack of knowledge of the underlying true state of the phenomenon or its environment. It depends on how accurately a mathematical model describes the true state of the phenomenon or what is termed "model fitness". Due to the inherent nature of uncertainty in any knowledge body (scientific or engineering), models are only an approximation to reality. Therefore, any conclusions drawn from the model can be very misleading when the underlying basis is not plausible or lacks validity. '''note-25 [19]]'''
*** What is missing in this context is any concept of output variability due to the model itself. Part of this is due to the lack of recognition of "process" in scientific investigation. The concept of the scientific method started with a single investigator, such as Galileo or Michael Faraday, working in their laboratories. It has grown into planet and solar system scale experiments such as data gathered on planetary flybys such as Mars, Pluto, and Ceres to the hunt for the Higgs Boson. Ultimately, science has become more like engineering in the scale and complexity of its investigations and theory aggregates, such as the "Unified theory" of particle physics. Subsets of this type of uncertainty include but are not limited to:
*** '''Algorithmic uncertainty''', or numerical uncertainty, comes from numerical errors and numerical approximations per implementation of the computer model. Most models are too complicated to solve exactly. For example, the [https://en.wikipedia.org/wiki/Finite_element_method finite element method] or [https://en.wikipedia.org/wiki/Finite_difference_method finite difference method] [may be used to approximate a solution [https://en.wikipedia.org/wiki/Partial_differential_equation partial differential equation], which introduces numerical errors. Other examples are numerical integration and infinite sum truncation, which are necessary approximations in numerical implementation.
*** '''Interpolation uncertainty''' comes from a lack of available data collected from computer model simulations or experimental measurements. For other input settings that don't have simulation data or experimental measurements, one must interpolate or extrapolate to predict the corresponding responses.
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====Commentary====
A more comprehensive taxonomy of uncertainties that acknowledges knowledge, data, and linguistic uncertainties is:
*'''Epistemic uncertainty''' -[https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Epistemic uncertainty] or systematic uncertainty, in contrast, reflects limitations in the current “state of knowledge” underlying models themselves, originates from competing theories or models, is not readily quantifiable, and is manifest by subjective confusion or indecision. It includes uncertainty due to measurement limitations, insufficient data, extrapolations and interpolations, and variability over time and space. Epistemic uncertainty is uncertainty about "...some determinate fact ... because of a lack of complete information. '''cite_note-26 [20]]''' Epistemic uncertainty can be classified into six main types:'''cite_note-27 [21]]'''
*''' Measurement error''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Observational_error Measurement error] is uncertainty that manifests itself as (apparently) random variation in the measurement of a quantity. Repeated measurements will vary and demonstrate classic statistical behavior.
* '''Systematic error''' - [https://en.wikipedia.org/wiki/Observational_error Systematic error] occurs due to bias in the measuring equipment, model, analysis, observation, or sampling procedure. It is formally defined as the difference between the true value of the quantity of interest and the value to which the mean of the results converges as sample or data sizes increase. Unlike measurement error, it is not (apparently) random, and therefore, results subject to systematic error alone do not vary about a true value. Systematic error can result from deliberate judgment to exclude (or include) data, parameters, or models that ought not to be excluded (or included).
** "The only way to deal with systematic error is to recognize a bias in the process, model, or procedure and remove it. Systematic error, however, is notoriously difficult to recognize except on theoretical grounds. Corrections may only be applied when the magnitude and direction of the bias are known. Such corrections underlie the application of double-sampling methods in environmental science." (Regan, et. al., op. cit.)
*'''Natural variation''' - [https://en.wikipedia.org/wiki/Natural_process_variation Natural variation] or underlying process variation occurs in systems that "...change (concerning time, space, or other variables) in ways that are difficult to predict...across the full range of temporal and spatial values (or other related variables)." '''note-28 [22]'''
** This form of uncertainty is reduceable utilizing tools such as [https://en.wikipedia.org/wiki/Statistical_process_control statistical process control] and replication of experiments or observations.
*'''Inherent randomness''' - [https://en.wikipedia.org/wiki/Randomness randomness] or [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty aleatory uncertainty] exists because the system is"...in principle, irreducible to a deterministic one (the most well-known case is described by [https://en.wikipedia.org/wiki/Uncertainty_principle Heisenberg’s uncertainty principle] in quantum mechanics)." '''note-29 [23], note-30 [Note 7]'''
** Even if the argument is accepted that it is unlikely that any given physical system at some level is inherently random, it is important for any taxonomy to distinguish between systems that appear random due to incomplete or inconsistent information and those that are intrinsically random. (Again, see Regan (2002)).
*'''Model uncertainty''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling Model uncertainty] occurs in the process of generating a model as a conceptual representation of a particular phenomenon to create a [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling#Overview simplified reflection of reality]. This selectivity of factors and variables creates uncertainty in at least three ways. (Regan (2002))
** '''Variables and processes''' that are regarded as relevant often represent a trade-off between system knowledge and assumed states and model objectives;
** '''Models''' depict observed processes using logical or mathematical constructs based on underlying theories about system states or dynamics using continuous equations to describe discrete processes.
** '''Curve fitting''' (including interpolation and extrapolation) with mathematical expressions using model variables where inputs are discrete data points.
** Regan (2002) argues that '''model uncertainty''' is "...notoriously difficult to quantify and impossible to eliminate ...(and)...(t)he only reliable way of determining how appropriate a model is for prediction is to perform validation studies."
** Subjective judgment occurs due to the interpretation of scarce or error-prone data.
*** The only way to address this type of uncertainty is to "...assign a degree of belief about an event in the form of a subjective probability." (Regan (2002))
*Linguistic uncertainty - Linguistic uncertainty, or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. '''note-31 [24]], note-32 [25]]'''
** '''Context dependence''' is uncertainty concerning the context in which a statement is to be understood;
** '''Ambiguity''' occurs when words have multiple meanings, and further reduction to a single meaning is not logically possible in a given context.
** '''Underspecificity''' occurs in the presence of "unwanted generality" or multiple interpretations, and reduction to a smaller set or even a single interpretation is not logically possible.
*Stochastic or statistical uncertainty - Stochastic or statistical uncertainty, which is sometimes known as [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Aleatoric uncertainty] as well as the subject of [https://en.wikipedia.org/wiki/Stochastic_control stochastic control] pertains to the parameters of a risk model, originates from sampling or measurement error, and can be quantified and mathematically expressed (e.g., using confidence intervals).
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== Uncertainty Taxonomies in a Legal Environment ==
From a legal perspective, a definition of taxonomy is dictionary-based, such as "the systematic distinguishing, ordering, and naming of type groups within a subject field." given by Walker (op. cit.). Such legal concepts of scientific or engineering taxonomies are not required to be epistemically complete. Still, they must cover "the most significant aspects of ...(current, good discipline practices) ... and of the descriptive information commonly encountered by decision-makers" in a logically distinct manner. (Walker, 1991, p. 571) These taxonomies must be sufficient to catalog the types of uncertainty associated with descriptive assertion, including the critical subset of cause and effect assertions. This legal/decision-maker framework has presented descriptive uncertainty as having six components (Walker, 1991). Although not part of the Walker taxonomy, underlying the scheme is linguistic uncertainty, or "vagueness," as a source of uncertainty, including uncertainties due to context dependence, ambiguity, and under-specificity. '''note-33 [26]]''' Arguably, these uncertainties underlie the most basic element in the taxonomy.
* '''Linguistic '''- or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. (Regan, 2002)
* '''Conceptual'''- the definition by choice and design of descriptive concepts or variables to be used as [https://en.wikipedia.org/wiki/Predicate_(grammar) predicates] where the subject of an assertion identifies what is being discussed and the predicate provides information about the topic or characterizations such as what the subject is, what the subject is doing, or what the subject is like (Wikipedia).
* "Conceptual uncertainty, or the potential for conceptual error, arises whenever predication occurs. Whenever a concept is used to describe something, using certain concepts instead of others begins to structure how we understand the object, event, or instance under discussion. Predication or conceptualization generates useful information about things, but it also can inhibit our ability to think about those things with concepts other than those selected. The concepts used may not be the most fruitful or the best designed- either for scientific purposes or for making wise, fair, effective, and efficient decisions." (Edited for reading, Walker, 1991)
* Similarly, choice and design of descriptive variables enhance and restrict data set membership, creating potentially incomplete, incompatible, or inconsistent data sets, "...the classification categories employed, and the relationships among those categories (nominal, ordinal or scalar)." '''note-34 [27]]'''
* Concept uncertainty '''note-35 [28]''' like systematic error discussed above is notoriously difficult to recognize, except on theoretical grounds, and the magnitude and direction of the bias are known.
Both terms could be analogized to that of [https://en.wikipedia.org/wiki/Accuracy_and_precision#Terminology_of_ISO_5725 accuracy] in [https://www.bipm.org/en/publications/guides/ ISO 5725] which looks at the ability of the concept or system's ability to produce results that are proximate to the "true" value or state of the phenomenon. Conceptual uncertainty and systemic bias are related to another measure of uncertainty: validity. Walker has defined validity as the ability of a concept to quantitatively describe and explain what the phenomenon objective "truly" is. Walker also ties the concepts together when he states, "Validity concerns the "accuracy" of the measurement data, not its precision." (Walker, 1998) 
Additionally, conceptual uncertainty and associated validity issues could be propagated throughout this taxonomy. Every component of action and decision in the face of uncertainty raises validity issues. Walker recognized this when the author defined epistemic choice for concepts used throughout the other uncertainty components or layers.
* '''Measurement''' uncertainty or Misclassification error- the application of the underlying concepts or variables to specific, individual cases and the uncertainty of the reliability (Walker, 1998) of the resultant data. (It is important to remember that assessment observation and measurement are used interchangeably in this analysis.) Thus, an assessment method or procedure is considered "reliable" or "precise" in the scientific terms of ISO 5715 if it repeatedly produces consistent results.
* Measurement uncertainty is logically distinct from conceptual uncertainty, which can only occur after any conceptual uncertainty has been established. Still, misclassification can occur even when the conceptual uncertainty has been minimized. Conversely, underlying errors or errors made in measurement or classification propagate thru the reasoning chain and reduce the quality or value of the final result.
* While uncertainties associated with quantitative measurements or assessments can be statistically evaluated and described, uncertainties related to qualitative assessments are more problematic.
**Working through the taxonomic chain from base concept to causal explanation requires amassing and integrating qualitative information and quantitative data into input parameters for mathematical models. Developing procedures for producing consistent, repeatable sets of information from assessments that minimize conceptual error or systemic bias requires professional efforts to "validate" such methods, models, and judgments at all layers in the process. Validation as a process is something that civil engineering as a practice needs to embrace more frequently.
* '''[https://en.wikipedia.org/wiki/Sampling Sampling]'''- in the classical statistics sense, is the selection of a limited subset of a larger population to use as a basis for making estimates about the population in the form of attributes, proprieties, or parameters. The resulting information would be determinative in making forecasts about future population sampling or possessing a desired amount of predictive power. The uncertainty about the ability of this information to correctly predict future samples is termed sampling error.
* '''Modeling'''- Modeling uncertainty arises whenever a claim is made that out of a class of candidate relationships and variable pairings, one variable pairing inclusive of constants is chosen that possesses a particular or persistent mathematical relationship to another variable X. Modeling errors arise in selecting the wrong variable pair and constants or by incorrectly specifying its constants. (Walker, 1991, pg. 599)
** '''Causal'''- In causal modeling and analysis, the relationship between causal variables is not strictly a mathematical function. Still, it makes testable predictions and explains "...how a system of variables works, why a system works the way it does, or why it makes sense to think of certain variables as a system" at all." (Walker, 1991, pg. 609)
** '''Epistemic'''- the choice of interpretations for fundamental, logical concepts used throughout the other components or layers.
The components of this uncertainty taxonomy can combine in different ways to "...produce different aggregate uncertainties." '''note-36 [29]'''
:''' Only the scientific and engineering professions possess the capacity to develop and validate procedures and models that combine the different kinds of qualitative '''note-37 [30]''' and quantitative information and their associated potential for error into an aggregate measure or "scalar variables" of uncertainty.'''note-38 [31]]
Scalar variables are quantitative variables whose categories are related by some measure of the relevant property's incremental frequency, degree, or amount. (Walker, 1991) Examples are Modulus of Elasticity, Compression stress in elastic materials, etc.

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=== Economics Uncertainty Taxonomies ===
Like scientific concepts of uncertainty, economics is environment-specific (in this case, read sector of economy or markets), phenomenon-oriented (in this case, read human activity and manufactured phenomena such as firms and markets guided by human judgment and decision), and model-centric.
:''' Economics is not interested in all forms of uncertainty, only specific, meaningful subsets possessing casual connections with material impacts, often labeled as 'risks'.'''
One of its earlier writers ([https://en.wikipedia.org/wiki/Frank_Knight#Life_and_career Knight], 1921, 1948, 1957) attempted to define "uncertainty" as the presence of "defects of managerial knowledge" (or [https://en.wikipedia.org/wiki/Knightian_uncertainty knightian uncertainty] and thus risk centric) as risk that is immeasurable, not possible to calculate.(Wikipedia) Risk in this sense was defined as "the ordinary risks of business activity which can ... be reduced... by applying the insurance principle." Knight does acknowledge that risk is a specific and unambiguous subset of uncertainty. (Risk, Uncertainty and Profit, 1957, pg. 19) For Knight, measuring uncertainty meant measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences, which Knight referred to as "the imperfection of knowledge". (Risk, Uncertainty and Profit, 1957, pg. 197) One of Knight's many contributions to this analysis was recognizing information and knowledge's role in economic activity.
:"''(W)e are concerned only to emphasize the fact that '''knowledge is in a sense variable in degree and that the practical problem may relate to the degree of knowledge rather than to its presence or absence in toto.''' We live only by knowing something about the future, while the problems of life, or conduct at least, arise from knowing so little. This is as true of business as of other spheres of activity. The situation's essence is action according to opinion, of greater or less foundation and value, neither entire ignorance nor complete and perfect information, but partial knowledge. If we are to understand the workings of the economic system, we must examine the meaning and significance of uncertainty; and to this end, some inquiry into the nature and function of knowledge itself is necessary.''" (Knight, pg. 199) Emphasis added
Knight also reinforced the difficulties in classifying uncertainties when he analyzed life insurance. In this case, accidental death was an uncertain phenomenon compared to sickness and accident, where an "...objective description and classification of cases was impossible..." (Knight, pg. 248) Another factor that made uncertainties ineligible as risks and, therefore, uninsurable because they were unclassifiable was the exercise of judgment in making decisions by the businessman. (Knight, pg. 251)
This is a simple classification of economic uncertainty and, as an idealization, is philosophically controversial. '''note-39 [32]'''
* Uncertainty and information about the economic environment are distinct from uncertainty about others’ behavior or choices.
* Risk as a specific subset of uncertainty implies that "...all possible acts are known, all possible outcomes arising from each act are known, and it is possible to assign probabilities to each act." '''note-40 [33]'''
Like the economic [https://en.wikipedia.org/wiki/Theory_of_the_firm theory of the firm], an economic theory of uncertainty seeks to explain and predict the nature of a specific subset of uncertainty (in this case, risk relating to physical phenomena and human activity), including its behavior, structure, and relationship to the universal uncertainty of physical phenomena. In this sense, uncertainty, like firms and markets, can establish price equilibriums under the right circumstances different from those that might have occurred in either of the other two. Such a theory of uncertainty offers a partitioning scheme that decomposes uncertainty into distinct and meaningful subsets, of which risk is a significant one. It also explains how the presence and variability of knowledge cause or " drive" economic consequences. Unlike physical phenomena, economic activity creates artificial objects such as products, firms, mediums of exchange, markets, economies, and knowledge about those activities. The economics of uncertainty exists as an alternative to "certainty" models that assume away imperfect knowledge and unknown choice/judgment preferences when it is more efficient for economic decision-making. This also allows the introduction of the logical concept of "hazard" versus "risk". Frank used hazard extensively in Risk, Uncertainty, and Profit, but not to the extent that one could say it was interchangeable. An example of this is in the term "[https://en.wikipedia.org/wiki/Moral_hazard Moral hazard]," where a firm could engage in "riskier" and potentially "uninsurable" behavior if the information were transparent, and, therefore, insurance coverage of losses is limited. note-41 [34]], note-42 [35]]

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=== Uncertainty and Risk in Policy and Regulatory Environment ===
:''"Hume's 'just reasoner', when faced with a difficult public policy decision in current times, would probably commission a risk analysis. But how could they incorporate 'a degree of doubt, caution, and modesty?"'' '''note-43 [36]'''
The problem of "decision-making in the face of uncertainty" is how to regulate based on incomplete information that has the potential to be materially inaccurate. A contextual assumption is that we need to evaluate decision rules for dealing with uncertainty. As a social enterprise, risk regulation, whatever its substantive objectives, should be as '''effective, efficient, and equitable''' as practically possible. These "three E's" form a set of "process objectives" or "meta-goals." From the uncertainty standpoint, causal information can be usefully divided into two major categories: information about groups and information about individuals.

Each category, group, and individual has its distinctive types of inherent uncertainty. These are logically distinct, generally independent, and cumulative, contributing to '''(?????)'''

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=== Engineering Uncertainty Taxonomies ===
Like scientific and economic concepts of uncertainty, engineering is environment-specific, phenomena-oriented, model-centric, external choice, and driven by decision-making in the face of uncertainty. Like economics and unlike science, engineering is not interested in all forms of uncertainty, only those with determinable and material consequences. Like economics, engineering conceives risk as a specific and unambiguous subset of uncertainty determined by measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences. Also, engineering recognizes the role information and, by extension, knowledge plays in constructing the built environment.
'''note-44 [37]]''' Like economics, engineering differs from economics and scientific uncertainty in its interest in behavioral uncertainty.
*"Behavioral or interaction uncertainty is how individuals or organizations act or interact. Behavioral uncertainty arises from four sources: design uncertainty, requirement uncertainty, volitional uncertainty, and human errors." '''note-45 [38]]'''
** A design uncertainty is a choice among alternatives over which an individual or group of individuals exercises direct control but has not yet decided upon.
** Requirement uncertainty includes parameters of interest to and determined by the stakeholder, independent of the engineer or designer.
** Volitional uncertainty is uncertainty about what the subject him/herself will decide. Other people’s future actions and conduct are not entirely predictable, particularly when dealing with other organizations.
** Human errors occur during the development of a system or project due to blunders or mistakes by an individual or individuals.

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==Practice Frameworks for Uncertainty and Risk in Civil Engineering Practice==
===PMIBoK===
PMI does not define uncertainty, but the PMI Body of Knowledge uses the term in 45 places (uncertainty) and as describing properties in 8 places (uncertainties). [See Note 8] PMI does define "risk" and uses the term over 1,300 times in the same document. In its glossary, PMI also defines terms such as "threat" and "opportunity" but not events.[391 The BoK contextually defines uncertainty when it states that project risk has its origins in the uncertainty present in all projects. (Op. Cit., pg 309) 
'''Uncertainty''' is contextually defined and taken as having the property of affecting project execution and the ability to meet stakeholder expectations. Uncertainty is more than the sum of all individual risks (known and unknown). The nature of this uncertainty is not defined, only its capacity to affect something else or its properties. 
'''Risk''' is defined as " ... an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives." (Ibid.) Beyond this formal definition, PMI adds context when, in its risk management section (Sec. 11 ), the BoK states that risk is a caused event or condition with multiple causes and impacts. The BoK even offers specific techniques for mapping cause-and-effect relationships and influence diagrams. (Sec. 11.2.2.5) Not as evident but equally important is the recognition that not only is risk caused but the impact is "triggered". The risk trigger is an event or situation that signals that it is about to occur (Glossary, pg. 566). Risk conditions are factors that affect or otherwise contribute to risk, such as project stakeholders. Risk implicitly retains some residual element of probability in it, or a value of less than 1.0 with risk that approaches a level of near certainty is termed "issues" or "realized risk" .(Op. Cit., pg 309). PMI also links risk to underlying variations in project outcomes. (Ibid.) Risk is based upon data and information which can be assessed as to its quality (Sec. 11.3.2.3). This analysis looks to determine the value of the information and examines the degree to which the risk information is understood as well as its " ... accuracy, quality, reliability and integrity ... " (Ibid.) Part of that is understanding the relationship between occurrence and impact as outlined in PMl's Figure 11-10 Risk Impact Matrix and developing a model for prioritizing risk products and setting the threshold for action.
====Commentary:====
For defining and identifying risk, PMI notes in Sec. 11.2.2.4 that " ... Every project and its plan is conceived and developed based on a set of hypotheses, scenarios, or assumptions." Part of the risk analysis process is identifying risks to the project, such as inaccuracy, instability, inconsistency, or incompleteness of assumptions. Similarly, the quality of management plans, as well as their consistency with others and the project objectives and assumptions are 11 •• .indicators of risk in the project." (Sec. 11.2.2.1) 
Similarly, the role of engineering economics in risk is embedded or implied in the PMI definition of risk when the BoK discusses risk identification as a process of assessing which risks out of the total risk exposure for the project "may affect" the project and distilling their characteristics. Embedded in this statement is the concept that not all risks can cause economic impacts (positive and negative) to the project. Also embedded is the requirement to understand risk characteristics to identify project risk (Cost and schedule). Lastly, risk identification is an iterative process of incrementing project risk documentation, much like project scope, cost, and schedule documentation. 
Lastly, PMI acknowledges the complex constitution of risk aggregates or the "sum" of all risks in its material, but is it a sum? Risk information has descriptive and explanatory content. These are knowledge and meta-knowledge components. Understanding project assumptions is project-specific knowledge, but identifying risks from assumption inaccuracy, instability, inconsistency, or incompleteness requires professional or program meta-knowledge. 
'''Uncertainty exists in all projects, but risk, a subset of uncertainty with material economic impacts, is a key meta-knowledge element for civil engineering practice.'''
===Software Development Process- Risk Focused===
The Unified Process requires the project team to focus on addressing the most critical risks early in the project life
cycle. The deliverables of each iteration, especially in the Elaboration phase, must be selected to ensure that the greatest risks are addressed first. <ref> Booch, Grady, Ivar Jacobson, and James Rumbaugh. "The unified software development process."
Reading: Addison Wesley (1999).</ref>
===American Society of Civil Engineers (ASCE)===
ASCE does not define uncertainty, but the ASCE CE Body of Knowledge (CEBoK) uses the term in 22 places (uncertainty) and describes properties in 13 places (uncertainties). 
The CEBoK does not use the phrase "uncertainty and risk" as PMI and AACE do in their publication. ASCE reverses the two (Risk and uncertainty) in twelve places regarding technical outcomes (Outcome 12). A fundamental difference between PMI and ISO is that risk is considered a subset of uncertainty. ASCE does not define "risk" but uses the term 25 times in the same document. Almost all are in the context of variation of design parameters and not in the classical context of cost, schedule uncertainty, and risk.
====Commentary:====
...
=== American Association of Cost Engineers (AACE) ===
AACE defined uncertainty, risk, and other related terms in its Risk Management Dictionary. <ref>Risk Management Committee uRisk Management Dictionary, Cost Engineering Vol. 37/No. 10, OCTOBER 1995. </ref>
AACE's overall strategy was to define risk and other terms that stem from base uncertainty. AACE first defined uncertainty as .... "(t)he total range of events that may happen and produce risks (including both threats and opportunities) affecting a project (see opportunities, events, conditions, risk, and threats" where the following sub-definitions apply:
** Biases--A lack of objectivity based on the individual's position or perspective. Systematic and predictable relationships between a person's opinion or statement and his/her underlying knowledge or circumstances. Note: there may be "system biases" as well as "individual biases."
** Condition (Uncertain Condition)-Any specific identifiable circumstance (such as the rate of inflation or the quality of labor available) that might affect the outcome of the project
** Event (Uncertain Condition)-is a specific identifiable action (such as a large government project being started in the same labor area as your project) or an act of nature that might happen and that (if it does happen) could affect the outcome of the project.
** Opportunities are Uncertain events that could improve the results or improve the probability that the desired outcome will happen
** Threats are Uncertain events that are potentially negative or reduce the probability that the desired outcome will happen.
*** AACE defines Risk as an "ambiguous term" (sic) that is synonymous with uncertainty or a negative subset such as "threats", or an overall negative impact of all possible uncertainties.
====Commentary:====
...
==Limitations of the definition==
...
==Working definition==
...
==Beneficial outcomes==
...
==See also==
Wiki articles on the project.

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==Notes==
<references group="note" />
<li> Implicit in this definition of uncertainty as a "state of' is ... </li>
: " ... a conceptualization of uncertainty as a subjective, cognitive experience of people--a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one's lack of knowledge, without which one could not feel uncertain; '''uncertainty is a form of "meta-cognition" ... ( or alternatively, its main component, meta-knowledge) ... -a knowing about knowing.'''" (Han, 2011, op. cit., Emphasis added)
</ol>
<ol start="5">
<li> Cox wanted his system to satisfy the following conditions:</li>
: Divisibility and comparability- The plausibility of a statement is a real number and depends on the information we have related to the statement.
: Common sense - Plausibilities should vary sensibly with the assessment of plausibilities in the model.
: Consistency - If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). -preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf
</ol>
<ol start="6">
<li> The utility of existing taxonomies of uncertainty in civil engineering and, by extension, "risk" has been unsatisfactory for this very reason. This is particularly true in cases where the source of the uncertainty is conflicting versus incomplete information. See Regan, et. al.,"A taxonomy and treatment of uncertainty for ecology and conservation biology" (2002)
for a survey of scientific taxonomies for uncertainty.</li>
<li> Regan et al. argue that " ... genuine examples of this kind of uncertainty are hard to find. Even classic cases of random experiments like coin tosses and the throwing of dice are deterministic; it is just that we do not have enough information about the dynamic processes and initial conditions to make any sensible estimates about the outcomes. Such processes are, for all intents and purposes, inherently random, but they are not genuinely inherently random. For similar reasons, complex systems such as ecosystems and weather patterns are unlikely to be inherently random. Similarly, chaotic systems are entirely deterministic. They are unpredictable because the deterministic processes generating them and the relevant initial conditions are hard
to fully specify (see Stewart 1989, Sugihara et al. 1990)." (Regan (2002) </li>
<li> The CEBoK uses the phrase "uncertainty and risk" twice in reference to scheduling (Section 6.5,2) and cost estimating (Section 7.2,2) as well as reversing the two (Risk and Uncertainty) also in two places in discussing project life cycle (both in Section 2.4.1) </li>
</ol>

==References==
<references />

Uncertainty and Risk in Civil Engineering Practice

2024-11-24T16:45:10Z

WikiSysop: /* Probability and Uncertainty */

'''Life is short and the art long; the occasion instant, experiment perilous, decision difficult.''' (Hippocrates as quoted in Fox,)
:''See also [[Project Definition]] , [[Project Life Cycle and Phase Models]] , [[Project Delivery Methods]]''
:''See also [[Technical Outcomes for Civil Engineering:Risk and Uncertainty]]''
==Basic Considerations==
As human beings,'' ... being alive means seeking opportunities and taking risks.'' As knowledge professionals
living in the 21st century, this means coping with an increasingly complex number of uncertainties for humans
living in this environment. We seek to understand better how these uncertainties can be characterized and
managed. The essence of this article and the experience for engineers in general and civil engineers in particular is
that manageable uncertainty is, by definition, termed risk, and its kindred cousin is termed hazard. This causes us to experience the
:" ''... human dread of and fascination for risk and the increasingly important role of risk analysis within societies ...'' " <ref name="McDaniels"> McDaniels, Timothy, and Mitchell Small. Risk analysis and society: an interdisciplinary characterization of the field. Cambridge University Press, 2004, page 1 </ref> (Ibid.)
and, by extension, civil engineering. McDaniels et al. argue that risk management has been
fundamental to our social and governance development for the past 10,000 years. The scale and shape of the
uncertainties faced in this period shaped the societies that have developed today. The central thrust of this effort
over the centuries has been to reshape and re-frame our understanding and conception of uncertainty from one of
complete unknowing and simple acceptance as our fate in life to one of management (cf "''Against the Gods''"
concept in Bernstein's book <ref name = Bernstein">Bernstein, Peter L., and Jesse Boggs. Against the gods. Simon & Schuster 1997.</ref>.
: ''All the knowledge professions and disciplines have struggled with managing uncertainty, for it is impossible to manage unbounded uncertainty.'' <ref group="note">See McDaniels et al. 2004 for an extensive discussion and bibliography on the historical development of Risk Analysis and some key milestones in risk analysis in the 20th century.</ref>
As outlined below, professions such as civil engineering have successfully acted in the face of uncertainty. It lies in the profession's ability to reduce a wide variety of uncertainties into increasingly smaller and crucially bounded subsets that can be managed. These are called 'risks. More recently, this process has evolved, and individual disciplines such as Civil Engineering (CE) have developed their knowledge and models to perform risk analysis. Risk is also taught as a distinct discipline and specialty practice within civil engineering. Still, it has some unique features that set it apart from other more classical practices within CE. As such, it is one of the first of some very specialized CE practices that use knowledge about civil engineering knowledge, or meta-knowledge. Other examples of civil engineering metaknowledge are project controls and quality controls.
==Semantic, Epistemic and Logical frameworks==
The semantic, epistemic, and logical frameworks for uncertainty and risk have several dimensions and layers of logical frameworks. The semantics problems include interchangeable usages for risk and uncertainty and hazard, uncertain and imperfect, and a lack of definitional material or context. The semantical scheme for this article will be to proceed from the distinction between certainty and uncertainty through marginal refinements and reductions of uncertainty up to the point of causal uncertainties. Beyond this point of semantics will be the logic frameworks or the subject of taxonomies of professional knowledge. First, it is about simple, testable phenomena, and then, it moves on to complex taxonomy schemes for artificially constructed phenomena or engineering projects.
<ref group="note">TBD</ref>
==Semantics framework==
===Certainty and Uncertainty===
''''' There is no such thing as absolute certainty, but there is assurance sufficient for the purposes of human life.''''' ([https://en.wikipedia.org/wiki/John_Stuart_Mill John Stuart Mill]) 
'''''If you tried to doubt everything, you would not get as far as doubting anything. The game of doubting itself presupposes certainty.''''' ([https://en.wikipedia.org/wiki/Ludwig_Wittgenstein Wittgenstein] # 115 from On [https://en.wikipedia.org/wiki/On_Certainty Certainty]) 
It is important to note that the references to epistemic or epidemiological knowledge in this article are assumed to relate to three forms of knowledge, namely:
* Knowledge that ( [https://en.wikipedia.org/wiki/Descriptive_knowledge descriptive or declarative or propositional knowledge])
* Knowledge how ( or "[https://en.wikipedia.org/wiki/Procedural_knowledge know-how]"), and
* [https://en.wikipedia.org/wiki/Knowledge_by_acquaintance Knowledge by acquaintance].
'''Certainty''' has been defined as "an epistemic property" of knowledge in all its forms and the state of our beliefs about that knowledge.
<ref>Certainty, Stanford Encyclopedia of Philosophy, accessed on September 12, 2015, at http://plato.stanford.edu/entries/certainty/#ConCer </ref>
Certainty about any belief about knowledge implies that it is not subject to doubt or skepticism. This immunity to criticism can be dogmatically based, emphasizing the importance of a propositional-based sense of truth over experiential, sensory perceptions. <ref>Definition of [https://en.wikipedia.org/wiki/Dogmatic_theology Dogmatic theology or belief], accessed at Wikipedia</ref>
: '''The demarcation line between dogmatic and non-dogmatic beliefs lies in the presence and recognition of specific criteria and information that would make the believer change their beliefs.'''
An empirical framework of [https://en.wikipedia.org/wiki/Testability testability] and [https://en.wikipedia.org/wiki/Falsifiability falsification] is required to recognize this and limit dogmatism. For example, one could argue that people would change their minds if God asked them to. Similarly, one could construct a concept of epistemic as opposed to dogmatic belief certainty as the ability to know anything that one chooses to know and can be known or [https://en.wikipedia.org/wiki/Omniscience inherent omniscience].

A second kind of certainty is epistemic, when conviction reflects the highest possible support for a belief. In this sense, knowledge is separate from beliefs, although someone may have beliefs about a property, such as certainty of knowledge. Logically, it has been shown that there will be unprovable statements within the system for any such knowledge system. Secondly, the knowledge system cannot demonstrate its own consistency. ([https://en.wikipedia.org/wiki/Gödel%27s_incompleteness_theorems Gödel's incompleteness theorems])
: '''Certainty, in real life, is useless or often damaging (the idea is that "total security from error" is impossible in practice, and a complete "lack of doubt" is undesirable)''' ([[https://en.wikipedia.org/wiki/Certainty Physicist Carlo Rovelli]])
'''Uncertainty''', on the other hand, arises immediately in the slightest amount of doubt or criticism.
<ref group="note"> The Merriam-Webster dictionary defines uncertainty as “the quality or state of being uncertain,” which is something of a circular definition. Likewise, one could define uncertainty as the state of not being certain.

Synonyms are distrust, doubt, misgiving, mistrust, reservation, skepticism, and suspicion. Another writer added indefinite, indeterminate, not certain to occur, problematical, unreliable, untrustworthy, unknown beyond doubt, dubious, doubtful, not clearly identified or defined, not constant, variable, and fitful to the list.
:(Han, Paul KJ, William MP Klein, and Neeraj K. Arora. "Varieties of Uncertainty in Health Care: A Conceptual Taxonomy." Medical Decision Making 31.6 (2011): 828-838.)
Han et al. noted that any definition of uncertainty clearly encompasses "...numerous types, sources, and manifestations of uncertainty, and ...(any) ...useful working definition of uncertainty needs to specify the concept underlying these varied meanings of the term."</ref>
Implicit in this definition of uncertainty as a “state of” is ...
"...a conceptualization of uncertainty as a subjective, cognitive experience of people—a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one’s lack of knowledge, without which one could not feel uncertain; uncertainty is a form of “meta-cognition” ...(or its main component, meta-knowledge)... —a knowing about knowing." (Han, 2011, op. cit., Emphasis added)
Similarly, uncertainty could be defined as "...any departure from the unachievable goal of complete [https://en.wikipedia.org/wiki/Determinism determinism]."
<ref name="Walker1">Walker, W. E., Harremoës, P., Rotmans, J., Van Der Sluijs, J. P., Van Asselt, M. B., Janssen, P., & Krayer von Krauss, M. P. (2003). Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support. Integrated assessment, 4(1), 5-17.</ref>
Reasoning under uncertainty is very different than performing the same under certainty. In reasoning under certainty, one has complete knowledge and deduces without doubt and equally important, without limitation, thereby concluding free from error. Reasoning under uncertainty, one works in a state of incomplete, inconsistent, and limited knowledge; doubts cloud any statements or assertions and thereby taint any deductions/inferences resulting in the potential for error. <ref group="note">Implicit in this definition of uncertainty as a “state of” is ...
"...a conceptualization of uncertainty as a subjective, cognitive experience of people—a state of mind rather than a feature of the objective world. The defining feature of this state, furthermore, appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one’s lack of knowledge, without which one could not feel uncertain; uncertainty is a form of “meta-cognition” ...(or alternatively, its main component, meta-knowledge)... —a knowing about knowing." (Han, 2011, op. cit., Emphasis added)</ref>
:'''Knowledge professions reason in a state of incomplete, inconsistent and limited knowledge with doubts that cloud any statements or assertions and taint any deductions/inferences resulting in the potential for error.'''
:''' It is also important to note the semantical and logical correlation between 'reasoning under uncertainty' or 'acting in the face of uncertainty' with the 'potential for' or 'presence of' error. The presence of uncertainty is invariably linked to the potential presence of error.''' The [https://en.wikipedia.org/wiki/Contingency_(philosophy) contingent] nature of uncertainty logically implies the contingent nature of error.
:'''Knowledge Professions use error as a proxy for uncertainty such that within discipline knowledge frameworks, uncertainty can be managed.''' The rationale for this belief is that error can be reliably described, quantified, explained, and ultimately reduced in ways that uncertainty cannot.

'''Uncertainty''' implies a range of variation that is impossible in certainty. Concepts and definitions of uncertainty are unbounded and vast but start from the point of complete and total ignorance. In describing uncertainty, statements can range from complete ignorance to relatively high degrees of belief that there is less potential for error in our reasoning or action. The latter is based upon confidence in justified knowledge, favorable past experience, and reliable parameters/models. One can't make that statement about ranges in certainty. If certainty, by definition, precludes doubt of any type or nature, then how could that be graduated to any degree? 
Uncertainty, on the other hand, can be reduced through disciplined efforts to acquire knowledge. In short, we can reduce or even manage uncertainty (to a degree); how can certainty be improved? Are there higher degrees of perfection? The answer is no. We reason from an uncertain starting point. Yet this effort presumes, as Mill and Wittgenstein postulated, that we can develop or acquire an increasing degree of belief or conviction that there is less potential for error in our reasoning or action. This position assumes that some way exists to identify what a lesser degree of uncertainty would look like.
:'''Knowledge professions acquire an increasing belief or conviction that there is less potential for error in their reasoning or action based on justified knowledge, favorable past experience, and reliable parameters/models.'''
Uncertainty is, in some form, amenable to description and explanation through observation and analysis. It is explainable and predictable to such a degree that it interests professionals such as scientists and engineers. Professional knowledge of this type has explanatory or predictive power, albeit limited in scope and application to relevant subjects such as physical objects or phenomena and functionality.
:'''The very act of reducing the scope of uncertainty to a limited set of phenomena implies choice and, ultimately, the decision to act in the face of such uncertainty, but acting or judging in this manner adds more value than doing the same under relative ignorance.'''
'''Uncertainty''', as described above, implies a hybrid nature. Examples of this are economic or decision-theoretic applications. In economics, [https://en.wikipedia.org/wiki/Perfect_information perfect information] allows one within the limiting framework of perfect competition in economic games to make decisions with 'perfect knowledge.' The crucial difference here is the distinction between '[https://en.wikipedia.org/wiki/Perfection perfection],' broadly, a state of completeness and lawlessness, and 'perfect, which can be thought of as an analytical limit that is approachable but can never be attained. Arbitrarily delimiting the realm of knowledge into defined, finite boundaries allows the simulated production of perfect choice, assumed crucially, to be free from error. The hybrid nature of this form of 'simulated' certainty produced within boundaries defined by assumptions and, therefore, clouded by doubt is itself a form of uncertainty. The importance of this is the semantical and logical association that exists between certainty and perfection versus the hybrid concept of a perfect anything, whether knowledge, infraction, competition, etc., or in the case of engineering, elastic, permeable, conductive, etc., being contained within an admittedly imperfect matrix of uncertainty.
:'''Simply put, a finite, heavily bounded piece of uncertain knowledge can be improved through the simulated use of [https://www.merriam-webster.com/dictionary/assumed assumedly] perfect information, but the perfection of knowledge cannot be attained or simulated.'''
Engineering and Economics, for example, simulate finite elements of perfect knowledge or information, heavily bounded with assumptions and a range of applications. By doing so, these professions reduce the bounds and variability of uncertainty and thereby manage it. Using these hybrid models to acquire knowledge takes place in an economy (regulated or institutional) and environment (transparent and structured), which puts pressure on the various professions to recognize and use qualitative and quantitative knowledge better to reduce uncertainties in their activities. The challenges in this reflect the nature of the profession's or discipline's knowledge (e.g., scientific, engineering, or medical), the policies and structure of the relevant professional knowledge bodies, process objectives, constraints, and "the elusive demands of politics." <ref>Walker, Vern R. "The siren songs of science: toward a taxonomy of scientific uncertainty for decisionmakers." Conn. L. Rev. 23 (1990): 567.</ref>
One common theme runs through all of these efforts to address the presence of uncertainty in professional activities. 
:"The available scientific information upon which a decision must be made is almost always a mixture of engineering knowledge and uncertainty. Regardless of the information or methodology, there is the potential for error. This is true regardless of the relevant science, whether archaeology or aeronautics, economics or engineering, pharmacology or toxicology or epidemiology. Achieving the best social decisions requires not only understanding and using what we know but also appreciating and weighing the extent of our uncertainty. In making the best use of ... information in ... decision-making, it is still true that the beginning of wisdom is knowing what it is we do not know." (Vern, Op. cit., Reformatted and engineering substituted for scientific) 
'''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty to develop explanations, calculate, and make predictions. This professional knowledge converges on but never reaches certainty, producing a "legitimacy" from the fruitfulness of its use.''' <ref>Randomness Is Unpredictability, Antony Eagle, The British Journal for the Philosophy of Science, Vol. 56, No. 4 (Dec., 2005), pp. 749-790 </ref> 
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=== Bias and Uncertainty ===
Up to this point, the concept of error, as outlined in uncertainty, could easily be simulated by a truly random variable. There should be no detectable differences around the unseen or specified statistical mean. However, several instances in experience point to a tendency towards one extreme or a pattern of error, a collective that is in itself an error of errors, namely [https://en.wikipedia.org/wiki/Bias bias].

Arguably, this could be part of the choice uncertainty discussion below, but it makes more sense to be looked at on its own. Acting in the face of uncertainty is an exercise of personal knowledge or experience. Whether using professional knowledge or individual experience, the potential is there to make a series of choices that, in some instances, make errors more likely than they would be if considered in the context of a statistical analysis.

This tendency for an individual to make the same error in a predictable or repeatable manner is termed bias. Personal experience, beliefs, knowledge, data, and models all have inherent built-in factors, errors, or defects that create predictable error patterns when applied in decision-making. Walker (1998) argues implicitly that such bias can also be considered systemic error. This type of uncertainty is difficult to identify except on theoretical grounds when the magnitude and direction of the bias are known. Most of these biases, whether systemic or knowledge-based, individual or practice-based, require disciplined efforts to "validate" such methods, models, and judgments at all layers while bounding the uncertainty.
: This is the challenge to professional knowledge that must be overcome, namely, developing and validating models and methods for managing bounded uncertainty that minimize this tendency towards repeatable groups of error or bias. Therefore, from this point forward, the discussion will refer to 'uncertainty and its proxies, error, and bias.'
===Propagating Error and Uncertainty===
Partitioning uncertainty and its proxies, error, and bias into logically distinct subsets involves or is associated with multiple-step execution or choice frameworks. Combining those measurements or choices into single parameters or choices involves, in some cases, assembling the data/information in a unique or limited range of sequences. This implies that associated errors in observing, calculating, or choosing will have unequal impacts or consequences depending on where in the chain the error occurs. Also implied in this is the possibility that subsequent errors will be causally linked to some degree. (In statistical practice, this depends upon whether the errors are independent of each other or correlated, specifically, co-variant.)
:'''Professions develop knowledge of how uncertainty and its proxies, error, and bias propagate through an ordered and normative series of observations or choices. This allows the profession to prioritize error and bias reduction to achieve an optimized reduction of uncertainty.'''
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===Cognition, Metacognition and Uncertainty===
The concept of choice and the resultant error in uncertainty can be further delimited depending on the role of [https://en.wikipedia.org/wiki/Cognition cognition], [https://en.wikipedia.org/wiki/Metacognition meta-cognition], and independent external elements. The rationale for this argument is that such choice activity in the face of uncertainty is a conscious, cognitive act.

An argument could be made that such cognition is a prerequisite for exercising choice and introducing error, as presented and discussed above. The counterfactual to this argument is the example of uninformed choice. Choices introduce as much as errors or more as informed choices could be made. (???) Choice, in this sense, is indifferent to knowledge. Purposeful choice in an environment of objectives requires knowledge, method, and experience, but uninformed, speculative choice does not.
:"''' Cognition''' is the set of all mental abilities and processes related to knowledge, attention, memory and working memory, judgment and evaluation, reasoning and "computation," problem-solving and decision making, comprehension and production of language, etc. Human cognition is conscious and unconscious, concrete or abstract, as well as intuitive (like knowledge of a language) and conceptual (like a model of a language). Cognitive processes use existing knowledge and generate new knowledge." (Wikipedia)
:"'''Metacognition''' is "cognition about cognition", or "knowing about knowing" and can take many forms. It includes knowledge about when and how to use particular strategies for learning or problem-solving. There are generally two aspects of [https://en.wikipedia.org/wiki/Metacognition metacognition]: knowledge about cognition and regulation of cognition."
Some types of metacognition knowledge are:
:'''Person knowledge''' (declarative knowledge), which is understanding one's own capabilities.
:'''Task knowledge''' (procedural knowledge), which is how one perceives the difficulty of a task, which is the content, length, and type of assignment.
:'''Strategic knowledge''' (conditional knowledge) is one's capability to use strategies to learn information. (Accessed at Wikipedia)
Like metacognitive knowledge, metacognitive regulation or "cognitive control" contains three essential skills.
:'''Planning:''' refers to the appropriate selection of strategies and the correct allocation of resources that affect task performance.
:'''Monitoring:''' refers to one's awareness of comprehension and task performance.
:'''Evaluating:''' refers to appraising the final product of a task and the efficiency at which the task was performed. This can include re-evaluating strategies that were used. (Accessed at Wikipedia)
Nothing has been said up to this point that limits this structure to an individual, or a collective, or two entities making cognitive choices that are incompatible or inconsistent to varying degrees. Interpretations of this scheme include an individual professional exercising judgment in a decision that, in turn, relies upon the collective judgment and decision of the discipline as an underlying basis versus the decision of another individual to oppose/protest or otherwise contest that decision. Thus, the framework of cognitive/meta-cognitive applies to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, economics of regulatory practice, and game theory.
:By solving useful problems in the face of inherent uncertainty, discipline-specific frameworks of cognitive/meta-cognitive activity apply to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, the economics of regulatory practice, and game theory.
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===Probability and Uncertainty===
Probability is a [https://en.wikipedia.org/wiki/Coherence_theory_of_truth coherent] approach to uncertainty in the physical and mathematics or "classical" domains such as engineering. <ref>See Colyvan for a critique of this claim, Colyvan Mark. "Is probability the only coherent approach to uncertainty?." Risk Analysis 28.3 (2008): 645-652.</ref> and is the "...is certainly the best-known and most widely used formalism for quantifying uncertainty.<ref>Morgan, Millett Granger, Max Henrion, and Mitchell Small. Uncertainty: a guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge University Press, 1992.</ref> [https://en.wikipedia.org/wiki/Cox%27s_theorem Cox’s theorem] ("Any measure of belief is [https://en.wikipedia.org/wiki/Isomorphism isomorphic] (but not necessarily equal) to a probability measure") is a well-known argument for the validity of that argument.
<ref group="note">Cox wanted his system to satisfy the following conditions: 
Divisibility and comparability – The plausibility of a statement is a real number and is dependent on the information we have related to the statement.
:Common sense – Plausibilities should vary sensibly with the assessment of plausibilities in the model.
:Consistency – If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). — preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf</ref>

Another writer, [https://en.wikipedia.org/wiki/Frank_Knight Knight] (1921,1956), presented the following taxonomy of probabilities: [10]
: '''Classical 'a priori' probability:''' As an idealized model, numerical probabilities are computed based on generalized principles of assigning equal likelihoods and mutually exhaustive possible outcomes (Runde, 1998) to all set members. Such probabilities are assigned to "...outcomes based on a judgment of indifference between those outcomes, that is, based on the absence of any evidence of real influences in play that may render any one outcome more or less probable than any other." (Runde, op. cit.) This builds upon Knights's concept of an 'absolutely homogeneous classification of instances that are completely identical except for [https://en.wikipedia.org/wiki/Indeterminate_(variable) indeterminate] factors. This judgment of probability or logical probability is on the same logical plane as the propositions of mathematics and ultimately [https://en.wikipedia.org/wiki/Inductivism inductions] from experience.'(Knight, 1956, pg.225)
: '''Bayesian 'a priori' probability:''' In contrast to interpreting probability as the "frequency" or "propensity" of some phenomenon, [https://en.wikipedia.org/wiki/Bayesian_probability Bayesian probability] is a quantity that we assign to represent a state of knowledge or a state of belief. In this view, probability is assigned to a hypothesis, often using the basis of past or prior experience. In contrast, under the frequentist view, a hypothesis is typically tested without being assigned a probability based on prior experience. The Bayesian interpretation of probability can be seen as an extension of propositional logic that enables reasoning with hypotheses, i.e., propositions whose truth or falsity is uncertain. Bayesian probability belongs to the category of evidential probabilities; to evaluate the probability of a hypothesis, the Bayesian probabilist specifies some prior probability, which is then updated in the light of new, relevant data (evidence). The Bayesian interpretation provides a standard set of procedures and formulae to perform this calculation. [11]
: '''Statistical or 'a posterior probability:''' Empirical evaluation of the frequency of association between predicates, not analyzable into varying combinations of equally probable or logical, 'a priori' alternatives. Knight argued that any "high degree of confidence" that the judgment that experience will remain valid for future predictions is "...still based on an 'a priori" judgment of indeterminateness." (Knight, Op. Cit.) Knight's argument to support this is what will be discussed further below that first, "...the impossibility of eliminating all factors not really indeterminate; and, second, the impossibility of enumerating the equally probable alternatives involved and determining their mode of combination so as to evaluate the probability by a priori calculation." (Knight, Op. Cit.) Knight noted that the main difference between this form and that of logical or inductive probability was the presence of empirical information, which allowed the analyst to identify propensity and direction in the observed phenomena. In this case, 'a priori' probability values may be derived from first principles and "statistical probabilities are determined a posteriori by the empirical method of counting instances." (Runde [1998] quoting Knight, op. cit.)
:'''Estimates:''' The distinction here is that there is no valid basis ('a priori' or 'a posteriori') for any classifying phenomena. For Knight, this form of probability presented the greatest logical difficulties of all ... but its distinction from the other types must be emphasized, and some of its complicated relations indicated..." (pp. 224-5, emphasis in the original)

Arguably, Knight's concept of probability can be viewed as "..a continuum of probability situations, depending on the degree of homogeneity of the 'instances' in question" [12] ; i.e. going from a logically distinct but equally likely set of elements to a unique set with one member with statistical frequency in the middle. Knight's main motivation for distinguishing between a priori probability and statistical probability underscores his opinion that "(i) the 'mathematical or a priori type of probability is practically never met with in business, while the second is extremely common'; and (ii) 'the statistical treatment never gives closely accurate quantitative results' (Runde quoting Knight pp. 215-16). Runde argues that Knight couldn't conceive of (empirically tabulated) occurrences that are used in daily commerce classes of instances that we have to make do within the course of everyday economic life could be divided into subclasses of instances that are sufficiently homogeneous to permit the determination of what he calls 'real' probability (p. 217).
:'''In modeling degrees of uncertainty, any measure of belief is isomorphic but not necessarily equal to a probability or statistics measure'''.
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===Explanation, Prediction and Uncertainty===
It should be clear that the general notion of uncertainty synonymous with complete ignorance must be bounded as part of a scheme to produce a useful concept of uncertainty in professional practice such as civil engineering.
:The first principle that must be introduced is that such uncertainty must be bounded by reliable and valuable knowledge gained from past experience combined with analytical and computational capabilities to address an immediate and real problem of interest. This could be viewed as the economic interest argument. Only uncertainties associated with physical phenomena and economic scarcity will be addressed.
:The second principle is that the knowledge model is capable of producing statements or assertions in the form of hypotheses that themselves are testable. [https://en.wikipedia.org/wiki/Testability Testability], in this sense, is defined as the property applying to an empirical hypothesis and involves two components:
::The logical property that is variously described as contingency, defeasibility, or falsifiability, which means that counterexamples to the hypothesis are logically possible. Contrast this to a [https://en.wikipedia.org/wiki/Tautology_(logic) logical tautology], which is always true that is true in every possible interpretation. The practical feasibility of observing a reproducible series of such counterexamples if they do exist. In short, a hypothesis is testable if there is some real, non-zero expectation of deciding whether it is true or false of verifiable, reproducible experience. Upon this property of its constituent hypotheses rests the ability to decide whether a theory can be supported or falsified by actual experience data. (Source Wikipedia.)
'''Knowledge models must be capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria described above in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error and bias.'''
===Variability and Aleatory Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge referred to as epistemic uncertainty. Acting in the face of epistemic uncertainty introduces choice uncertainty (discussed below) and results in the production of explanations and predictions. It is implied that there will be testable hypotheses in the form of outcomes of interest to the knowledge professions. Inherent in nature is a variation of results, which may appear to uncertain but isn't. Observations of phenomena will not be the same, and part of the experimental process is to narrow those results down to within an acceptable level of tolerance. Statistical measures seek to identify underlying and unobserved measures of central tendency. These parameters are not directly observed but are derived to a degree of confidence.
In some cases, variation around a mean or within a statistical range may be perfectly acceptable to the knowledge profession. Examples of this are in the physical sciences and medicine. In Engineering and, to a lesser extent, economics, the process or phenomenon may be required to be more controlled to a narrower range than what a natural or "unmanaged" amount of variation would allow. Several courses of action present themselves.
:The first would be to ignore the aleatory phenomenon and choice, which is rejected outright.
:The second would be to assume that, in effect, such variation is itself a form of minimally managed uncertainty and treat it the same as epistemic uncertainty. This is not a desirable outcome as reducing Aleatory error and bias requires
:A third approach would be to acknowledge that process variation is, in some forms, manageable. This means making choices and acting in a way that presumes that the natural occurrence of error can be reduced, or more importantly, bias can be reduced. There is ample precedent for the third approach in civil engineering design and project management. In this sense, the argument is made that the aleatory response to managed efforts results in an elastic variation range. Presumably, an effective management effort either "shifts" the central tendency of the process results or reduces the variation range of occurrence or some other population parameter. Choosing to act and accept that narrower range rather than the broader natural variation may result in an outcome that, while well within the bounds of the expected variation, is outside the arbitrary control limits established by the choice and action. While this phenomenon mimics the broader uncertainty and its proxy error, it is not an error. The information on the variation was known, and the ability to produce narrower results was an error, albeit a pseudo-error, when compared to the broader classes of uncertainty discussed above, such as total ignorance.
::Looking at uncertainty using this third approach means that the error in choosing a new target parameter for this process may contain epistemic errors (our knowledge of the phenomenon may be erroneous, or the model flawed), aleatory uncertainties as discussed, or, more importantly, choice error or bias in thinking that we can move the process results to meet the requirements.
:'''In civil engineering practice, this may mean applying professional judgment in developing partitioning schemes or allocating uncertainty between the three dimensions (Epistemic, Aleatory, and Behavioral) of uncertainty.'''
Lastly, raising the topic like this brings the questions of effectiveness and efficiency to the fore. Advancing knowledge models that reduce error and bias at first are largely choice models resulting in justifications and favorable experiences. At some point in the process, the knowledge profession advances explanations and predictions that require reliable mathematical or logical concepts that are reduced to models and parameters. The question of efficiency becomes important; the broader uncertainty may have been reduced, but the process is economically inefficient. The profession is urged to become more frugal or, for example, reduce the variability of the outcomes. Knowledge professions often operate under conditions that require them to acquire knowledge and reduce uncertainty effectively and efficiently. Doing so requires understanding both epistemic uncertainty, as discussed above, and statistical process variation or aleatory variation.
:'''Knowledge professions manage and reduce uncertainty in the form of error and bias in an economically effective and efficient manner. This effort requires understanding both epistemic and aleatory uncertainty.'''
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===Choice and Behavioral Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge, referred to as epistemic uncertainty, as well as the inherent variations in results, or what was termed aleatory uncertainty. Acting in the face of that epistemic and aleatory uncertainty introduces yet another uncertainty into the process, namely choice uncertainty. This uncertainty reflects uncertainties associated with first-person and third-person choice. This is particularly relevant for engineering with project stakeholders.
===Summary of Semantics Framework for Uncertainty===
Professional disciplines have gradually re-framed their understanding of uncertainty and, thru experience and analysis, have arrived at the following meta-knowledge concepts of uncertainty:
* '''Uncertainty and its proxy, error, have three components: [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic epistemic uncertainty], [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic aleatory uncertainty], and behavioral uncertainty.'''
* '''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty using non-dogmatic beliefs, which recognize the existence of specific criteria and information that would make the believer change their beliefs using coherent approaches such as probability.'''
* '''Knowledge professions develop knowledge of how uncertainty and its proxy, error, propagate through an ordered and normative series of observations or choices that meet testability criteria in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error. An error can then be described, explained, and reduced in ways that uncertainty cannot. This allows the professions to prioritize error reduction to achieve an optimized reduction of uncertainty and produce a "legitimacy" from the fruitfulness of its use.'''
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===Uncertainty versus Risk/Hazard===
Up to this point, nothing has been said about risk or hazard. Now, risk can be defined. Simply put risk or hazard are finite, logically distinct subsets of the broader uncertainty. Risk and hazard are currently defined interchangeably. First and foremost, risk is a specific subset of general uncertainty. Any arbitrary subset of general uncertainty can be defined as a risk if it meets the following criteria:
* Uncertainty is associated with the potential for material error when acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena.
* Uncertainty and its proxy, error, can be partitioned into logically distinct subsets associated with multiple-step execution or choice frameworks in physical environments linked to a unique or limited range of sequences.
**Subcriteria 1:Resulting errors will have varying impacts or consequences depending on where the error occurs and the degree of causal linkage or influence in the chain.
**Subcriteria 2:Potential errors can be sequenced and prioritized to achieve an optimized reduction of error or bias and, by proxy, uncertainty over time.
* Uncertainty and its proxies, error bias, and environments are isomorphic to coherent approaches such as probability.
* Uncertainty and its proxies, error, and bias are isomorphic to coherent approaches such as knowledge models capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria in 'reasoning under uncertainty.'
This is a definition of general risk. There is no differentiation for engineering or economics, medicine or insurance. There is no difference between a 'good' risk consequence and a 'negative' one. Redefining general risk into simpler terms gives the following:
:'''General Risk is a subset of general uncertainty that is associated with the potential for material errors and biases when sequentially acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena; isomorphic to probability measures and methods and capable of testability.'''
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==Logical Framework for Specific Risk Taxonomies==
As noted above, at the most fundamental level, uncertainty is ".....the subjective perception of ignorance." [13] [Note 6] At this point in the discussion, the focus has moved from discussing uncertainty to its framed subset, risk.
:'''Further, the discussion moves from general risk to a professional or discipline-specific risk [https://en.wikipedia.org/wiki/Taxonomy taxonomy]. Beyond this point, risk will be used in place of uncertainty.'''
Han et al. note that, in general, "(t) taxonomies are valuable not only in their comprehensiveness but in their coherent reduction of uncertainty to conceptually discrete elements."
Uncertainty (and, by implication, risk or hazard) taxonomies offer an approach or tool to more precisely identify and define ontological uncertainty so that it can be quantified, analyzed, and communicated. Viewed this way, uncertainty is not a single, monolithic phenomenon but "...multi-dimensional with theoretically distinct domains and constructs that are potentially measurable and related to different outcomes, mechanisms of action, and management strategies." [14] Taxonomic schemes for classifying the different kinds of scientific and, by extension, engineering uncertainty require identifying the various kinds of potential error associated with descriptive scientific or engineering information and knowledge. An example of this is the following quote from a legal authority:
:"I would especially stress the need for an agency to disclose the uncertainty that surrounds its determinations. '''And by uncertainty, I mean the agency's ignorance as well as its quantitative estimates of error."''' (Emphasis added) [15]
'''Given the necessity of acting in the face of enormous uncertainties [16], discipline taxonomies must offer a clearly coherent and probabilistic approach to uncertainty that is sufficiently general in nature, logically distinct and exhaustive in scope and "...provide decision-makers with a foundation for understanding the nature of discipline information..."''' [17]
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=== Scientific Risk Taxonomies ===
The [https://en.wikipedia.org/wiki/Scientific_method scientific method] is a body of techniques for investigating phenomena in its environment, acquiring new knowledge, or correcting and integrating previous knowledge. Inherent in that process are uncertainties of many forms. Any one taxonomy of [https://en.wikipedia.org/wiki/Uncertainty scientific uncertainty] has largely been focused on statistical models used to assess and quantify sampling, errors, and parameters, although several different taxonomies are conceivable: note-24 [18]
====Environment-specific, Phenomena oriented, model-centric taxonomy of uncertainties====
** [https://en.wikipedia.org/wiki/Uncertainty_quantification#Sources_of_uncertainty '''Parameter uncertainty'''] the source of which is model parameters that are inputs to the computer model (mathematical model) but whose exact values are unknown and cannot be controlled, or whose values cannot be exactly inferred by statistical methods. Subsets of this type of uncertainty include but are not limited to:
***'''Experimental uncertainty''' is also known as observation error, which comes from the variability of experimental measurements. An example is repeating an experiment measurement several times using exactly the same settings for all inputs/variables and recording the variability. It is a subset of the larger uncertainty component, parameter uncertainty.
*** '''Parametric variability''' comes from the variability of the input variables of the phenomena model and is also a subset of the larger uncertainty component, parameter uncertainty.
** '''Structural uncertainty''', or model inadequacy, model [https://en.wikipedia.org/wiki/bias bias] or "[https://en.wikipedia.org/wiki/Systemic_bias systemic bias]", or model discrepancy, which comes from the lack of knowledge of the underlying true state of the phenomenon or its environment. It depends on how accurately a mathematical model describes the true state of the phenomenon or what is termed "model fitness". Due to the inherent nature of uncertainty in any knowledge body (scientific or engineering), models are only an approximation to reality. Therefore, any conclusions drawn from the model can be very misleading when the underlying basis is not plausible or lacks validity. '''note-25 [19]]'''
*** What is missing in this context is any concept of output variability due to the model itself. Part of this is due to the lack of recognition of "process" in scientific investigation. The concept of the scientific method started with a single investigator, such as Galileo or Michael Faraday, working in their laboratories. It has grown into planet and solar system scale experiments such as data gathered on planetary flybys such as Mars, Pluto, and Ceres to the hunt for the Higgs Boson. Ultimately, science has become more like engineering in the scale and complexity of its investigations and theory aggregates, such as the "Unified theory" of particle physics. Subsets of this type of uncertainty include but are not limited to:
*** '''Algorithmic uncertainty''', or numerical uncertainty, comes from numerical errors and numerical approximations per implementation of the computer model. Most models are too complicated to solve exactly. For example, the [https://en.wikipedia.org/wiki/Finite_element_method finite element method] or [https://en.wikipedia.org/wiki/Finite_difference_method finite difference method] [may be used to approximate a solution [https://en.wikipedia.org/wiki/Partial_differential_equation partial differential equation], which introduces numerical errors. Other examples are numerical integration and infinite sum truncation, which are necessary approximations in numerical implementation.
*** '''Interpolation uncertainty''' comes from a lack of available data collected from computer model simulations or experimental measurements. For other input settings that don't have simulation data or experimental measurements, one must interpolate or extrapolate to predict the corresponding responses.
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====Commentary====
A more comprehensive taxonomy of uncertainties that acknowledges knowledge, data, and linguistic uncertainties is:
*'''Epistemic uncertainty''' -[https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Epistemic uncertainty] or systematic uncertainty, in contrast, reflects limitations in the current “state of knowledge” underlying models themselves, originates from competing theories or models, is not readily quantifiable, and is manifest by subjective confusion or indecision. It includes uncertainty due to measurement limitations, insufficient data, extrapolations and interpolations, and variability over time and space. Epistemic uncertainty is uncertainty about "...some determinate fact ... because of a lack of complete information. '''cite_note-26 [20]]''' Epistemic uncertainty can be classified into six main types:'''cite_note-27 [21]]'''
*''' Measurement error''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Observational_error Measurement error] is uncertainty that manifests itself as (apparently) random variation in the measurement of a quantity. Repeated measurements will vary and demonstrate classic statistical behavior.
* '''Systematic error''' - [https://en.wikipedia.org/wiki/Observational_error Systematic error] occurs due to bias in the measuring equipment, model, analysis, observation, or sampling procedure. It is formally defined as the difference between the true value of the quantity of interest and the value to which the mean of the results converges as sample or data sizes increase. Unlike measurement error, it is not (apparently) random, and therefore, results subject to systematic error alone do not vary about a true value. Systematic error can result from deliberate judgment to exclude (or include) data, parameters, or models that ought not to be excluded (or included).
** "The only way to deal with systematic error is to recognize a bias in the process, model, or procedure and remove it. Systematic error, however, is notoriously difficult to recognize except on theoretical grounds. Corrections may only be applied when the magnitude and direction of the bias are known. Such corrections underlie the application of double-sampling methods in environmental science." (Regan, et. al., op. cit.)
*'''Natural variation''' - [https://en.wikipedia.org/wiki/Natural_process_variation Natural variation] or underlying process variation occurs in systems that "...change (concerning time, space, or other variables) in ways that are difficult to predict...across the full range of temporal and spatial values (or other related variables)." '''note-28 [22]'''
** This form of uncertainty is reduceable utilizing tools such as [https://en.wikipedia.org/wiki/Statistical_process_control statistical process control] and replication of experiments or observations.
*'''Inherent randomness''' - [https://en.wikipedia.org/wiki/Randomness randomness] or [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty aleatory uncertainty] exists because the system is"...in principle, irreducible to a deterministic one (the most well-known case is described by [https://en.wikipedia.org/wiki/Uncertainty_principle Heisenberg’s uncertainty principle] in quantum mechanics)." '''note-29 [23], note-30 [Note 7]'''
** Even if the argument is accepted that it is unlikely that any given physical system at some level is inherently random, it is important for any taxonomy to distinguish between systems that appear random due to incomplete or inconsistent information and those that are intrinsically random. (Again, see Regan (2002)).
*'''Model uncertainty''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling Model uncertainty] occurs in the process of generating a model as a conceptual representation of a particular phenomenon to create a [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling#Overview simplified reflection of reality]. This selectivity of factors and variables creates uncertainty in at least three ways. (Regan (2002))
** '''Variables and processes''' that are regarded as relevant often represent a trade-off between system knowledge and assumed states and model objectives;
** '''Models''' depict observed processes using logical or mathematical constructs based on underlying theories about system states or dynamics using continuous equations to describe discrete processes.
** '''Curve fitting''' (including interpolation and extrapolation) with mathematical expressions using model variables where inputs are discrete data points.
** Regan (2002) argues that '''model uncertainty''' is "...notoriously difficult to quantify and impossible to eliminate ...(and)...(t)he only reliable way of determining how appropriate a model is for prediction is to perform validation studies."
** Subjective judgment occurs due to the interpretation of scarce or error-prone data.
*** The only way to address this type of uncertainty is to "...assign a degree of belief about an event in the form of a subjective probability." (Regan (2002))
*Linguistic uncertainty - Linguistic uncertainty, or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. '''note-31 [24]], note-32 [25]]'''
** '''Context dependence''' is uncertainty concerning the context in which a statement is to be understood;
** '''Ambiguity''' occurs when words have multiple meanings, and further reduction to a single meaning is not logically possible in a given context.
** '''Underspecificity''' occurs in the presence of "unwanted generality" or multiple interpretations, and reduction to a smaller set or even a single interpretation is not logically possible.
*Stochastic or statistical uncertainty - Stochastic or statistical uncertainty, which is sometimes known as [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Aleatoric uncertainty] as well as the subject of [https://en.wikipedia.org/wiki/Stochastic_control stochastic control] pertains to the parameters of a risk model, originates from sampling or measurement error, and can be quantified and mathematically expressed (e.g., using confidence intervals).
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== Uncertainty Taxonomies in a Legal Environment ==
From a legal perspective, a definition of taxonomy is dictionary-based, such as "the systematic distinguishing, ordering, and naming of type groups within a subject field." given by Walker (op. cit.). Such legal concepts of scientific or engineering taxonomies are not required to be epistemically complete. Still, they must cover "the most significant aspects of ...(current, good discipline practices) ... and of the descriptive information commonly encountered by decision-makers" in a logically distinct manner. (Walker, 1991, p. 571) These taxonomies must be sufficient to catalog the types of uncertainty associated with descriptive assertion, including the critical subset of cause and effect assertions. This legal/decision-maker framework has presented descriptive uncertainty as having six components (Walker, 1991). Although not part of the Walker taxonomy, underlying the scheme is linguistic uncertainty, or "vagueness," as a source of uncertainty, including uncertainties due to context dependence, ambiguity, and under-specificity. '''note-33 [26]]''' Arguably, these uncertainties underlie the most basic element in the taxonomy.
* '''Linguistic '''- or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. (Regan, 2002)
* '''Conceptual'''- the definition by choice and design of descriptive concepts or variables to be used as [https://en.wikipedia.org/wiki/Predicate_(grammar) predicates] where the subject of an assertion identifies what is being discussed and the predicate provides information about the topic or characterizations such as what the subject is, what the subject is doing, or what the subject is like (Wikipedia).
* "Conceptual uncertainty, or the potential for conceptual error, arises whenever predication occurs. Whenever a concept is used to describe something, using certain concepts instead of others begins to structure how we understand the object, event, or instance under discussion. Predication or conceptualization generates useful information about things, but it also can inhibit our ability to think about those things with concepts other than those selected. The concepts used may not be the most fruitful or the best designed- either for scientific purposes or for making wise, fair, effective, and efficient decisions." (Edited for reading, Walker, 1991)
* Similarly, choice and design of descriptive variables enhance and restrict data set membership, creating potentially incomplete, incompatible, or inconsistent data sets, "...the classification categories employed, and the relationships among those categories (nominal, ordinal or scalar)." '''note-34 [27]]'''
* Concept uncertainty '''note-35 [28]''' like systematic error discussed above is notoriously difficult to recognize, except on theoretical grounds, and the magnitude and direction of the bias are known.
Both terms could be analogized to that of [https://en.wikipedia.org/wiki/Accuracy_and_precision#Terminology_of_ISO_5725 accuracy] in [https://www.bipm.org/en/publications/guides/ ISO 5725] which looks at the ability of the concept or system's ability to produce results that are proximate to the "true" value or state of the phenomenon. Conceptual uncertainty and systemic bias are related to another measure of uncertainty: validity. Walker has defined validity as the ability of a concept to quantitatively describe and explain what the phenomenon objective "truly" is. Walker also ties the concepts together when he states, "Validity concerns the "accuracy" of the measurement data, not its precision." (Walker, 1998) 
Additionally, conceptual uncertainty and associated validity issues could be propagated throughout this taxonomy. Every component of action and decision in the face of uncertainty raises validity issues. Walker recognized this when the author defined epistemic choice for concepts used throughout the other uncertainty components or layers.
* '''Measurement''' uncertainty or Misclassification error- the application of the underlying concepts or variables to specific, individual cases and the uncertainty of the reliability (Walker, 1998) of the resultant data. (It is important to remember that assessment observation and measurement are used interchangeably in this analysis.) Thus, an assessment method or procedure is considered "reliable" or "precise" in the scientific terms of ISO 5715 if it repeatedly produces consistent results.
* Measurement uncertainty is logically distinct from conceptual uncertainty, which can only occur after any conceptual uncertainty has been established. Still, misclassification can occur even when the conceptual uncertainty has been minimized. Conversely, underlying errors or errors made in measurement or classification propagate thru the reasoning chain and reduce the quality or value of the final result.
* While uncertainties associated with quantitative measurements or assessments can be statistically evaluated and described, uncertainties related to qualitative assessments are more problematic.
**Working through the taxonomic chain from base concept to causal explanation requires amassing and integrating qualitative information and quantitative data into input parameters for mathematical models. Developing procedures for producing consistent, repeatable sets of information from assessments that minimize conceptual error or systemic bias requires professional efforts to "validate" such methods, models, and judgments at all layers in the process. Validation as a process is something that civil engineering as a practice needs to embrace more frequently.
* '''[https://en.wikipedia.org/wiki/Sampling Sampling]'''- in the classical statistics sense, is the selection of a limited subset of a larger population to use as a basis for making estimates about the population in the form of attributes, proprieties, or parameters. The resulting information would be determinative in making forecasts about future population sampling or possessing a desired amount of predictive power. The uncertainty about the ability of this information to correctly predict future samples is termed sampling error.
* '''Modeling'''- Modeling uncertainty arises whenever a claim is made that out of a class of candidate relationships and variable pairings, one variable pairing inclusive of constants is chosen that possesses a particular or persistent mathematical relationship to another variable X. Modeling errors arise in selecting the wrong variable pair and constants or by incorrectly specifying its constants. (Walker, 1991, pg. 599)
** '''Causal'''- In causal modeling and analysis, the relationship between causal variables is not strictly a mathematical function. Still, it makes testable predictions and explains "...how a system of variables works, why a system works the way it does, or why it makes sense to think of certain variables as a system" at all." (Walker, 1991, pg. 609)
** '''Epistemic'''- the choice of interpretations for fundamental, logical concepts used throughout the other components or layers.
The components of this uncertainty taxonomy can combine in different ways to "...produce different aggregate uncertainties." '''note-36 [29]'''
:''' Only the scientific and engineering professions possess the capacity to develop and validate procedures and models that combine the different kinds of qualitative '''note-37 [30]''' and quantitative information and their associated potential for error into an aggregate measure or "scalar variables" of uncertainty.'''note-38 [31]]
Scalar variables are quantitative variables whose categories are related by some measure of the relevant property's incremental frequency, degree, or amount. (Walker, 1991) Examples are Modulus of Elasticity, Compression stress in elastic materials, etc.

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=== Economics Uncertainty Taxonomies ===
Like scientific concepts of uncertainty, economics is environment-specific (in this case, read sector of economy or markets), phenomenon-oriented (in this case, read human activity and manufactured phenomena such as firms and markets guided by human judgment and decision), and model-centric.
:''' Economics is not interested in all forms of uncertainty, only specific, meaningful subsets possessing casual connections with material impacts, often labeled as 'risks'.'''
One of its earlier writers ([https://en.wikipedia.org/wiki/Frank_Knight#Life_and_career Knight], 1921, 1948, 1957) attempted to define "uncertainty" as the presence of "defects of managerial knowledge" (or [https://en.wikipedia.org/wiki/Knightian_uncertainty knightian uncertainty] and thus risk centric) as risk that is immeasurable, not possible to calculate.(Wikipedia) Risk in this sense was defined as "the ordinary risks of business activity which can ... be reduced... by applying the insurance principle." Knight does acknowledge that risk is a specific and unambiguous subset of uncertainty. (Risk, Uncertainty and Profit, 1957, pg. 19) For Knight, measuring uncertainty meant measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences, which Knight referred to as "the imperfection of knowledge". (Risk, Uncertainty and Profit, 1957, pg. 197) One of Knight's many contributions to this analysis was recognizing information and knowledge's role in economic activity.
:"''(W)e are concerned only to emphasize the fact that '''knowledge is in a sense variable in degree and that the practical problem may relate to the degree of knowledge rather than to its presence or absence in toto.''' We live only by knowing something about the future, while the problems of life, or conduct at least, arise from knowing so little. This is as true of business as of other spheres of activity. The situation's essence is action according to opinion, of greater or less foundation and value, neither entire ignorance nor complete and perfect information, but partial knowledge. If we are to understand the workings of the economic system, we must examine the meaning and significance of uncertainty; and to this end, some inquiry into the nature and function of knowledge itself is necessary.''" (Knight, pg. 199) Emphasis added
Knight also reinforced the difficulties in classifying uncertainties when he analyzed life insurance. In this case, accidental death was an uncertain phenomenon compared to sickness and accident, where an "...objective description and classification of cases was impossible..." (Knight, pg. 248) Another factor that made uncertainties ineligible as risks and, therefore, uninsurable because they were unclassifiable was the exercise of judgment in making decisions by the businessman. (Knight, pg. 251)
This is a simple classification of economic uncertainty and, as an idealization, is philosophically controversial. '''note-39 [32]'''
* Uncertainty and information about the economic environment are distinct from uncertainty about others’ behavior or choices.
* Risk as a specific subset of uncertainty implies that "...all possible acts are known, all possible outcomes arising from each act are known, and it is possible to assign probabilities to each act." '''note-40 [33]'''
Like the economic [https://en.wikipedia.org/wiki/Theory_of_the_firm theory of the firm], an economic theory of uncertainty seeks to explain and predict the nature of a specific subset of uncertainty (in this case, risk relating to physical phenomena and human activity), including its behavior, structure, and relationship to the universal uncertainty of physical phenomena. In this sense, uncertainty, like firms and markets, can establish price equilibriums under the right circumstances different from those that might have occurred in either of the other two. Such a theory of uncertainty offers a partitioning scheme that decomposes uncertainty into distinct and meaningful subsets, of which risk is a significant one. It also explains how the presence and variability of knowledge cause or " drive" economic consequences. Unlike physical phenomena, economic activity creates artificial objects such as products, firms, mediums of exchange, markets, economies, and knowledge about those activities. The economics of uncertainty exists as an alternative to "certainty" models that assume away imperfect knowledge and unknown choice/judgment preferences when it is more efficient for economic decision-making. This also allows the introduction of the logical concept of "hazard" versus "risk". Frank used hazard extensively in Risk, Uncertainty, and Profit, but not to the extent that one could say it was interchangeable. An example of this is in the term "[https://en.wikipedia.org/wiki/Moral_hazard Moral hazard]," where a firm could engage in "riskier" and potentially "uninsurable" behavior if the information were transparent, and, therefore, insurance coverage of losses is limited. note-41 [34]], note-42 [35]]

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=== Uncertainty and Risk in Policy and Regulatory Environment ===
:''"Hume's 'just reasoner', when faced with a difficult public policy decision in current times, would probably commission a risk analysis. But how could they incorporate 'a degree of doubt, caution, and modesty?"'' '''note-43 [36]'''
The problem of "decision-making in the face of uncertainty" is how to regulate based on incomplete information that has the potential to be materially inaccurate. A contextual assumption is that we need to evaluate decision rules for dealing with uncertainty. As a social enterprise, risk regulation, whatever its substantive objectives, should be as '''effective, efficient, and equitable''' as practically possible. These "three E's" form a set of "process objectives" or "meta-goals." From the uncertainty standpoint, causal information can be usefully divided into two major categories: information about groups and information about individuals.

Each category, group, and individual has its distinctive types of inherent uncertainty. These are logically distinct, generally independent, and cumulative, contributing to '''(?????)'''

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=== Engineering Uncertainty Taxonomies ===
Like scientific and economic concepts of uncertainty, engineering is environment-specific, phenomena-oriented, model-centric, external choice, and driven by decision-making in the face of uncertainty. Like economics and unlike science, engineering is not interested in all forms of uncertainty, only those with determinable and material consequences. Like economics, engineering conceives risk as a specific and unambiguous subset of uncertainty determined by measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences. Also, engineering recognizes the role information and, by extension, knowledge plays in constructing the built environment.
'''note-44 [37]]''' Like economics, engineering differs from economics and scientific uncertainty in its interest in behavioral uncertainty.
*"Behavioral or interaction uncertainty is how individuals or organizations act or interact. Behavioral uncertainty arises from four sources: design uncertainty, requirement uncertainty, volitional uncertainty, and human errors." '''note-45 [38]]'''
** A design uncertainty is a choice among alternatives over which an individual or group of individuals exercises direct control but has not yet decided upon.
** Requirement uncertainty includes parameters of interest to and determined by the stakeholder, independent of the engineer or designer.
** Volitional uncertainty is uncertainty about what the subject him/herself will decide. Other people’s future actions and conduct are not entirely predictable, particularly when dealing with other organizations.
** Human errors occur during the development of a system or project due to blunders or mistakes by an individual or individuals.

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==Practice Frameworks for Uncertainty and Risk in Civil Engineering Practice==
===PMIBoK===
PMI does not define uncertainty, but the PMI Body of Knowledge uses the term in 45 places (uncertainty) and as describing properties in 8 places (uncertainties). [See Note 8] PMI does define "risk" and uses the term over 1,300 times in the same document. In its glossary, PMI also defines terms such as "threat" and "opportunity" but not events.[391 The BoK contextually defines uncertainty when it states that project risk has its origins in the uncertainty present in all projects. (Op. Cit., pg 309) 
'''Uncertainty''' is contextually defined and taken as having the property of affecting project execution and the ability to meet stakeholder expectations. Uncertainty is more than the sum of all individual risks (known and unknown). The nature of this uncertainty is not defined, only its capacity to affect something else or its properties. 
'''Risk''' is defined as " ... an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives." (Ibid.) Beyond this formal definition, PMI adds context when, in its risk management section (Sec. 11 ), the BoK states that risk is a caused event or condition with multiple causes and impacts. The BoK even offers specific techniques for mapping cause-and-effect relationships and influence diagrams. (Sec. 11.2.2.5) Not as evident but equally important is the recognition that not only is risk caused but the impact is "triggered". The risk trigger is an event or situation that signals that it is about to occur (Glossary, pg. 566). Risk conditions are factors that affect or otherwise contribute to risk, such as project stakeholders. Risk implicitly retains some residual element of probability in it, or a value of less than 1.0 with risk that approaches a level of near certainty is termed "issues" or "realized risk" .(Op. Cit., pg 309). PMI also links risk to underlying variations in project outcomes. (Ibid.) Risk is based upon data and information which can be assessed as to its quality (Sec. 11.3.2.3). This analysis looks to determine the value of the information and examines the degree to which the risk information is understood as well as its " ... accuracy, quality, reliability and integrity ... " (Ibid.) Part of that is understanding the relationship between occurrence and impact as outlined in PMl's Figure 11-10 Risk Impact Matrix and developing a model for prioritizing risk products and setting the threshold for action.
====Commentary:====
For defining and identifying risk, PMI notes in Sec. 11.2.2.4 that " ... Every project and its plan is conceived and developed based on a set of hypotheses, scenarios, or assumptions." Part of the risk analysis process is identifying risks to the project, such as inaccuracy, instability, inconsistency, or incompleteness of assumptions. Similarly, the quality of management plans, as well as their consistency with others and the project objectives and assumptions are 11 •• .indicators of risk in the project." (Sec. 11.2.2.1) 
Similarly, the role of engineering economics in risk is embedded or implied in the PMI definition of risk when the BoK discusses risk identification as a process of assessing which risks out of the total risk exposure for the project "may affect" the project and distilling their characteristics. Embedded in this statement is the concept that not all risks can cause economic impacts (positive and negative) to the project. Also embedded is the requirement to understand risk characteristics to identify project risk (Cost and schedule). Lastly, risk identification is an iterative process of incrementing project risk documentation, much like project scope, cost, and schedule documentation. 
Lastly, PMI acknowledges the complex constitution of risk aggregates or the "sum" of all risks in its material, but is it a sum? Risk information has descriptive and explanatory content. These are knowledge and meta-knowledge components. Understanding project assumptions is project-specific knowledge, but identifying risks from assumption inaccuracy, instability, inconsistency, or incompleteness requires professional or program meta-knowledge. 
'''Uncertainty exists in all projects, but risk, a subset of uncertainty with material economic impacts, is a key meta-knowledge element for civil engineering practice.'''
===Software Development Process- Risk Focused===
The Unified Process requires the project team to focus on addressing the most critical risks early in the project life
cycle. The deliverables of each iteration, especially in the Elaboration phase, must be selected to ensure that the greatest risks are addressed first. <ref> Booch, Grady, Ivar Jacobson, and James Rumbaugh. "The unified software development process."
Reading: Addison Wesley (1999).</ref>
===American Society of Civil Engineers (ASCE)===
ASCE does not define uncertainty, but the ASCE CE Body of Knowledge (CEBoK) uses the term in 22 places (uncertainty) and describes properties in 13 places (uncertainties). 
The CEBoK does not use the phrase "uncertainty and risk" as PMI and AACE do in their publication. ASCE reverses the two (Risk and uncertainty) in twelve places regarding technical outcomes (Outcome 12). A fundamental difference between PMI and ISO is that risk is considered a subset of uncertainty. ASCE does not define "risk" but uses the term 25 times in the same document. Almost all are in the context of variation of design parameters and not in the classical context of cost, schedule uncertainty, and risk.
====Commentary:====
...
=== American Association of Cost Engineers (AACE) ===
AACE defined uncertainty, risk, and other related terms in its Risk Management Dictionary. <ref>Risk Management Committee uRisk Management Dictionary, Cost Engineering Vol. 37/No. 10, OCTOBER 1995. </ref>
AACE's overall strategy was to define risk and other terms that stem from base uncertainty. AACE first defined uncertainty as .... "(t)he total range of events that may happen and produce risks (including both threats and opportunities) affecting a project (see opportunities, events, conditions, risk, and threats" where the following sub-definitions apply:
** Biases--A lack of objectivity based on the individual's position or perspective. Systematic and predictable relationships between a person's opinion or statement and his/her underlying knowledge or circumstances. Note: there may be "system biases" as well as "individual biases."
** Condition (Uncertain Condition)-Any specific identifiable circumstance (such as the rate of inflation or the quality of labor available) that might affect the outcome of the project
** Event (Uncertain Condition)-is a specific identifiable action (such as a large government project being started in the same labor area as your project) or an act of nature that might happen and that (if it does happen) could affect the outcome of the project.
** Opportunities are Uncertain events that could improve the results or improve the probability that the desired outcome will happen
** Threats are Uncertain events that are potentially negative or reduce the probability that the desired outcome will happen.
*** AACE defines Risk as an "ambiguous term" (sic) that is synonymous with uncertainty or a negative subset such as "threats", or an overall negative impact of all possible uncertainties.
====Commentary:====
...
==Limitations of the definition==
...
==Working definition==
...
==Beneficial outcomes==
...
==See also==
Wiki articles on the project.

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==Notes==
<references group="note" />
<li> Implicit in this definition of uncertainty as a "state of' is ... </li>
: " ... a conceptualization of uncertainty as a subjective, cognitive experience of people--a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one's lack of knowledge, without which one could not feel uncertain; '''uncertainty is a form of "meta-cognition" ... ( or alternatively, its main component, meta-knowledge) ... -a knowing about knowing.'''" (Han, 2011, op. cit., Emphasis added)
</ol>
<ol start="5">
<li> Cox wanted his system to satisfy the following conditions:</li>
: Divisibility and comparability- The plausibility of a statement is a real number and depends on the information we have related to the statement.
: Common sense - Plausibilities should vary sensibly with the assessment of plausibilities in the model.
: Consistency - If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). -preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf
</ol>
<ol start="6">
<li> The utility of existing taxonomies of uncertainty in civil engineering and, by extension, "risk" has been unsatisfactory for this very reason. This is particularly true in cases where the source of the uncertainty is conflicting versus incomplete information. See Regan, et. al.,"A taxonomy and treatment of uncertainty for ecology and conservation biology" (2002)
for a survey of scientific taxonomies for uncertainty.</li>
<li> Regan et al. argue that " ... genuine examples of this kind of uncertainty are hard to find. Even classic cases of random experiments like coin tosses and the throwing of dice are deterministic; it is just that we do not have enough information about the dynamic processes and initial conditions to make any sensible estimates about the outcomes. Such processes are, for all intents and purposes, inherently random, but they are not genuinely inherently random. For similar reasons, complex systems such as ecosystems and weather patterns are unlikely to be inherently random. Similarly, chaotic systems are entirely deterministic. They are unpredictable because the deterministic processes generating them and the relevant initial conditions are hard
to fully specify (see Stewart 1989, Sugihara et al. 1990)." (Regan (2002) </li>
<li> The CEBoK uses the phrase "uncertainty and risk" twice in reference to scheduling (Section 6.5,2) and cost estimating (Section 7.2,2) as well as reversing the two (Risk and Uncertainty) also in two places in discussing project life cycle (both in Section 2.4.1) </li>
</ol>

==References==
<references />

Uncertainty and Risk in Civil Engineering Practice

2024-11-24T16:44:18Z

WikiSysop: /* Probability and Uncertainty */

'''Life is short and the art long; the occasion instant, experiment perilous, decision difficult.''' (Hippocrates as quoted in Fox,)
:''See also [[Project Definition]] , [[Project Life Cycle and Phase Models]] , [[Project Delivery Methods]]''
:''See also [[Technical Outcomes for Civil Engineering:Risk and Uncertainty]]''
==Basic Considerations==
As human beings,'' ... being alive means seeking opportunities and taking risks.'' As knowledge professionals
living in the 21st century, this means coping with an increasingly complex number of uncertainties for humans
living in this environment. We seek to understand better how these uncertainties can be characterized and
managed. The essence of this article and the experience for engineers in general and civil engineers in particular is
that manageable uncertainty is, by definition, termed risk, and its kindred cousin is termed hazard. This causes us to experience the
:" ''... human dread of and fascination for risk and the increasingly important role of risk analysis within societies ...'' " <ref name="McDaniels"> McDaniels, Timothy, and Mitchell Small. Risk analysis and society: an interdisciplinary characterization of the field. Cambridge University Press, 2004, page 1 </ref> (Ibid.)
and, by extension, civil engineering. McDaniels et al. argue that risk management has been
fundamental to our social and governance development for the past 10,000 years. The scale and shape of the
uncertainties faced in this period shaped the societies that have developed today. The central thrust of this effort
over the centuries has been to reshape and re-frame our understanding and conception of uncertainty from one of
complete unknowing and simple acceptance as our fate in life to one of management (cf "''Against the Gods''"
concept in Bernstein's book <ref name = Bernstein">Bernstein, Peter L., and Jesse Boggs. Against the gods. Simon & Schuster 1997.</ref>.
: ''All the knowledge professions and disciplines have struggled with managing uncertainty, for it is impossible to manage unbounded uncertainty.'' <ref group="note">See McDaniels et al. 2004 for an extensive discussion and bibliography on the historical development of Risk Analysis and some key milestones in risk analysis in the 20th century.</ref>
As outlined below, professions such as civil engineering have successfully acted in the face of uncertainty. It lies in the profession's ability to reduce a wide variety of uncertainties into increasingly smaller and crucially bounded subsets that can be managed. These are called 'risks. More recently, this process has evolved, and individual disciplines such as Civil Engineering (CE) have developed their knowledge and models to perform risk analysis. Risk is also taught as a distinct discipline and specialty practice within civil engineering. Still, it has some unique features that set it apart from other more classical practices within CE. As such, it is one of the first of some very specialized CE practices that use knowledge about civil engineering knowledge, or meta-knowledge. Other examples of civil engineering metaknowledge are project controls and quality controls.
==Semantic, Epistemic and Logical frameworks==
The semantic, epistemic, and logical frameworks for uncertainty and risk have several dimensions and layers of logical frameworks. The semantics problems include interchangeable usages for risk and uncertainty and hazard, uncertain and imperfect, and a lack of definitional material or context. The semantical scheme for this article will be to proceed from the distinction between certainty and uncertainty through marginal refinements and reductions of uncertainty up to the point of causal uncertainties. Beyond this point of semantics will be the logic frameworks or the subject of taxonomies of professional knowledge. First, it is about simple, testable phenomena, and then, it moves on to complex taxonomy schemes for artificially constructed phenomena or engineering projects.
<ref group="note">TBD</ref>
==Semantics framework==
===Certainty and Uncertainty===
''''' There is no such thing as absolute certainty, but there is assurance sufficient for the purposes of human life.''''' ([https://en.wikipedia.org/wiki/John_Stuart_Mill John Stuart Mill]) 
'''''If you tried to doubt everything, you would not get as far as doubting anything. The game of doubting itself presupposes certainty.''''' ([https://en.wikipedia.org/wiki/Ludwig_Wittgenstein Wittgenstein] # 115 from On [https://en.wikipedia.org/wiki/On_Certainty Certainty]) 
It is important to note that the references to epistemic or epidemiological knowledge in this article are assumed to relate to three forms of knowledge, namely:
* Knowledge that ( [https://en.wikipedia.org/wiki/Descriptive_knowledge descriptive or declarative or propositional knowledge])
* Knowledge how ( or "[https://en.wikipedia.org/wiki/Procedural_knowledge know-how]"), and
* [https://en.wikipedia.org/wiki/Knowledge_by_acquaintance Knowledge by acquaintance].
'''Certainty''' has been defined as "an epistemic property" of knowledge in all its forms and the state of our beliefs about that knowledge.
<ref>Certainty, Stanford Encyclopedia of Philosophy, accessed on September 12, 2015, at http://plato.stanford.edu/entries/certainty/#ConCer </ref>
Certainty about any belief about knowledge implies that it is not subject to doubt or skepticism. This immunity to criticism can be dogmatically based, emphasizing the importance of a propositional-based sense of truth over experiential, sensory perceptions. <ref>Definition of [https://en.wikipedia.org/wiki/Dogmatic_theology Dogmatic theology or belief], accessed at Wikipedia</ref>
: '''The demarcation line between dogmatic and non-dogmatic beliefs lies in the presence and recognition of specific criteria and information that would make the believer change their beliefs.'''
An empirical framework of [https://en.wikipedia.org/wiki/Testability testability] and [https://en.wikipedia.org/wiki/Falsifiability falsification] is required to recognize this and limit dogmatism. For example, one could argue that people would change their minds if God asked them to. Similarly, one could construct a concept of epistemic as opposed to dogmatic belief certainty as the ability to know anything that one chooses to know and can be known or [https://en.wikipedia.org/wiki/Omniscience inherent omniscience].

A second kind of certainty is epistemic, when conviction reflects the highest possible support for a belief. In this sense, knowledge is separate from beliefs, although someone may have beliefs about a property, such as certainty of knowledge. Logically, it has been shown that there will be unprovable statements within the system for any such knowledge system. Secondly, the knowledge system cannot demonstrate its own consistency. ([https://en.wikipedia.org/wiki/Gödel%27s_incompleteness_theorems Gödel's incompleteness theorems])
: '''Certainty, in real life, is useless or often damaging (the idea is that "total security from error" is impossible in practice, and a complete "lack of doubt" is undesirable)''' ([[https://en.wikipedia.org/wiki/Certainty Physicist Carlo Rovelli]])
'''Uncertainty''', on the other hand, arises immediately in the slightest amount of doubt or criticism.
<ref group="note"> The Merriam-Webster dictionary defines uncertainty as “the quality or state of being uncertain,” which is something of a circular definition. Likewise, one could define uncertainty as the state of not being certain.

Synonyms are distrust, doubt, misgiving, mistrust, reservation, skepticism, and suspicion. Another writer added indefinite, indeterminate, not certain to occur, problematical, unreliable, untrustworthy, unknown beyond doubt, dubious, doubtful, not clearly identified or defined, not constant, variable, and fitful to the list.
:(Han, Paul KJ, William MP Klein, and Neeraj K. Arora. "Varieties of Uncertainty in Health Care: A Conceptual Taxonomy." Medical Decision Making 31.6 (2011): 828-838.)
Han et al. noted that any definition of uncertainty clearly encompasses "...numerous types, sources, and manifestations of uncertainty, and ...(any) ...useful working definition of uncertainty needs to specify the concept underlying these varied meanings of the term."</ref>
Implicit in this definition of uncertainty as a “state of” is ...
"...a conceptualization of uncertainty as a subjective, cognitive experience of people—a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one’s lack of knowledge, without which one could not feel uncertain; uncertainty is a form of “meta-cognition” ...(or its main component, meta-knowledge)... —a knowing about knowing." (Han, 2011, op. cit., Emphasis added)
Similarly, uncertainty could be defined as "...any departure from the unachievable goal of complete [https://en.wikipedia.org/wiki/Determinism determinism]."
<ref name="Walker1">Walker, W. E., Harremoës, P., Rotmans, J., Van Der Sluijs, J. P., Van Asselt, M. B., Janssen, P., & Krayer von Krauss, M. P. (2003). Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support. Integrated assessment, 4(1), 5-17.</ref>
Reasoning under uncertainty is very different than performing the same under certainty. In reasoning under certainty, one has complete knowledge and deduces without doubt and equally important, without limitation, thereby concluding free from error. Reasoning under uncertainty, one works in a state of incomplete, inconsistent, and limited knowledge; doubts cloud any statements or assertions and thereby taint any deductions/inferences resulting in the potential for error. <ref group="note">Implicit in this definition of uncertainty as a “state of” is ...
"...a conceptualization of uncertainty as a subjective, cognitive experience of people—a state of mind rather than a feature of the objective world. The defining feature of this state, furthermore, appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one’s lack of knowledge, without which one could not feel uncertain; uncertainty is a form of “meta-cognition” ...(or alternatively, its main component, meta-knowledge)... —a knowing about knowing." (Han, 2011, op. cit., Emphasis added)</ref>
:'''Knowledge professions reason in a state of incomplete, inconsistent and limited knowledge with doubts that cloud any statements or assertions and taint any deductions/inferences resulting in the potential for error.'''
:''' It is also important to note the semantical and logical correlation between 'reasoning under uncertainty' or 'acting in the face of uncertainty' with the 'potential for' or 'presence of' error. The presence of uncertainty is invariably linked to the potential presence of error.''' The [https://en.wikipedia.org/wiki/Contingency_(philosophy) contingent] nature of uncertainty logically implies the contingent nature of error.
:'''Knowledge Professions use error as a proxy for uncertainty such that within discipline knowledge frameworks, uncertainty can be managed.''' The rationale for this belief is that error can be reliably described, quantified, explained, and ultimately reduced in ways that uncertainty cannot.

'''Uncertainty''' implies a range of variation that is impossible in certainty. Concepts and definitions of uncertainty are unbounded and vast but start from the point of complete and total ignorance. In describing uncertainty, statements can range from complete ignorance to relatively high degrees of belief that there is less potential for error in our reasoning or action. The latter is based upon confidence in justified knowledge, favorable past experience, and reliable parameters/models. One can't make that statement about ranges in certainty. If certainty, by definition, precludes doubt of any type or nature, then how could that be graduated to any degree? 
Uncertainty, on the other hand, can be reduced through disciplined efforts to acquire knowledge. In short, we can reduce or even manage uncertainty (to a degree); how can certainty be improved? Are there higher degrees of perfection? The answer is no. We reason from an uncertain starting point. Yet this effort presumes, as Mill and Wittgenstein postulated, that we can develop or acquire an increasing degree of belief or conviction that there is less potential for error in our reasoning or action. This position assumes that some way exists to identify what a lesser degree of uncertainty would look like.
:'''Knowledge professions acquire an increasing belief or conviction that there is less potential for error in their reasoning or action based on justified knowledge, favorable past experience, and reliable parameters/models.'''
Uncertainty is, in some form, amenable to description and explanation through observation and analysis. It is explainable and predictable to such a degree that it interests professionals such as scientists and engineers. Professional knowledge of this type has explanatory or predictive power, albeit limited in scope and application to relevant subjects such as physical objects or phenomena and functionality.
:'''The very act of reducing the scope of uncertainty to a limited set of phenomena implies choice and, ultimately, the decision to act in the face of such uncertainty, but acting or judging in this manner adds more value than doing the same under relative ignorance.'''
'''Uncertainty''', as described above, implies a hybrid nature. Examples of this are economic or decision-theoretic applications. In economics, [https://en.wikipedia.org/wiki/Perfect_information perfect information] allows one within the limiting framework of perfect competition in economic games to make decisions with 'perfect knowledge.' The crucial difference here is the distinction between '[https://en.wikipedia.org/wiki/Perfection perfection],' broadly, a state of completeness and lawlessness, and 'perfect, which can be thought of as an analytical limit that is approachable but can never be attained. Arbitrarily delimiting the realm of knowledge into defined, finite boundaries allows the simulated production of perfect choice, assumed crucially, to be free from error. The hybrid nature of this form of 'simulated' certainty produced within boundaries defined by assumptions and, therefore, clouded by doubt is itself a form of uncertainty. The importance of this is the semantical and logical association that exists between certainty and perfection versus the hybrid concept of a perfect anything, whether knowledge, infraction, competition, etc., or in the case of engineering, elastic, permeable, conductive, etc., being contained within an admittedly imperfect matrix of uncertainty.
:'''Simply put, a finite, heavily bounded piece of uncertain knowledge can be improved through the simulated use of [https://www.merriam-webster.com/dictionary/assumed assumedly] perfect information, but the perfection of knowledge cannot be attained or simulated.'''
Engineering and Economics, for example, simulate finite elements of perfect knowledge or information, heavily bounded with assumptions and a range of applications. By doing so, these professions reduce the bounds and variability of uncertainty and thereby manage it. Using these hybrid models to acquire knowledge takes place in an economy (regulated or institutional) and environment (transparent and structured), which puts pressure on the various professions to recognize and use qualitative and quantitative knowledge better to reduce uncertainties in their activities. The challenges in this reflect the nature of the profession's or discipline's knowledge (e.g., scientific, engineering, or medical), the policies and structure of the relevant professional knowledge bodies, process objectives, constraints, and "the elusive demands of politics." <ref>Walker, Vern R. "The siren songs of science: toward a taxonomy of scientific uncertainty for decisionmakers." Conn. L. Rev. 23 (1990): 567.</ref>
One common theme runs through all of these efforts to address the presence of uncertainty in professional activities. 
:"The available scientific information upon which a decision must be made is almost always a mixture of engineering knowledge and uncertainty. Regardless of the information or methodology, there is the potential for error. This is true regardless of the relevant science, whether archaeology or aeronautics, economics or engineering, pharmacology or toxicology or epidemiology. Achieving the best social decisions requires not only understanding and using what we know but also appreciating and weighing the extent of our uncertainty. In making the best use of ... information in ... decision-making, it is still true that the beginning of wisdom is knowing what it is we do not know." (Vern, Op. cit., Reformatted and engineering substituted for scientific) 
'''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty to develop explanations, calculate, and make predictions. This professional knowledge converges on but never reaches certainty, producing a "legitimacy" from the fruitfulness of its use.''' <ref>Randomness Is Unpredictability, Antony Eagle, The British Journal for the Philosophy of Science, Vol. 56, No. 4 (Dec., 2005), pp. 749-790 </ref> 
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=== Bias and Uncertainty ===
Up to this point, the concept of error, as outlined in uncertainty, could easily be simulated by a truly random variable. There should be no detectable differences around the unseen or specified statistical mean. However, several instances in experience point to a tendency towards one extreme or a pattern of error, a collective that is in itself an error of errors, namely [https://en.wikipedia.org/wiki/Bias bias].

Arguably, this could be part of the choice uncertainty discussion below, but it makes more sense to be looked at on its own. Acting in the face of uncertainty is an exercise of personal knowledge or experience. Whether using professional knowledge or individual experience, the potential is there to make a series of choices that, in some instances, make errors more likely than they would be if considered in the context of a statistical analysis.

This tendency for an individual to make the same error in a predictable or repeatable manner is termed bias. Personal experience, beliefs, knowledge, data, and models all have inherent built-in factors, errors, or defects that create predictable error patterns when applied in decision-making. Walker (1998) argues implicitly that such bias can also be considered systemic error. This type of uncertainty is difficult to identify except on theoretical grounds when the magnitude and direction of the bias are known. Most of these biases, whether systemic or knowledge-based, individual or practice-based, require disciplined efforts to "validate" such methods, models, and judgments at all layers while bounding the uncertainty.
: This is the challenge to professional knowledge that must be overcome, namely, developing and validating models and methods for managing bounded uncertainty that minimize this tendency towards repeatable groups of error or bias. Therefore, from this point forward, the discussion will refer to 'uncertainty and its proxies, error, and bias.'
===Propagating Error and Uncertainty===
Partitioning uncertainty and its proxies, error, and bias into logically distinct subsets involves or is associated with multiple-step execution or choice frameworks. Combining those measurements or choices into single parameters or choices involves, in some cases, assembling the data/information in a unique or limited range of sequences. This implies that associated errors in observing, calculating, or choosing will have unequal impacts or consequences depending on where in the chain the error occurs. Also implied in this is the possibility that subsequent errors will be causally linked to some degree. (In statistical practice, this depends upon whether the errors are independent of each other or correlated, specifically, co-variant.)
:'''Professions develop knowledge of how uncertainty and its proxies, error, and bias propagate through an ordered and normative series of observations or choices. This allows the profession to prioritize error and bias reduction to achieve an optimized reduction of uncertainty.'''
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===Cognition, Metacognition and Uncertainty===
The concept of choice and the resultant error in uncertainty can be further delimited depending on the role of [https://en.wikipedia.org/wiki/Cognition cognition], [https://en.wikipedia.org/wiki/Metacognition meta-cognition], and independent external elements. The rationale for this argument is that such choice activity in the face of uncertainty is a conscious, cognitive act.

An argument could be made that such cognition is a prerequisite for exercising choice and introducing error, as presented and discussed above. The counterfactual to this argument is the example of uninformed choice. Choices introduce as much as errors or more as informed choices could be made. (???) Choice, in this sense, is indifferent to knowledge. Purposeful choice in an environment of objectives requires knowledge, method, and experience, but uninformed, speculative choice does not.
:"''' Cognition''' is the set of all mental abilities and processes related to knowledge, attention, memory and working memory, judgment and evaluation, reasoning and "computation," problem-solving and decision making, comprehension and production of language, etc. Human cognition is conscious and unconscious, concrete or abstract, as well as intuitive (like knowledge of a language) and conceptual (like a model of a language). Cognitive processes use existing knowledge and generate new knowledge." (Wikipedia)
:"'''Metacognition''' is "cognition about cognition", or "knowing about knowing" and can take many forms. It includes knowledge about when and how to use particular strategies for learning or problem-solving. There are generally two aspects of [https://en.wikipedia.org/wiki/Metacognition metacognition]: knowledge about cognition and regulation of cognition."
Some types of metacognition knowledge are:
:'''Person knowledge''' (declarative knowledge), which is understanding one's own capabilities.
:'''Task knowledge''' (procedural knowledge), which is how one perceives the difficulty of a task, which is the content, length, and type of assignment.
:'''Strategic knowledge''' (conditional knowledge) is one's capability to use strategies to learn information. (Accessed at Wikipedia)
Like metacognitive knowledge, metacognitive regulation or "cognitive control" contains three essential skills.
:'''Planning:''' refers to the appropriate selection of strategies and the correct allocation of resources that affect task performance.
:'''Monitoring:''' refers to one's awareness of comprehension and task performance.
:'''Evaluating:''' refers to appraising the final product of a task and the efficiency at which the task was performed. This can include re-evaluating strategies that were used. (Accessed at Wikipedia)
Nothing has been said up to this point that limits this structure to an individual, or a collective, or two entities making cognitive choices that are incompatible or inconsistent to varying degrees. Interpretations of this scheme include an individual professional exercising judgment in a decision that, in turn, relies upon the collective judgment and decision of the discipline as an underlying basis versus the decision of another individual to oppose/protest or otherwise contest that decision. Thus, the framework of cognitive/meta-cognitive applies to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, economics of regulatory practice, and game theory.
:By solving useful problems in the face of inherent uncertainty, discipline-specific frameworks of cognitive/meta-cognitive activity apply to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, the economics of regulatory practice, and game theory.
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===Probability and Uncertainty===
Probability is a [https://en.wikipedia.org/wiki/Coherence_theory_of_truth coherent] approach to uncertainty in the physical and mathematics or "classical" domains such as engineering. <ref>See Colyvan for a critique of this claim, Colyvan Mark. "Is probability the only coherent approach to uncertainty?." Risk Analysis 28.3 (2008): 645-652.</ref> and is the "...is certainly the best-known and most widely used formalism for quantifying uncertainty.<ref>Morgan, Millett Granger, Max Henrion, and Mitchell Small. Uncertainty: a guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge University Press, 1992.</ref> [https://en.wikipedia.org/wiki/Cox%27s_theorem Cox’s theorem] ("Any measure of belief is [https://en.wikipedia.org/wiki/Isomorphism isomorphic] (but not necessarily equal) to a probability measure") is a well-known argument for the validity of that argument.
<ref group="note">Cox wanted his system to satisfy the following conditions:
Divisibility and comparability – The plausibility of a statement is a real number and is dependent on the information we have related to the statement.
Common sense – Plausibilities should vary sensibly with the assessment of plausibilities in the model.
Consistency – If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). — preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf</ref>

Another writer, [https://en.wikipedia.org/wiki/Frank_Knight Knight] (1921,1956), presented the following taxonomy of probabilities: [10]
: '''Classical 'a priori' probability:''' As an idealized model, numerical probabilities are computed based on generalized principles of assigning equal likelihoods and mutually exhaustive possible outcomes (Runde, 1998) to all set members. Such probabilities are assigned to "...outcomes based on a judgment of indifference between those outcomes, that is, based on the absence of any evidence of real influences in play that may render any one outcome more or less probable than any other." (Runde, op. cit.) This builds upon Knights's concept of an 'absolutely homogeneous classification of instances that are completely identical except for [https://en.wikipedia.org/wiki/Indeterminate_(variable) indeterminate] factors. This judgment of probability or logical probability is on the same logical plane as the propositions of mathematics and ultimately [https://en.wikipedia.org/wiki/Inductivism inductions] from experience.'(Knight, 1956, pg.225)
: '''Bayesian 'a priori' probability:''' In contrast to interpreting probability as the "frequency" or "propensity" of some phenomenon, [https://en.wikipedia.org/wiki/Bayesian_probability Bayesian probability] is a quantity that we assign to represent a state of knowledge or a state of belief. In this view, probability is assigned to a hypothesis, often using the basis of past or prior experience. In contrast, under the frequentist view, a hypothesis is typically tested without being assigned a probability based on prior experience. The Bayesian interpretation of probability can be seen as an extension of propositional logic that enables reasoning with hypotheses, i.e., propositions whose truth or falsity is uncertain. Bayesian probability belongs to the category of evidential probabilities; to evaluate the probability of a hypothesis, the Bayesian probabilist specifies some prior probability, which is then updated in the light of new, relevant data (evidence). The Bayesian interpretation provides a standard set of procedures and formulae to perform this calculation. [11]
: '''Statistical or 'a posterior probability:''' Empirical evaluation of the frequency of association between predicates, not analyzable into varying combinations of equally probable or logical, 'a priori' alternatives. Knight argued that any "high degree of confidence" that the judgment that experience will remain valid for future predictions is "...still based on an 'a priori" judgment of indeterminateness." (Knight, Op. Cit.) Knight's argument to support this is what will be discussed further below that first, "...the impossibility of eliminating all factors not really indeterminate; and, second, the impossibility of enumerating the equally probable alternatives involved and determining their mode of combination so as to evaluate the probability by a priori calculation." (Knight, Op. Cit.) Knight noted that the main difference between this form and that of logical or inductive probability was the presence of empirical information, which allowed the analyst to identify propensity and direction in the observed phenomena. In this case, 'a priori' probability values may be derived from first principles and "statistical probabilities are determined a posteriori by the empirical method of counting instances." (Runde [1998] quoting Knight, op. cit.)
:'''Estimates:''' The distinction here is that there is no valid basis ('a priori' or 'a posteriori') for any classifying phenomena. For Knight, this form of probability presented the greatest logical difficulties of all ... but its distinction from the other types must be emphasized, and some of its complicated relations indicated..." (pp. 224-5, emphasis in the original)

Arguably, Knight's concept of probability can be viewed as "..a continuum of probability situations, depending on the degree of homogeneity of the 'instances' in question" [12] ; i.e. going from a logically distinct but equally likely set of elements to a unique set with one member with statistical frequency in the middle. Knight's main motivation for distinguishing between a priori probability and statistical probability underscores his opinion that "(i) the 'mathematical or a priori type of probability is practically never met with in business, while the second is extremely common'; and (ii) 'the statistical treatment never gives closely accurate quantitative results' (Runde quoting Knight pp. 215-16). Runde argues that Knight couldn't conceive of (empirically tabulated) occurrences that are used in daily commerce classes of instances that we have to make do within the course of everyday economic life could be divided into subclasses of instances that are sufficiently homogeneous to permit the determination of what he calls 'real' probability (p. 217).
:'''In modeling degrees of uncertainty, any measure of belief is isomorphic but not necessarily equal to a probability or statistics measure'''.
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===Explanation, Prediction and Uncertainty===
It should be clear that the general notion of uncertainty synonymous with complete ignorance must be bounded as part of a scheme to produce a useful concept of uncertainty in professional practice such as civil engineering.
:The first principle that must be introduced is that such uncertainty must be bounded by reliable and valuable knowledge gained from past experience combined with analytical and computational capabilities to address an immediate and real problem of interest. This could be viewed as the economic interest argument. Only uncertainties associated with physical phenomena and economic scarcity will be addressed.
:The second principle is that the knowledge model is capable of producing statements or assertions in the form of hypotheses that themselves are testable. [https://en.wikipedia.org/wiki/Testability Testability], in this sense, is defined as the property applying to an empirical hypothesis and involves two components:
::The logical property that is variously described as contingency, defeasibility, or falsifiability, which means that counterexamples to the hypothesis are logically possible. Contrast this to a [https://en.wikipedia.org/wiki/Tautology_(logic) logical tautology], which is always true that is true in every possible interpretation. The practical feasibility of observing a reproducible series of such counterexamples if they do exist. In short, a hypothesis is testable if there is some real, non-zero expectation of deciding whether it is true or false of verifiable, reproducible experience. Upon this property of its constituent hypotheses rests the ability to decide whether a theory can be supported or falsified by actual experience data. (Source Wikipedia.)
'''Knowledge models must be capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria described above in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error and bias.'''
===Variability and Aleatory Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge referred to as epistemic uncertainty. Acting in the face of epistemic uncertainty introduces choice uncertainty (discussed below) and results in the production of explanations and predictions. It is implied that there will be testable hypotheses in the form of outcomes of interest to the knowledge professions. Inherent in nature is a variation of results, which may appear to uncertain but isn't. Observations of phenomena will not be the same, and part of the experimental process is to narrow those results down to within an acceptable level of tolerance. Statistical measures seek to identify underlying and unobserved measures of central tendency. These parameters are not directly observed but are derived to a degree of confidence.
In some cases, variation around a mean or within a statistical range may be perfectly acceptable to the knowledge profession. Examples of this are in the physical sciences and medicine. In Engineering and, to a lesser extent, economics, the process or phenomenon may be required to be more controlled to a narrower range than what a natural or "unmanaged" amount of variation would allow. Several courses of action present themselves.
:The first would be to ignore the aleatory phenomenon and choice, which is rejected outright.
:The second would be to assume that, in effect, such variation is itself a form of minimally managed uncertainty and treat it the same as epistemic uncertainty. This is not a desirable outcome as reducing Aleatory error and bias requires
:A third approach would be to acknowledge that process variation is, in some forms, manageable. This means making choices and acting in a way that presumes that the natural occurrence of error can be reduced, or more importantly, bias can be reduced. There is ample precedent for the third approach in civil engineering design and project management. In this sense, the argument is made that the aleatory response to managed efforts results in an elastic variation range. Presumably, an effective management effort either "shifts" the central tendency of the process results or reduces the variation range of occurrence or some other population parameter. Choosing to act and accept that narrower range rather than the broader natural variation may result in an outcome that, while well within the bounds of the expected variation, is outside the arbitrary control limits established by the choice and action. While this phenomenon mimics the broader uncertainty and its proxy error, it is not an error. The information on the variation was known, and the ability to produce narrower results was an error, albeit a pseudo-error, when compared to the broader classes of uncertainty discussed above, such as total ignorance.
::Looking at uncertainty using this third approach means that the error in choosing a new target parameter for this process may contain epistemic errors (our knowledge of the phenomenon may be erroneous, or the model flawed), aleatory uncertainties as discussed, or, more importantly, choice error or bias in thinking that we can move the process results to meet the requirements.
:'''In civil engineering practice, this may mean applying professional judgment in developing partitioning schemes or allocating uncertainty between the three dimensions (Epistemic, Aleatory, and Behavioral) of uncertainty.'''
Lastly, raising the topic like this brings the questions of effectiveness and efficiency to the fore. Advancing knowledge models that reduce error and bias at first are largely choice models resulting in justifications and favorable experiences. At some point in the process, the knowledge profession advances explanations and predictions that require reliable mathematical or logical concepts that are reduced to models and parameters. The question of efficiency becomes important; the broader uncertainty may have been reduced, but the process is economically inefficient. The profession is urged to become more frugal or, for example, reduce the variability of the outcomes. Knowledge professions often operate under conditions that require them to acquire knowledge and reduce uncertainty effectively and efficiently. Doing so requires understanding both epistemic uncertainty, as discussed above, and statistical process variation or aleatory variation.
:'''Knowledge professions manage and reduce uncertainty in the form of error and bias in an economically effective and efficient manner. This effort requires understanding both epistemic and aleatory uncertainty.'''
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===Choice and Behavioral Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge, referred to as epistemic uncertainty, as well as the inherent variations in results, or what was termed aleatory uncertainty. Acting in the face of that epistemic and aleatory uncertainty introduces yet another uncertainty into the process, namely choice uncertainty. This uncertainty reflects uncertainties associated with first-person and third-person choice. This is particularly relevant for engineering with project stakeholders.
===Summary of Semantics Framework for Uncertainty===
Professional disciplines have gradually re-framed their understanding of uncertainty and, thru experience and analysis, have arrived at the following meta-knowledge concepts of uncertainty:
* '''Uncertainty and its proxy, error, have three components: [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic epistemic uncertainty], [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic aleatory uncertainty], and behavioral uncertainty.'''
* '''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty using non-dogmatic beliefs, which recognize the existence of specific criteria and information that would make the believer change their beliefs using coherent approaches such as probability.'''
* '''Knowledge professions develop knowledge of how uncertainty and its proxy, error, propagate through an ordered and normative series of observations or choices that meet testability criteria in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error. An error can then be described, explained, and reduced in ways that uncertainty cannot. This allows the professions to prioritize error reduction to achieve an optimized reduction of uncertainty and produce a "legitimacy" from the fruitfulness of its use.'''
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===Uncertainty versus Risk/Hazard===
Up to this point, nothing has been said about risk or hazard. Now, risk can be defined. Simply put risk or hazard are finite, logically distinct subsets of the broader uncertainty. Risk and hazard are currently defined interchangeably. First and foremost, risk is a specific subset of general uncertainty. Any arbitrary subset of general uncertainty can be defined as a risk if it meets the following criteria:
* Uncertainty is associated with the potential for material error when acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena.
* Uncertainty and its proxy, error, can be partitioned into logically distinct subsets associated with multiple-step execution or choice frameworks in physical environments linked to a unique or limited range of sequences.
**Subcriteria 1:Resulting errors will have varying impacts or consequences depending on where the error occurs and the degree of causal linkage or influence in the chain.
**Subcriteria 2:Potential errors can be sequenced and prioritized to achieve an optimized reduction of error or bias and, by proxy, uncertainty over time.
* Uncertainty and its proxies, error bias, and environments are isomorphic to coherent approaches such as probability.
* Uncertainty and its proxies, error, and bias are isomorphic to coherent approaches such as knowledge models capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria in 'reasoning under uncertainty.'
This is a definition of general risk. There is no differentiation for engineering or economics, medicine or insurance. There is no difference between a 'good' risk consequence and a 'negative' one. Redefining general risk into simpler terms gives the following:
:'''General Risk is a subset of general uncertainty that is associated with the potential for material errors and biases when sequentially acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena; isomorphic to probability measures and methods and capable of testability.'''
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==Logical Framework for Specific Risk Taxonomies==
As noted above, at the most fundamental level, uncertainty is ".....the subjective perception of ignorance." [13] [Note 6] At this point in the discussion, the focus has moved from discussing uncertainty to its framed subset, risk.
:'''Further, the discussion moves from general risk to a professional or discipline-specific risk [https://en.wikipedia.org/wiki/Taxonomy taxonomy]. Beyond this point, risk will be used in place of uncertainty.'''
Han et al. note that, in general, "(t) taxonomies are valuable not only in their comprehensiveness but in their coherent reduction of uncertainty to conceptually discrete elements."
Uncertainty (and, by implication, risk or hazard) taxonomies offer an approach or tool to more precisely identify and define ontological uncertainty so that it can be quantified, analyzed, and communicated. Viewed this way, uncertainty is not a single, monolithic phenomenon but "...multi-dimensional with theoretically distinct domains and constructs that are potentially measurable and related to different outcomes, mechanisms of action, and management strategies." [14] Taxonomic schemes for classifying the different kinds of scientific and, by extension, engineering uncertainty require identifying the various kinds of potential error associated with descriptive scientific or engineering information and knowledge. An example of this is the following quote from a legal authority:
:"I would especially stress the need for an agency to disclose the uncertainty that surrounds its determinations. '''And by uncertainty, I mean the agency's ignorance as well as its quantitative estimates of error."''' (Emphasis added) [15]
'''Given the necessity of acting in the face of enormous uncertainties [16], discipline taxonomies must offer a clearly coherent and probabilistic approach to uncertainty that is sufficiently general in nature, logically distinct and exhaustive in scope and "...provide decision-makers with a foundation for understanding the nature of discipline information..."''' [17]
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=== Scientific Risk Taxonomies ===
The [https://en.wikipedia.org/wiki/Scientific_method scientific method] is a body of techniques for investigating phenomena in its environment, acquiring new knowledge, or correcting and integrating previous knowledge. Inherent in that process are uncertainties of many forms. Any one taxonomy of [https://en.wikipedia.org/wiki/Uncertainty scientific uncertainty] has largely been focused on statistical models used to assess and quantify sampling, errors, and parameters, although several different taxonomies are conceivable: note-24 [18]
====Environment-specific, Phenomena oriented, model-centric taxonomy of uncertainties====
** [https://en.wikipedia.org/wiki/Uncertainty_quantification#Sources_of_uncertainty '''Parameter uncertainty'''] the source of which is model parameters that are inputs to the computer model (mathematical model) but whose exact values are unknown and cannot be controlled, or whose values cannot be exactly inferred by statistical methods. Subsets of this type of uncertainty include but are not limited to:
***'''Experimental uncertainty''' is also known as observation error, which comes from the variability of experimental measurements. An example is repeating an experiment measurement several times using exactly the same settings for all inputs/variables and recording the variability. It is a subset of the larger uncertainty component, parameter uncertainty.
*** '''Parametric variability''' comes from the variability of the input variables of the phenomena model and is also a subset of the larger uncertainty component, parameter uncertainty.
** '''Structural uncertainty''', or model inadequacy, model [https://en.wikipedia.org/wiki/bias bias] or "[https://en.wikipedia.org/wiki/Systemic_bias systemic bias]", or model discrepancy, which comes from the lack of knowledge of the underlying true state of the phenomenon or its environment. It depends on how accurately a mathematical model describes the true state of the phenomenon or what is termed "model fitness". Due to the inherent nature of uncertainty in any knowledge body (scientific or engineering), models are only an approximation to reality. Therefore, any conclusions drawn from the model can be very misleading when the underlying basis is not plausible or lacks validity. '''note-25 [19]]'''
*** What is missing in this context is any concept of output variability due to the model itself. Part of this is due to the lack of recognition of "process" in scientific investigation. The concept of the scientific method started with a single investigator, such as Galileo or Michael Faraday, working in their laboratories. It has grown into planet and solar system scale experiments such as data gathered on planetary flybys such as Mars, Pluto, and Ceres to the hunt for the Higgs Boson. Ultimately, science has become more like engineering in the scale and complexity of its investigations and theory aggregates, such as the "Unified theory" of particle physics. Subsets of this type of uncertainty include but are not limited to:
*** '''Algorithmic uncertainty''', or numerical uncertainty, comes from numerical errors and numerical approximations per implementation of the computer model. Most models are too complicated to solve exactly. For example, the [https://en.wikipedia.org/wiki/Finite_element_method finite element method] or [https://en.wikipedia.org/wiki/Finite_difference_method finite difference method] [may be used to approximate a solution [https://en.wikipedia.org/wiki/Partial_differential_equation partial differential equation], which introduces numerical errors. Other examples are numerical integration and infinite sum truncation, which are necessary approximations in numerical implementation.
*** '''Interpolation uncertainty''' comes from a lack of available data collected from computer model simulations or experimental measurements. For other input settings that don't have simulation data or experimental measurements, one must interpolate or extrapolate to predict the corresponding responses.
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====Commentary====
A more comprehensive taxonomy of uncertainties that acknowledges knowledge, data, and linguistic uncertainties is:
*'''Epistemic uncertainty''' -[https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Epistemic uncertainty] or systematic uncertainty, in contrast, reflects limitations in the current “state of knowledge” underlying models themselves, originates from competing theories or models, is not readily quantifiable, and is manifest by subjective confusion or indecision. It includes uncertainty due to measurement limitations, insufficient data, extrapolations and interpolations, and variability over time and space. Epistemic uncertainty is uncertainty about "...some determinate fact ... because of a lack of complete information. '''cite_note-26 [20]]''' Epistemic uncertainty can be classified into six main types:'''cite_note-27 [21]]'''
*''' Measurement error''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Observational_error Measurement error] is uncertainty that manifests itself as (apparently) random variation in the measurement of a quantity. Repeated measurements will vary and demonstrate classic statistical behavior.
* '''Systematic error''' - [https://en.wikipedia.org/wiki/Observational_error Systematic error] occurs due to bias in the measuring equipment, model, analysis, observation, or sampling procedure. It is formally defined as the difference between the true value of the quantity of interest and the value to which the mean of the results converges as sample or data sizes increase. Unlike measurement error, it is not (apparently) random, and therefore, results subject to systematic error alone do not vary about a true value. Systematic error can result from deliberate judgment to exclude (or include) data, parameters, or models that ought not to be excluded (or included).
** "The only way to deal with systematic error is to recognize a bias in the process, model, or procedure and remove it. Systematic error, however, is notoriously difficult to recognize except on theoretical grounds. Corrections may only be applied when the magnitude and direction of the bias are known. Such corrections underlie the application of double-sampling methods in environmental science." (Regan, et. al., op. cit.)
*'''Natural variation''' - [https://en.wikipedia.org/wiki/Natural_process_variation Natural variation] or underlying process variation occurs in systems that "...change (concerning time, space, or other variables) in ways that are difficult to predict...across the full range of temporal and spatial values (or other related variables)." '''note-28 [22]'''
** This form of uncertainty is reduceable utilizing tools such as [https://en.wikipedia.org/wiki/Statistical_process_control statistical process control] and replication of experiments or observations.
*'''Inherent randomness''' - [https://en.wikipedia.org/wiki/Randomness randomness] or [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty aleatory uncertainty] exists because the system is"...in principle, irreducible to a deterministic one (the most well-known case is described by [https://en.wikipedia.org/wiki/Uncertainty_principle Heisenberg’s uncertainty principle] in quantum mechanics)." '''note-29 [23], note-30 [Note 7]'''
** Even if the argument is accepted that it is unlikely that any given physical system at some level is inherently random, it is important for any taxonomy to distinguish between systems that appear random due to incomplete or inconsistent information and those that are intrinsically random. (Again, see Regan (2002)).
*'''Model uncertainty''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling Model uncertainty] occurs in the process of generating a model as a conceptual representation of a particular phenomenon to create a [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling#Overview simplified reflection of reality]. This selectivity of factors and variables creates uncertainty in at least three ways. (Regan (2002))
** '''Variables and processes''' that are regarded as relevant often represent a trade-off between system knowledge and assumed states and model objectives;
** '''Models''' depict observed processes using logical or mathematical constructs based on underlying theories about system states or dynamics using continuous equations to describe discrete processes.
** '''Curve fitting''' (including interpolation and extrapolation) with mathematical expressions using model variables where inputs are discrete data points.
** Regan (2002) argues that '''model uncertainty''' is "...notoriously difficult to quantify and impossible to eliminate ...(and)...(t)he only reliable way of determining how appropriate a model is for prediction is to perform validation studies."
** Subjective judgment occurs due to the interpretation of scarce or error-prone data.
*** The only way to address this type of uncertainty is to "...assign a degree of belief about an event in the form of a subjective probability." (Regan (2002))
*Linguistic uncertainty - Linguistic uncertainty, or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. '''note-31 [24]], note-32 [25]]'''
** '''Context dependence''' is uncertainty concerning the context in which a statement is to be understood;
** '''Ambiguity''' occurs when words have multiple meanings, and further reduction to a single meaning is not logically possible in a given context.
** '''Underspecificity''' occurs in the presence of "unwanted generality" or multiple interpretations, and reduction to a smaller set or even a single interpretation is not logically possible.
*Stochastic or statistical uncertainty - Stochastic or statistical uncertainty, which is sometimes known as [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Aleatoric uncertainty] as well as the subject of [https://en.wikipedia.org/wiki/Stochastic_control stochastic control] pertains to the parameters of a risk model, originates from sampling or measurement error, and can be quantified and mathematically expressed (e.g., using confidence intervals).
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== Uncertainty Taxonomies in a Legal Environment ==
From a legal perspective, a definition of taxonomy is dictionary-based, such as "the systematic distinguishing, ordering, and naming of type groups within a subject field." given by Walker (op. cit.). Such legal concepts of scientific or engineering taxonomies are not required to be epistemically complete. Still, they must cover "the most significant aspects of ...(current, good discipline practices) ... and of the descriptive information commonly encountered by decision-makers" in a logically distinct manner. (Walker, 1991, p. 571) These taxonomies must be sufficient to catalog the types of uncertainty associated with descriptive assertion, including the critical subset of cause and effect assertions. This legal/decision-maker framework has presented descriptive uncertainty as having six components (Walker, 1991). Although not part of the Walker taxonomy, underlying the scheme is linguistic uncertainty, or "vagueness," as a source of uncertainty, including uncertainties due to context dependence, ambiguity, and under-specificity. '''note-33 [26]]''' Arguably, these uncertainties underlie the most basic element in the taxonomy.
* '''Linguistic '''- or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. (Regan, 2002)
* '''Conceptual'''- the definition by choice and design of descriptive concepts or variables to be used as [https://en.wikipedia.org/wiki/Predicate_(grammar) predicates] where the subject of an assertion identifies what is being discussed and the predicate provides information about the topic or characterizations such as what the subject is, what the subject is doing, or what the subject is like (Wikipedia).
* "Conceptual uncertainty, or the potential for conceptual error, arises whenever predication occurs. Whenever a concept is used to describe something, using certain concepts instead of others begins to structure how we understand the object, event, or instance under discussion. Predication or conceptualization generates useful information about things, but it also can inhibit our ability to think about those things with concepts other than those selected. The concepts used may not be the most fruitful or the best designed- either for scientific purposes or for making wise, fair, effective, and efficient decisions." (Edited for reading, Walker, 1991)
* Similarly, choice and design of descriptive variables enhance and restrict data set membership, creating potentially incomplete, incompatible, or inconsistent data sets, "...the classification categories employed, and the relationships among those categories (nominal, ordinal or scalar)." '''note-34 [27]]'''
* Concept uncertainty '''note-35 [28]''' like systematic error discussed above is notoriously difficult to recognize, except on theoretical grounds, and the magnitude and direction of the bias are known.
Both terms could be analogized to that of [https://en.wikipedia.org/wiki/Accuracy_and_precision#Terminology_of_ISO_5725 accuracy] in [https://www.bipm.org/en/publications/guides/ ISO 5725] which looks at the ability of the concept or system's ability to produce results that are proximate to the "true" value or state of the phenomenon. Conceptual uncertainty and systemic bias are related to another measure of uncertainty: validity. Walker has defined validity as the ability of a concept to quantitatively describe and explain what the phenomenon objective "truly" is. Walker also ties the concepts together when he states, "Validity concerns the "accuracy" of the measurement data, not its precision." (Walker, 1998) 
Additionally, conceptual uncertainty and associated validity issues could be propagated throughout this taxonomy. Every component of action and decision in the face of uncertainty raises validity issues. Walker recognized this when the author defined epistemic choice for concepts used throughout the other uncertainty components or layers.
* '''Measurement''' uncertainty or Misclassification error- the application of the underlying concepts or variables to specific, individual cases and the uncertainty of the reliability (Walker, 1998) of the resultant data. (It is important to remember that assessment observation and measurement are used interchangeably in this analysis.) Thus, an assessment method or procedure is considered "reliable" or "precise" in the scientific terms of ISO 5715 if it repeatedly produces consistent results.
* Measurement uncertainty is logically distinct from conceptual uncertainty, which can only occur after any conceptual uncertainty has been established. Still, misclassification can occur even when the conceptual uncertainty has been minimized. Conversely, underlying errors or errors made in measurement or classification propagate thru the reasoning chain and reduce the quality or value of the final result.
* While uncertainties associated with quantitative measurements or assessments can be statistically evaluated and described, uncertainties related to qualitative assessments are more problematic.
**Working through the taxonomic chain from base concept to causal explanation requires amassing and integrating qualitative information and quantitative data into input parameters for mathematical models. Developing procedures for producing consistent, repeatable sets of information from assessments that minimize conceptual error or systemic bias requires professional efforts to "validate" such methods, models, and judgments at all layers in the process. Validation as a process is something that civil engineering as a practice needs to embrace more frequently.
* '''[https://en.wikipedia.org/wiki/Sampling Sampling]'''- in the classical statistics sense, is the selection of a limited subset of a larger population to use as a basis for making estimates about the population in the form of attributes, proprieties, or parameters. The resulting information would be determinative in making forecasts about future population sampling or possessing a desired amount of predictive power. The uncertainty about the ability of this information to correctly predict future samples is termed sampling error.
* '''Modeling'''- Modeling uncertainty arises whenever a claim is made that out of a class of candidate relationships and variable pairings, one variable pairing inclusive of constants is chosen that possesses a particular or persistent mathematical relationship to another variable X. Modeling errors arise in selecting the wrong variable pair and constants or by incorrectly specifying its constants. (Walker, 1991, pg. 599)
** '''Causal'''- In causal modeling and analysis, the relationship between causal variables is not strictly a mathematical function. Still, it makes testable predictions and explains "...how a system of variables works, why a system works the way it does, or why it makes sense to think of certain variables as a system" at all." (Walker, 1991, pg. 609)
** '''Epistemic'''- the choice of interpretations for fundamental, logical concepts used throughout the other components or layers.
The components of this uncertainty taxonomy can combine in different ways to "...produce different aggregate uncertainties." '''note-36 [29]'''
:''' Only the scientific and engineering professions possess the capacity to develop and validate procedures and models that combine the different kinds of qualitative '''note-37 [30]''' and quantitative information and their associated potential for error into an aggregate measure or "scalar variables" of uncertainty.'''note-38 [31]]
Scalar variables are quantitative variables whose categories are related by some measure of the relevant property's incremental frequency, degree, or amount. (Walker, 1991) Examples are Modulus of Elasticity, Compression stress in elastic materials, etc.

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=== Economics Uncertainty Taxonomies ===
Like scientific concepts of uncertainty, economics is environment-specific (in this case, read sector of economy or markets), phenomenon-oriented (in this case, read human activity and manufactured phenomena such as firms and markets guided by human judgment and decision), and model-centric.
:''' Economics is not interested in all forms of uncertainty, only specific, meaningful subsets possessing casual connections with material impacts, often labeled as 'risks'.'''
One of its earlier writers ([https://en.wikipedia.org/wiki/Frank_Knight#Life_and_career Knight], 1921, 1948, 1957) attempted to define "uncertainty" as the presence of "defects of managerial knowledge" (or [https://en.wikipedia.org/wiki/Knightian_uncertainty knightian uncertainty] and thus risk centric) as risk that is immeasurable, not possible to calculate.(Wikipedia) Risk in this sense was defined as "the ordinary risks of business activity which can ... be reduced... by applying the insurance principle." Knight does acknowledge that risk is a specific and unambiguous subset of uncertainty. (Risk, Uncertainty and Profit, 1957, pg. 19) For Knight, measuring uncertainty meant measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences, which Knight referred to as "the imperfection of knowledge". (Risk, Uncertainty and Profit, 1957, pg. 197) One of Knight's many contributions to this analysis was recognizing information and knowledge's role in economic activity.
:"''(W)e are concerned only to emphasize the fact that '''knowledge is in a sense variable in degree and that the practical problem may relate to the degree of knowledge rather than to its presence or absence in toto.''' We live only by knowing something about the future, while the problems of life, or conduct at least, arise from knowing so little. This is as true of business as of other spheres of activity. The situation's essence is action according to opinion, of greater or less foundation and value, neither entire ignorance nor complete and perfect information, but partial knowledge. If we are to understand the workings of the economic system, we must examine the meaning and significance of uncertainty; and to this end, some inquiry into the nature and function of knowledge itself is necessary.''" (Knight, pg. 199) Emphasis added
Knight also reinforced the difficulties in classifying uncertainties when he analyzed life insurance. In this case, accidental death was an uncertain phenomenon compared to sickness and accident, where an "...objective description and classification of cases was impossible..." (Knight, pg. 248) Another factor that made uncertainties ineligible as risks and, therefore, uninsurable because they were unclassifiable was the exercise of judgment in making decisions by the businessman. (Knight, pg. 251)
This is a simple classification of economic uncertainty and, as an idealization, is philosophically controversial. '''note-39 [32]'''
* Uncertainty and information about the economic environment are distinct from uncertainty about others’ behavior or choices.
* Risk as a specific subset of uncertainty implies that "...all possible acts are known, all possible outcomes arising from each act are known, and it is possible to assign probabilities to each act." '''note-40 [33]'''
Like the economic [https://en.wikipedia.org/wiki/Theory_of_the_firm theory of the firm], an economic theory of uncertainty seeks to explain and predict the nature of a specific subset of uncertainty (in this case, risk relating to physical phenomena and human activity), including its behavior, structure, and relationship to the universal uncertainty of physical phenomena. In this sense, uncertainty, like firms and markets, can establish price equilibriums under the right circumstances different from those that might have occurred in either of the other two. Such a theory of uncertainty offers a partitioning scheme that decomposes uncertainty into distinct and meaningful subsets, of which risk is a significant one. It also explains how the presence and variability of knowledge cause or " drive" economic consequences. Unlike physical phenomena, economic activity creates artificial objects such as products, firms, mediums of exchange, markets, economies, and knowledge about those activities. The economics of uncertainty exists as an alternative to "certainty" models that assume away imperfect knowledge and unknown choice/judgment preferences when it is more efficient for economic decision-making. This also allows the introduction of the logical concept of "hazard" versus "risk". Frank used hazard extensively in Risk, Uncertainty, and Profit, but not to the extent that one could say it was interchangeable. An example of this is in the term "[https://en.wikipedia.org/wiki/Moral_hazard Moral hazard]," where a firm could engage in "riskier" and potentially "uninsurable" behavior if the information were transparent, and, therefore, insurance coverage of losses is limited. note-41 [34]], note-42 [35]]

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=== Uncertainty and Risk in Policy and Regulatory Environment ===
:''"Hume's 'just reasoner', when faced with a difficult public policy decision in current times, would probably commission a risk analysis. But how could they incorporate 'a degree of doubt, caution, and modesty?"'' '''note-43 [36]'''
The problem of "decision-making in the face of uncertainty" is how to regulate based on incomplete information that has the potential to be materially inaccurate. A contextual assumption is that we need to evaluate decision rules for dealing with uncertainty. As a social enterprise, risk regulation, whatever its substantive objectives, should be as '''effective, efficient, and equitable''' as practically possible. These "three E's" form a set of "process objectives" or "meta-goals." From the uncertainty standpoint, causal information can be usefully divided into two major categories: information about groups and information about individuals.

Each category, group, and individual has its distinctive types of inherent uncertainty. These are logically distinct, generally independent, and cumulative, contributing to '''(?????)'''

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=== Engineering Uncertainty Taxonomies ===
Like scientific and economic concepts of uncertainty, engineering is environment-specific, phenomena-oriented, model-centric, external choice, and driven by decision-making in the face of uncertainty. Like economics and unlike science, engineering is not interested in all forms of uncertainty, only those with determinable and material consequences. Like economics, engineering conceives risk as a specific and unambiguous subset of uncertainty determined by measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences. Also, engineering recognizes the role information and, by extension, knowledge plays in constructing the built environment.
'''note-44 [37]]''' Like economics, engineering differs from economics and scientific uncertainty in its interest in behavioral uncertainty.
*"Behavioral or interaction uncertainty is how individuals or organizations act or interact. Behavioral uncertainty arises from four sources: design uncertainty, requirement uncertainty, volitional uncertainty, and human errors." '''note-45 [38]]'''
** A design uncertainty is a choice among alternatives over which an individual or group of individuals exercises direct control but has not yet decided upon.
** Requirement uncertainty includes parameters of interest to and determined by the stakeholder, independent of the engineer or designer.
** Volitional uncertainty is uncertainty about what the subject him/herself will decide. Other people’s future actions and conduct are not entirely predictable, particularly when dealing with other organizations.
** Human errors occur during the development of a system or project due to blunders or mistakes by an individual or individuals.

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==Practice Frameworks for Uncertainty and Risk in Civil Engineering Practice==
===PMIBoK===
PMI does not define uncertainty, but the PMI Body of Knowledge uses the term in 45 places (uncertainty) and as describing properties in 8 places (uncertainties). [See Note 8] PMI does define "risk" and uses the term over 1,300 times in the same document. In its glossary, PMI also defines terms such as "threat" and "opportunity" but not events.[391 The BoK contextually defines uncertainty when it states that project risk has its origins in the uncertainty present in all projects. (Op. Cit., pg 309) 
'''Uncertainty''' is contextually defined and taken as having the property of affecting project execution and the ability to meet stakeholder expectations. Uncertainty is more than the sum of all individual risks (known and unknown). The nature of this uncertainty is not defined, only its capacity to affect something else or its properties. 
'''Risk''' is defined as " ... an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives." (Ibid.) Beyond this formal definition, PMI adds context when, in its risk management section (Sec. 11 ), the BoK states that risk is a caused event or condition with multiple causes and impacts. The BoK even offers specific techniques for mapping cause-and-effect relationships and influence diagrams. (Sec. 11.2.2.5) Not as evident but equally important is the recognition that not only is risk caused but the impact is "triggered". The risk trigger is an event or situation that signals that it is about to occur (Glossary, pg. 566). Risk conditions are factors that affect or otherwise contribute to risk, such as project stakeholders. Risk implicitly retains some residual element of probability in it, or a value of less than 1.0 with risk that approaches a level of near certainty is termed "issues" or "realized risk" .(Op. Cit., pg 309). PMI also links risk to underlying variations in project outcomes. (Ibid.) Risk is based upon data and information which can be assessed as to its quality (Sec. 11.3.2.3). This analysis looks to determine the value of the information and examines the degree to which the risk information is understood as well as its " ... accuracy, quality, reliability and integrity ... " (Ibid.) Part of that is understanding the relationship between occurrence and impact as outlined in PMl's Figure 11-10 Risk Impact Matrix and developing a model for prioritizing risk products and setting the threshold for action.
====Commentary:====
For defining and identifying risk, PMI notes in Sec. 11.2.2.4 that " ... Every project and its plan is conceived and developed based on a set of hypotheses, scenarios, or assumptions." Part of the risk analysis process is identifying risks to the project, such as inaccuracy, instability, inconsistency, or incompleteness of assumptions. Similarly, the quality of management plans, as well as their consistency with others and the project objectives and assumptions are 11 •• .indicators of risk in the project." (Sec. 11.2.2.1) 
Similarly, the role of engineering economics in risk is embedded or implied in the PMI definition of risk when the BoK discusses risk identification as a process of assessing which risks out of the total risk exposure for the project "may affect" the project and distilling their characteristics. Embedded in this statement is the concept that not all risks can cause economic impacts (positive and negative) to the project. Also embedded is the requirement to understand risk characteristics to identify project risk (Cost and schedule). Lastly, risk identification is an iterative process of incrementing project risk documentation, much like project scope, cost, and schedule documentation. 
Lastly, PMI acknowledges the complex constitution of risk aggregates or the "sum" of all risks in its material, but is it a sum? Risk information has descriptive and explanatory content. These are knowledge and meta-knowledge components. Understanding project assumptions is project-specific knowledge, but identifying risks from assumption inaccuracy, instability, inconsistency, or incompleteness requires professional or program meta-knowledge. 
'''Uncertainty exists in all projects, but risk, a subset of uncertainty with material economic impacts, is a key meta-knowledge element for civil engineering practice.'''
===Software Development Process- Risk Focused===
The Unified Process requires the project team to focus on addressing the most critical risks early in the project life
cycle. The deliverables of each iteration, especially in the Elaboration phase, must be selected to ensure that the greatest risks are addressed first. <ref> Booch, Grady, Ivar Jacobson, and James Rumbaugh. "The unified software development process."
Reading: Addison Wesley (1999).</ref>
===American Society of Civil Engineers (ASCE)===
ASCE does not define uncertainty, but the ASCE CE Body of Knowledge (CEBoK) uses the term in 22 places (uncertainty) and describes properties in 13 places (uncertainties). 
The CEBoK does not use the phrase "uncertainty and risk" as PMI and AACE do in their publication. ASCE reverses the two (Risk and uncertainty) in twelve places regarding technical outcomes (Outcome 12). A fundamental difference between PMI and ISO is that risk is considered a subset of uncertainty. ASCE does not define "risk" but uses the term 25 times in the same document. Almost all are in the context of variation of design parameters and not in the classical context of cost, schedule uncertainty, and risk.
====Commentary:====
...
=== American Association of Cost Engineers (AACE) ===
AACE defined uncertainty, risk, and other related terms in its Risk Management Dictionary. <ref>Risk Management Committee uRisk Management Dictionary, Cost Engineering Vol. 37/No. 10, OCTOBER 1995. </ref>
AACE's overall strategy was to define risk and other terms that stem from base uncertainty. AACE first defined uncertainty as .... "(t)he total range of events that may happen and produce risks (including both threats and opportunities) affecting a project (see opportunities, events, conditions, risk, and threats" where the following sub-definitions apply:
** Biases--A lack of objectivity based on the individual's position or perspective. Systematic and predictable relationships between a person's opinion or statement and his/her underlying knowledge or circumstances. Note: there may be "system biases" as well as "individual biases."
** Condition (Uncertain Condition)-Any specific identifiable circumstance (such as the rate of inflation or the quality of labor available) that might affect the outcome of the project
** Event (Uncertain Condition)-is a specific identifiable action (such as a large government project being started in the same labor area as your project) or an act of nature that might happen and that (if it does happen) could affect the outcome of the project.
** Opportunities are Uncertain events that could improve the results or improve the probability that the desired outcome will happen
** Threats are Uncertain events that are potentially negative or reduce the probability that the desired outcome will happen.
*** AACE defines Risk as an "ambiguous term" (sic) that is synonymous with uncertainty or a negative subset such as "threats", or an overall negative impact of all possible uncertainties.
====Commentary:====
...
==Limitations of the definition==
...
==Working definition==
...
==Beneficial outcomes==
...
==See also==
Wiki articles on the project.

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==Notes==
<references group="note" />
<li> Implicit in this definition of uncertainty as a "state of' is ... </li>
: " ... a conceptualization of uncertainty as a subjective, cognitive experience of people--a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one's lack of knowledge, without which one could not feel uncertain; '''uncertainty is a form of "meta-cognition" ... ( or alternatively, its main component, meta-knowledge) ... -a knowing about knowing.'''" (Han, 2011, op. cit., Emphasis added)
</ol>
<ol start="5">
<li> Cox wanted his system to satisfy the following conditions:</li>
: Divisibility and comparability- The plausibility of a statement is a real number and depends on the information we have related to the statement.
: Common sense - Plausibilities should vary sensibly with the assessment of plausibilities in the model.
: Consistency - If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). -preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf
</ol>
<ol start="6">
<li> The utility of existing taxonomies of uncertainty in civil engineering and, by extension, "risk" has been unsatisfactory for this very reason. This is particularly true in cases where the source of the uncertainty is conflicting versus incomplete information. See Regan, et. al.,"A taxonomy and treatment of uncertainty for ecology and conservation biology" (2002)
for a survey of scientific taxonomies for uncertainty.</li>
<li> Regan et al. argue that " ... genuine examples of this kind of uncertainty are hard to find. Even classic cases of random experiments like coin tosses and the throwing of dice are deterministic; it is just that we do not have enough information about the dynamic processes and initial conditions to make any sensible estimates about the outcomes. Such processes are, for all intents and purposes, inherently random, but they are not genuinely inherently random. For similar reasons, complex systems such as ecosystems and weather patterns are unlikely to be inherently random. Similarly, chaotic systems are entirely deterministic. They are unpredictable because the deterministic processes generating them and the relevant initial conditions are hard
to fully specify (see Stewart 1989, Sugihara et al. 1990)." (Regan (2002) </li>
<li> The CEBoK uses the phrase "uncertainty and risk" twice in reference to scheduling (Section 6.5,2) and cost estimating (Section 7.2,2) as well as reversing the two (Risk and Uncertainty) also in two places in discussing project life cycle (both in Section 2.4.1) </li>
</ol>

==References==
<references />

Uncertainty and Risk in Civil Engineering Practice

2024-11-24T16:38:47Z

WikiSysop:

'''Life is short and the art long; the occasion instant, experiment perilous, decision difficult.''' (Hippocrates as quoted in Fox,)
:''See also [[Project Definition]] , [[Project Life Cycle and Phase Models]] , [[Project Delivery Methods]]''
:''See also [[Technical Outcomes for Civil Engineering:Risk and Uncertainty]]''
==Basic Considerations==
As human beings,'' ... being alive means seeking opportunities and taking risks.'' As knowledge professionals
living in the 21st century, this means coping with an increasingly complex number of uncertainties for humans
living in this environment. We seek to understand better how these uncertainties can be characterized and
managed. The essence of this article and the experience for engineers in general and civil engineers in particular is
that manageable uncertainty is, by definition, termed risk, and its kindred cousin is termed hazard. This causes us to experience the
:" ''... human dread of and fascination for risk and the increasingly important role of risk analysis within societies ...'' " <ref name="McDaniels"> McDaniels, Timothy, and Mitchell Small. Risk analysis and society: an interdisciplinary characterization of the field. Cambridge University Press, 2004, page 1 </ref> (Ibid.)
and, by extension, civil engineering. McDaniels et al. argue that risk management has been
fundamental to our social and governance development for the past 10,000 years. The scale and shape of the
uncertainties faced in this period shaped the societies that have developed today. The central thrust of this effort
over the centuries has been to reshape and re-frame our understanding and conception of uncertainty from one of
complete unknowing and simple acceptance as our fate in life to one of management (cf "''Against the Gods''"
concept in Bernstein's book <ref name = Bernstein">Bernstein, Peter L., and Jesse Boggs. Against the gods. Simon & Schuster 1997.</ref>.
: ''All the knowledge professions and disciplines have struggled with managing uncertainty, for it is impossible to manage unbounded uncertainty.'' <ref group="note">See McDaniels et al. 2004 for an extensive discussion and bibliography on the historical development of Risk Analysis and some key milestones in risk analysis in the 20th century.</ref>
As outlined below, professions such as civil engineering have successfully acted in the face of uncertainty. It lies in the profession's ability to reduce a wide variety of uncertainties into increasingly smaller and crucially bounded subsets that can be managed. These are called 'risks. More recently, this process has evolved, and individual disciplines such as Civil Engineering (CE) have developed their knowledge and models to perform risk analysis. Risk is also taught as a distinct discipline and specialty practice within civil engineering. Still, it has some unique features that set it apart from other more classical practices within CE. As such, it is one of the first of some very specialized CE practices that use knowledge about civil engineering knowledge, or meta-knowledge. Other examples of civil engineering metaknowledge are project controls and quality controls.
==Semantic, Epistemic and Logical frameworks==
The semantic, epistemic, and logical frameworks for uncertainty and risk have several dimensions and layers of logical frameworks. The semantics problems include interchangeable usages for risk and uncertainty and hazard, uncertain and imperfect, and a lack of definitional material or context. The semantical scheme for this article will be to proceed from the distinction between certainty and uncertainty through marginal refinements and reductions of uncertainty up to the point of causal uncertainties. Beyond this point of semantics will be the logic frameworks or the subject of taxonomies of professional knowledge. First, it is about simple, testable phenomena, and then, it moves on to complex taxonomy schemes for artificially constructed phenomena or engineering projects.
<ref group="note">TBD</ref>
==Semantics framework==
===Certainty and Uncertainty===
''''' There is no such thing as absolute certainty, but there is assurance sufficient for the purposes of human life.''''' ([https://en.wikipedia.org/wiki/John_Stuart_Mill John Stuart Mill]) 
'''''If you tried to doubt everything, you would not get as far as doubting anything. The game of doubting itself presupposes certainty.''''' ([https://en.wikipedia.org/wiki/Ludwig_Wittgenstein Wittgenstein] # 115 from On [https://en.wikipedia.org/wiki/On_Certainty Certainty]) 
It is important to note that the references to epistemic or epidemiological knowledge in this article are assumed to relate to three forms of knowledge, namely:
* Knowledge that ( [https://en.wikipedia.org/wiki/Descriptive_knowledge descriptive or declarative or propositional knowledge])
* Knowledge how ( or "[https://en.wikipedia.org/wiki/Procedural_knowledge know-how]"), and
* [https://en.wikipedia.org/wiki/Knowledge_by_acquaintance Knowledge by acquaintance].
'''Certainty''' has been defined as "an epistemic property" of knowledge in all its forms and the state of our beliefs about that knowledge.
<ref>Certainty, Stanford Encyclopedia of Philosophy, accessed on September 12, 2015, at http://plato.stanford.edu/entries/certainty/#ConCer </ref>
Certainty about any belief about knowledge implies that it is not subject to doubt or skepticism. This immunity to criticism can be dogmatically based, emphasizing the importance of a propositional-based sense of truth over experiential, sensory perceptions. <ref>Definition of [https://en.wikipedia.org/wiki/Dogmatic_theology Dogmatic theology or belief], accessed at Wikipedia</ref>
: '''The demarcation line between dogmatic and non-dogmatic beliefs lies in the presence and recognition of specific criteria and information that would make the believer change their beliefs.'''
An empirical framework of [https://en.wikipedia.org/wiki/Testability testability] and [https://en.wikipedia.org/wiki/Falsifiability falsification] is required to recognize this and limit dogmatism. For example, one could argue that people would change their minds if God asked them to. Similarly, one could construct a concept of epistemic as opposed to dogmatic belief certainty as the ability to know anything that one chooses to know and can be known or [https://en.wikipedia.org/wiki/Omniscience inherent omniscience].

A second kind of certainty is epistemic, when conviction reflects the highest possible support for a belief. In this sense, knowledge is separate from beliefs, although someone may have beliefs about a property, such as certainty of knowledge. Logically, it has been shown that there will be unprovable statements within the system for any such knowledge system. Secondly, the knowledge system cannot demonstrate its own consistency. ([https://en.wikipedia.org/wiki/Gödel%27s_incompleteness_theorems Gödel's incompleteness theorems])
: '''Certainty, in real life, is useless or often damaging (the idea is that "total security from error" is impossible in practice, and a complete "lack of doubt" is undesirable)''' ([[https://en.wikipedia.org/wiki/Certainty Physicist Carlo Rovelli]])
'''Uncertainty''', on the other hand, arises immediately in the slightest amount of doubt or criticism.
<ref group="note"> The Merriam-Webster dictionary defines uncertainty as “the quality or state of being uncertain,” which is something of a circular definition. Likewise, one could define uncertainty as the state of not being certain.

Synonyms are distrust, doubt, misgiving, mistrust, reservation, skepticism, and suspicion. Another writer added indefinite, indeterminate, not certain to occur, problematical, unreliable, untrustworthy, unknown beyond doubt, dubious, doubtful, not clearly identified or defined, not constant, variable, and fitful to the list.
:(Han, Paul KJ, William MP Klein, and Neeraj K. Arora. "Varieties of Uncertainty in Health Care: A Conceptual Taxonomy." Medical Decision Making 31.6 (2011): 828-838.)
Han et al. noted that any definition of uncertainty clearly encompasses "...numerous types, sources, and manifestations of uncertainty, and ...(any) ...useful working definition of uncertainty needs to specify the concept underlying these varied meanings of the term."</ref>
Implicit in this definition of uncertainty as a “state of” is ...
"...a conceptualization of uncertainty as a subjective, cognitive experience of people—a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one’s lack of knowledge, without which one could not feel uncertain; uncertainty is a form of “meta-cognition” ...(or its main component, meta-knowledge)... —a knowing about knowing." (Han, 2011, op. cit., Emphasis added)
Similarly, uncertainty could be defined as "...any departure from the unachievable goal of complete [https://en.wikipedia.org/wiki/Determinism determinism]."
<ref name="Walker1">Walker, W. E., Harremoës, P., Rotmans, J., Van Der Sluijs, J. P., Van Asselt, M. B., Janssen, P., & Krayer von Krauss, M. P. (2003). Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support. Integrated assessment, 4(1), 5-17.</ref>
Reasoning under uncertainty is very different than performing the same under certainty. In reasoning under certainty, one has complete knowledge and deduces without doubt and equally important, without limitation, thereby concluding free from error. Reasoning under uncertainty, one works in a state of incomplete, inconsistent, and limited knowledge; doubts cloud any statements or assertions and thereby taint any deductions/inferences resulting in the potential for error. <ref group="note">Implicit in this definition of uncertainty as a “state of” is ...
"...a conceptualization of uncertainty as a subjective, cognitive experience of people—a state of mind rather than a feature of the objective world. The defining feature of this state, furthermore, appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one’s lack of knowledge, without which one could not feel uncertain; uncertainty is a form of “meta-cognition” ...(or alternatively, its main component, meta-knowledge)... —a knowing about knowing." (Han, 2011, op. cit., Emphasis added)</ref>
:'''Knowledge professions reason in a state of incomplete, inconsistent and limited knowledge with doubts that cloud any statements or assertions and taint any deductions/inferences resulting in the potential for error.'''
:''' It is also important to note the semantical and logical correlation between 'reasoning under uncertainty' or 'acting in the face of uncertainty' with the 'potential for' or 'presence of' error. The presence of uncertainty is invariably linked to the potential presence of error.''' The [https://en.wikipedia.org/wiki/Contingency_(philosophy) contingent] nature of uncertainty logically implies the contingent nature of error.
:'''Knowledge Professions use error as a proxy for uncertainty such that within discipline knowledge frameworks, uncertainty can be managed.''' The rationale for this belief is that error can be reliably described, quantified, explained, and ultimately reduced in ways that uncertainty cannot.

'''Uncertainty''' implies a range of variation that is impossible in certainty. Concepts and definitions of uncertainty are unbounded and vast but start from the point of complete and total ignorance. In describing uncertainty, statements can range from complete ignorance to relatively high degrees of belief that there is less potential for error in our reasoning or action. The latter is based upon confidence in justified knowledge, favorable past experience, and reliable parameters/models. One can't make that statement about ranges in certainty. If certainty, by definition, precludes doubt of any type or nature, then how could that be graduated to any degree? 
Uncertainty, on the other hand, can be reduced through disciplined efforts to acquire knowledge. In short, we can reduce or even manage uncertainty (to a degree); how can certainty be improved? Are there higher degrees of perfection? The answer is no. We reason from an uncertain starting point. Yet this effort presumes, as Mill and Wittgenstein postulated, that we can develop or acquire an increasing degree of belief or conviction that there is less potential for error in our reasoning or action. This position assumes that some way exists to identify what a lesser degree of uncertainty would look like.
:'''Knowledge professions acquire an increasing belief or conviction that there is less potential for error in their reasoning or action based on justified knowledge, favorable past experience, and reliable parameters/models.'''
Uncertainty is, in some form, amenable to description and explanation through observation and analysis. It is explainable and predictable to such a degree that it interests professionals such as scientists and engineers. Professional knowledge of this type has explanatory or predictive power, albeit limited in scope and application to relevant subjects such as physical objects or phenomena and functionality.
:'''The very act of reducing the scope of uncertainty to a limited set of phenomena implies choice and, ultimately, the decision to act in the face of such uncertainty, but acting or judging in this manner adds more value than doing the same under relative ignorance.'''
'''Uncertainty''', as described above, implies a hybrid nature. Examples of this are economic or decision-theoretic applications. In economics, [https://en.wikipedia.org/wiki/Perfect_information perfect information] allows one within the limiting framework of perfect competition in economic games to make decisions with 'perfect knowledge.' The crucial difference here is the distinction between '[https://en.wikipedia.org/wiki/Perfection perfection],' broadly, a state of completeness and lawlessness, and 'perfect, which can be thought of as an analytical limit that is approachable but can never be attained. Arbitrarily delimiting the realm of knowledge into defined, finite boundaries allows the simulated production of perfect choice, assumed crucially, to be free from error. The hybrid nature of this form of 'simulated' certainty produced within boundaries defined by assumptions and, therefore, clouded by doubt is itself a form of uncertainty. The importance of this is the semantical and logical association that exists between certainty and perfection versus the hybrid concept of a perfect anything, whether knowledge, infraction, competition, etc., or in the case of engineering, elastic, permeable, conductive, etc., being contained within an admittedly imperfect matrix of uncertainty.
:'''Simply put, a finite, heavily bounded piece of uncertain knowledge can be improved through the simulated use of [https://www.merriam-webster.com/dictionary/assumed assumedly] perfect information, but the perfection of knowledge cannot be attained or simulated.'''
Engineering and Economics, for example, simulate finite elements of perfect knowledge or information, heavily bounded with assumptions and a range of applications. By doing so, these professions reduce the bounds and variability of uncertainty and thereby manage it. Using these hybrid models to acquire knowledge takes place in an economy (regulated or institutional) and environment (transparent and structured), which puts pressure on the various professions to recognize and use qualitative and quantitative knowledge better to reduce uncertainties in their activities. The challenges in this reflect the nature of the profession's or discipline's knowledge (e.g., scientific, engineering, or medical), the policies and structure of the relevant professional knowledge bodies, process objectives, constraints, and "the elusive demands of politics." <ref>Walker, Vern R. "The siren songs of science: toward a taxonomy of scientific uncertainty for decisionmakers." Conn. L. Rev. 23 (1990): 567.</ref>
One common theme runs through all of these efforts to address the presence of uncertainty in professional activities. 
:"The available scientific information upon which a decision must be made is almost always a mixture of engineering knowledge and uncertainty. Regardless of the information or methodology, there is the potential for error. This is true regardless of the relevant science, whether archaeology or aeronautics, economics or engineering, pharmacology or toxicology or epidemiology. Achieving the best social decisions requires not only understanding and using what we know but also appreciating and weighing the extent of our uncertainty. In making the best use of ... information in ... decision-making, it is still true that the beginning of wisdom is knowing what it is we do not know." (Vern, Op. cit., Reformatted and engineering substituted for scientific) 
'''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty to develop explanations, calculate, and make predictions. This professional knowledge converges on but never reaches certainty, producing a "legitimacy" from the fruitfulness of its use.''' <ref>Randomness Is Unpredictability, Antony Eagle, The British Journal for the Philosophy of Science, Vol. 56, No. 4 (Dec., 2005), pp. 749-790 </ref> 
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=== Bias and Uncertainty ===
Up to this point, the concept of error, as outlined in uncertainty, could easily be simulated by a truly random variable. There should be no detectable differences around the unseen or specified statistical mean. However, several instances in experience point to a tendency towards one extreme or a pattern of error, a collective that is in itself an error of errors, namely [https://en.wikipedia.org/wiki/Bias bias].

Arguably, this could be part of the choice uncertainty discussion below, but it makes more sense to be looked at on its own. Acting in the face of uncertainty is an exercise of personal knowledge or experience. Whether using professional knowledge or individual experience, the potential is there to make a series of choices that, in some instances, make errors more likely than they would be if considered in the context of a statistical analysis.

This tendency for an individual to make the same error in a predictable or repeatable manner is termed bias. Personal experience, beliefs, knowledge, data, and models all have inherent built-in factors, errors, or defects that create predictable error patterns when applied in decision-making. Walker (1998) argues implicitly that such bias can also be considered systemic error. This type of uncertainty is difficult to identify except on theoretical grounds when the magnitude and direction of the bias are known. Most of these biases, whether systemic or knowledge-based, individual or practice-based, require disciplined efforts to "validate" such methods, models, and judgments at all layers while bounding the uncertainty.
: This is the challenge to professional knowledge that must be overcome, namely, developing and validating models and methods for managing bounded uncertainty that minimize this tendency towards repeatable groups of error or bias. Therefore, from this point forward, the discussion will refer to 'uncertainty and its proxies, error, and bias.'
===Propagating Error and Uncertainty===
Partitioning uncertainty and its proxies, error, and bias into logically distinct subsets involves or is associated with multiple-step execution or choice frameworks. Combining those measurements or choices into single parameters or choices involves, in some cases, assembling the data/information in a unique or limited range of sequences. This implies that associated errors in observing, calculating, or choosing will have unequal impacts or consequences depending on where in the chain the error occurs. Also implied in this is the possibility that subsequent errors will be causally linked to some degree. (In statistical practice, this depends upon whether the errors are independent of each other or correlated, specifically, co-variant.)
:'''Professions develop knowledge of how uncertainty and its proxies, error, and bias propagate through an ordered and normative series of observations or choices. This allows the profession to prioritize error and bias reduction to achieve an optimized reduction of uncertainty.'''
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===Cognition, Metacognition and Uncertainty===
The concept of choice and the resultant error in uncertainty can be further delimited depending on the role of [https://en.wikipedia.org/wiki/Cognition cognition], [https://en.wikipedia.org/wiki/Metacognition meta-cognition], and independent external elements. The rationale for this argument is that such choice activity in the face of uncertainty is a conscious, cognitive act.

An argument could be made that such cognition is a prerequisite for exercising choice and introducing error, as presented and discussed above. The counterfactual to this argument is the example of uninformed choice. Choices introduce as much as errors or more as informed choices could be made. (???) Choice, in this sense, is indifferent to knowledge. Purposeful choice in an environment of objectives requires knowledge, method, and experience, but uninformed, speculative choice does not.
:"''' Cognition''' is the set of all mental abilities and processes related to knowledge, attention, memory and working memory, judgment and evaluation, reasoning and "computation," problem-solving and decision making, comprehension and production of language, etc. Human cognition is conscious and unconscious, concrete or abstract, as well as intuitive (like knowledge of a language) and conceptual (like a model of a language). Cognitive processes use existing knowledge and generate new knowledge." (Wikipedia)
:"'''Metacognition''' is "cognition about cognition", or "knowing about knowing" and can take many forms. It includes knowledge about when and how to use particular strategies for learning or problem-solving. There are generally two aspects of [https://en.wikipedia.org/wiki/Metacognition metacognition]: knowledge about cognition and regulation of cognition."
Some types of metacognition knowledge are:
:'''Person knowledge''' (declarative knowledge), which is understanding one's own capabilities.
:'''Task knowledge''' (procedural knowledge), which is how one perceives the difficulty of a task, which is the content, length, and type of assignment.
:'''Strategic knowledge''' (conditional knowledge) is one's capability to use strategies to learn information. (Accessed at Wikipedia)
Like metacognitive knowledge, metacognitive regulation or "cognitive control" contains three essential skills.
:'''Planning:''' refers to the appropriate selection of strategies and the correct allocation of resources that affect task performance.
:'''Monitoring:''' refers to one's awareness of comprehension and task performance.
:'''Evaluating:''' refers to appraising the final product of a task and the efficiency at which the task was performed. This can include re-evaluating strategies that were used. (Accessed at Wikipedia)
Nothing has been said up to this point that limits this structure to an individual, or a collective, or two entities making cognitive choices that are incompatible or inconsistent to varying degrees. Interpretations of this scheme include an individual professional exercising judgment in a decision that, in turn, relies upon the collective judgment and decision of the discipline as an underlying basis versus the decision of another individual to oppose/protest or otherwise contest that decision. Thus, the framework of cognitive/meta-cognitive applies to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, economics of regulatory practice, and game theory.
:By solving useful problems in the face of inherent uncertainty, discipline-specific frameworks of cognitive/meta-cognitive activity apply to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, the economics of regulatory practice, and game theory.
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===Probability and Uncertainty===
Probability is a [https://en.wikipedia.org/wiki/Coherence_theory_of_truth coherent] approach to uncertainty in the physical and mathematics or "classical" domains such as engineering. [8] and is the "...is certainly the best-known and most widely used formalism for quantifying uncertainty.[9] [https://en.wikipedia.org/wiki/Cox%27s_theorem Cox’s theorem] ("Any measure of belief is [https://en.wikipedia.org/wiki/Isomorphism isomorphic] (but not necessarily equal) to a probability measure") is a well-known argument for the validity of that argument. [Note 5]

Another writer, [https://en.wikipedia.org/wiki/Frank_Knight Knight] (1921,1956), presented the following taxonomy of probabilities: [10]
: '''Classical 'a priori' probability:''' As an idealized model, numerical probabilities are computed based on generalized principles of assigning equal likelihoods and mutually exhaustive possible outcomes (Runde, 1998) to all set members. Such probabilities are assigned to "...outcomes based on a judgment of indifference between those outcomes, that is, based on the absence of any evidence of real influences in play that may render any one outcome more or less probable than any other." (Runde, op. cit.) This builds upon Knights's concept of an 'absolutely homogeneous classification of instances that are completely identical except for [https://en.wikipedia.org/wiki/Indeterminate_(variable) indeterminate] factors. This judgment of probability or logical probability is on the same logical plane as the propositions of mathematics and ultimately [https://en.wikipedia.org/wiki/Inductivism inductions] from experience.'(Knight, 1956, pg.225)
: '''Bayesian 'a priori' probability:''' In contrast to interpreting probability as the "frequency" or "propensity" of some phenomenon, [https://en.wikipedia.org/wiki/Bayesian_probability Bayesian probability] is a quantity that we assign to represent a state of knowledge or a state of belief. In this view, probability is assigned to a hypothesis, often using the basis of past or prior experience. In contrast, under the frequentist view, a hypothesis is typically tested without being assigned a probability based on prior experience. The Bayesian interpretation of probability can be seen as an extension of propositional logic that enables reasoning with hypotheses, i.e., propositions whose truth or falsity is uncertain. Bayesian probability belongs to the category of evidential probabilities; to evaluate the probability of a hypothesis, the Bayesian probabilist specifies some prior probability, which is then updated in the light of new, relevant data (evidence). The Bayesian interpretation provides a standard set of procedures and formulae to perform this calculation. [11]
: '''Statistical or 'a posterior probability:''' Empirical evaluation of the frequency of association between predicates, not analyzable into varying combinations of equally probable or logical, 'a priori' alternatives. Knight argued that any "high degree of confidence" that the judgment that experience will remain valid for future predictions is "...still based on an 'a priori" judgment of indeterminateness." (Knight, Op. Cit.) Knight's argument to support this is what will be discussed further below that first, "...the impossibility of eliminating all factors not really indeterminate; and, second, the impossibility of enumerating the equally probable alternatives involved and determining their mode of combination so as to evaluate the probability by a priori calculation." (Knight, Op. Cit.) Knight noted that the main difference between this form and that of logical or inductive probability was the presence of empirical information, which allowed the analyst to identify propensity and direction in the observed phenomena. In this case, 'a priori' probability values may be derived from first principles and "statistical probabilities are determined a posteriori by the empirical method of counting instances." (Runde [1998] quoting Knight, op. cit.)
:'''Estimates:''' The distinction here is that there is no valid basis ('a priori' or 'a posteriori') for any classifying phenomena. For Knight, this form of probability presented the greatest logical difficulties of all ... but its distinction from the other types must be emphasized, and some of its complicated relations indicated..." (pp. 224-5, emphasis in the original)

Arguably, Knight's concept of probability can be viewed as "..a continuum of probability situations, depending on the degree of homogeneity of the 'instances' in question" [12] ; i.e. going from a logically distinct but equally likely set of elements to a unique set with one member with statistical frequency in the middle. Knight's main motivation for distinguishing between a priori probability and statistical probability underscores his opinion that "(i) the 'mathematical or a priori type of probability is practically never met with in business, while the second is extremely common'; and (ii) 'the statistical treatment never gives closely accurate quantitative results' (Runde quoting Knight pp. 215-16). Runde argues that Knight couldn't conceive of (empirically tabulated) occurrences that are used in daily commerce classes of instances that we have to make do within the course of everyday economic life could be divided into subclasses of instances that are sufficiently homogeneous to permit the determination of what he calls 'real' probability (p. 217).
:'''In modeling degrees of uncertainty, any measure of belief is isomorphic but not necessarily equal to a probability or statistics measure'''.
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===Explanation, Prediction and Uncertainty===
It should be clear that the general notion of uncertainty synonymous with complete ignorance must be bounded as part of a scheme to produce a useful concept of uncertainty in professional practice such as civil engineering.
:The first principle that must be introduced is that such uncertainty must be bounded by reliable and valuable knowledge gained from past experience combined with analytical and computational capabilities to address an immediate and real problem of interest. This could be viewed as the economic interest argument. Only uncertainties associated with physical phenomena and economic scarcity will be addressed.
:The second principle is that the knowledge model is capable of producing statements or assertions in the form of hypotheses that themselves are testable. [https://en.wikipedia.org/wiki/Testability Testability], in this sense, is defined as the property applying to an empirical hypothesis and involves two components:
::The logical property that is variously described as contingency, defeasibility, or falsifiability, which means that counterexamples to the hypothesis are logically possible. Contrast this to a [https://en.wikipedia.org/wiki/Tautology_(logic) logical tautology], which is always true that is true in every possible interpretation. The practical feasibility of observing a reproducible series of such counterexamples if they do exist. In short, a hypothesis is testable if there is some real, non-zero expectation of deciding whether it is true or false of verifiable, reproducible experience. Upon this property of its constituent hypotheses rests the ability to decide whether a theory can be supported or falsified by actual experience data. (Source Wikipedia.)
'''Knowledge models must be capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria described above in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error and bias.'''
===Variability and Aleatory Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge referred to as epistemic uncertainty. Acting in the face of epistemic uncertainty introduces choice uncertainty (discussed below) and results in the production of explanations and predictions. It is implied that there will be testable hypotheses in the form of outcomes of interest to the knowledge professions. Inherent in nature is a variation of results, which may appear to uncertain but isn't. Observations of phenomena will not be the same, and part of the experimental process is to narrow those results down to within an acceptable level of tolerance. Statistical measures seek to identify underlying and unobserved measures of central tendency. These parameters are not directly observed but are derived to a degree of confidence.
In some cases, variation around a mean or within a statistical range may be perfectly acceptable to the knowledge profession. Examples of this are in the physical sciences and medicine. In Engineering and, to a lesser extent, economics, the process or phenomenon may be required to be more controlled to a narrower range than what a natural or "unmanaged" amount of variation would allow. Several courses of action present themselves.
:The first would be to ignore the aleatory phenomenon and choice, which is rejected outright.
:The second would be to assume that, in effect, such variation is itself a form of minimally managed uncertainty and treat it the same as epistemic uncertainty. This is not a desirable outcome as reducing Aleatory error and bias requires
:A third approach would be to acknowledge that process variation is, in some forms, manageable. This means making choices and acting in a way that presumes that the natural occurrence of error can be reduced, or more importantly, bias can be reduced. There is ample precedent for the third approach in civil engineering design and project management. In this sense, the argument is made that the aleatory response to managed efforts results in an elastic variation range. Presumably, an effective management effort either "shifts" the central tendency of the process results or reduces the variation range of occurrence or some other population parameter. Choosing to act and accept that narrower range rather than the broader natural variation may result in an outcome that, while well within the bounds of the expected variation, is outside the arbitrary control limits established by the choice and action. While this phenomenon mimics the broader uncertainty and its proxy error, it is not an error. The information on the variation was known, and the ability to produce narrower results was an error, albeit a pseudo-error, when compared to the broader classes of uncertainty discussed above, such as total ignorance.
::Looking at uncertainty using this third approach means that the error in choosing a new target parameter for this process may contain epistemic errors (our knowledge of the phenomenon may be erroneous, or the model flawed), aleatory uncertainties as discussed, or, more importantly, choice error or bias in thinking that we can move the process results to meet the requirements.
:'''In civil engineering practice, this may mean applying professional judgment in developing partitioning schemes or allocating uncertainty between the three dimensions (Epistemic, Aleatory, and Behavioral) of uncertainty.'''
Lastly, raising the topic like this brings the questions of effectiveness and efficiency to the fore. Advancing knowledge models that reduce error and bias at first are largely choice models resulting in justifications and favorable experiences. At some point in the process, the knowledge profession advances explanations and predictions that require reliable mathematical or logical concepts that are reduced to models and parameters. The question of efficiency becomes important; the broader uncertainty may have been reduced, but the process is economically inefficient. The profession is urged to become more frugal or, for example, reduce the variability of the outcomes. Knowledge professions often operate under conditions that require them to acquire knowledge and reduce uncertainty effectively and efficiently. Doing so requires understanding both epistemic uncertainty, as discussed above, and statistical process variation or aleatory variation.
:'''Knowledge professions manage and reduce uncertainty in the form of error and bias in an economically effective and efficient manner. This effort requires understanding both epistemic and aleatory uncertainty.'''
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===Choice and Behavioral Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge, referred to as epistemic uncertainty, as well as the inherent variations in results, or what was termed aleatory uncertainty. Acting in the face of that epistemic and aleatory uncertainty introduces yet another uncertainty into the process, namely choice uncertainty. This uncertainty reflects uncertainties associated with first-person and third-person choice. This is particularly relevant for engineering with project stakeholders.
===Summary of Semantics Framework for Uncertainty===
Professional disciplines have gradually re-framed their understanding of uncertainty and, thru experience and analysis, have arrived at the following meta-knowledge concepts of uncertainty:
* '''Uncertainty and its proxy, error, have three components: [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic epistemic uncertainty], [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic aleatory uncertainty], and behavioral uncertainty.'''
* '''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty using non-dogmatic beliefs, which recognize the existence of specific criteria and information that would make the believer change their beliefs using coherent approaches such as probability.'''
* '''Knowledge professions develop knowledge of how uncertainty and its proxy, error, propagate through an ordered and normative series of observations or choices that meet testability criteria in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error. An error can then be described, explained, and reduced in ways that uncertainty cannot. This allows the professions to prioritize error reduction to achieve an optimized reduction of uncertainty and produce a "legitimacy" from the fruitfulness of its use.'''
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===Uncertainty versus Risk/Hazard===
Up to this point, nothing has been said about risk or hazard. Now, risk can be defined. Simply put risk or hazard are finite, logically distinct subsets of the broader uncertainty. Risk and hazard are currently defined interchangeably. First and foremost, risk is a specific subset of general uncertainty. Any arbitrary subset of general uncertainty can be defined as a risk if it meets the following criteria:
* Uncertainty is associated with the potential for material error when acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena.
* Uncertainty and its proxy, error, can be partitioned into logically distinct subsets associated with multiple-step execution or choice frameworks in physical environments linked to a unique or limited range of sequences.
**Subcriteria 1:Resulting errors will have varying impacts or consequences depending on where the error occurs and the degree of causal linkage or influence in the chain.
**Subcriteria 2:Potential errors can be sequenced and prioritized to achieve an optimized reduction of error or bias and, by proxy, uncertainty over time.
* Uncertainty and its proxies, error bias, and environments are isomorphic to coherent approaches such as probability.
* Uncertainty and its proxies, error, and bias are isomorphic to coherent approaches such as knowledge models capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria in 'reasoning under uncertainty.'
This is a definition of general risk. There is no differentiation for engineering or economics, medicine or insurance. There is no difference between a 'good' risk consequence and a 'negative' one. Redefining general risk into simpler terms gives the following:
:'''General Risk is a subset of general uncertainty that is associated with the potential for material errors and biases when sequentially acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena; isomorphic to probability measures and methods and capable of testability.'''
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==Logical Framework for Specific Risk Taxonomies==
As noted above, at the most fundamental level, uncertainty is ".....the subjective perception of ignorance." [13] [Note 6] At this point in the discussion, the focus has moved from discussing uncertainty to its framed subset, risk.
:'''Further, the discussion moves from general risk to a professional or discipline-specific risk [https://en.wikipedia.org/wiki/Taxonomy taxonomy]. Beyond this point, risk will be used in place of uncertainty.'''
Han et al. note that, in general, "(t) taxonomies are valuable not only in their comprehensiveness but in their coherent reduction of uncertainty to conceptually discrete elements."
Uncertainty (and, by implication, risk or hazard) taxonomies offer an approach or tool to more precisely identify and define ontological uncertainty so that it can be quantified, analyzed, and communicated. Viewed this way, uncertainty is not a single, monolithic phenomenon but "...multi-dimensional with theoretically distinct domains and constructs that are potentially measurable and related to different outcomes, mechanisms of action, and management strategies." [14] Taxonomic schemes for classifying the different kinds of scientific and, by extension, engineering uncertainty require identifying the various kinds of potential error associated with descriptive scientific or engineering information and knowledge. An example of this is the following quote from a legal authority:
:"I would especially stress the need for an agency to disclose the uncertainty that surrounds its determinations. '''And by uncertainty, I mean the agency's ignorance as well as its quantitative estimates of error."''' (Emphasis added) [15]
'''Given the necessity of acting in the face of enormous uncertainties [16], discipline taxonomies must offer a clearly coherent and probabilistic approach to uncertainty that is sufficiently general in nature, logically distinct and exhaustive in scope and "...provide decision-makers with a foundation for understanding the nature of discipline information..."''' [17]
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=== Scientific Risk Taxonomies ===
The [https://en.wikipedia.org/wiki/Scientific_method scientific method] is a body of techniques for investigating phenomena in its environment, acquiring new knowledge, or correcting and integrating previous knowledge. Inherent in that process are uncertainties of many forms. Any one taxonomy of [https://en.wikipedia.org/wiki/Uncertainty scientific uncertainty] has largely been focused on statistical models used to assess and quantify sampling, errors, and parameters, although several different taxonomies are conceivable: note-24 [18]
====Environment-specific, Phenomena oriented, model-centric taxonomy of uncertainties====
** [https://en.wikipedia.org/wiki/Uncertainty_quantification#Sources_of_uncertainty '''Parameter uncertainty'''] the source of which is model parameters that are inputs to the computer model (mathematical model) but whose exact values are unknown and cannot be controlled, or whose values cannot be exactly inferred by statistical methods. Subsets of this type of uncertainty include but are not limited to:
***'''Experimental uncertainty''' is also known as observation error, which comes from the variability of experimental measurements. An example is repeating an experiment measurement several times using exactly the same settings for all inputs/variables and recording the variability. It is a subset of the larger uncertainty component, parameter uncertainty.
*** '''Parametric variability''' comes from the variability of the input variables of the phenomena model and is also a subset of the larger uncertainty component, parameter uncertainty.
** '''Structural uncertainty''', or model inadequacy, model [https://en.wikipedia.org/wiki/bias bias] or "[https://en.wikipedia.org/wiki/Systemic_bias systemic bias]", or model discrepancy, which comes from the lack of knowledge of the underlying true state of the phenomenon or its environment. It depends on how accurately a mathematical model describes the true state of the phenomenon or what is termed "model fitness". Due to the inherent nature of uncertainty in any knowledge body (scientific or engineering), models are only an approximation to reality. Therefore, any conclusions drawn from the model can be very misleading when the underlying basis is not plausible or lacks validity. '''note-25 [19]]'''
*** What is missing in this context is any concept of output variability due to the model itself. Part of this is due to the lack of recognition of "process" in scientific investigation. The concept of the scientific method started with a single investigator, such as Galileo or Michael Faraday, working in their laboratories. It has grown into planet and solar system scale experiments such as data gathered on planetary flybys such as Mars, Pluto, and Ceres to the hunt for the Higgs Boson. Ultimately, science has become more like engineering in the scale and complexity of its investigations and theory aggregates, such as the "Unified theory" of particle physics. Subsets of this type of uncertainty include but are not limited to:
*** '''Algorithmic uncertainty''', or numerical uncertainty, comes from numerical errors and numerical approximations per implementation of the computer model. Most models are too complicated to solve exactly. For example, the [https://en.wikipedia.org/wiki/Finite_element_method finite element method] or [https://en.wikipedia.org/wiki/Finite_difference_method finite difference method] [may be used to approximate a solution [https://en.wikipedia.org/wiki/Partial_differential_equation partial differential equation], which introduces numerical errors. Other examples are numerical integration and infinite sum truncation, which are necessary approximations in numerical implementation.
*** '''Interpolation uncertainty''' comes from a lack of available data collected from computer model simulations or experimental measurements. For other input settings that don't have simulation data or experimental measurements, one must interpolate or extrapolate to predict the corresponding responses.
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====Commentary====
A more comprehensive taxonomy of uncertainties that acknowledges knowledge, data, and linguistic uncertainties is:
*'''Epistemic uncertainty''' -[https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Epistemic uncertainty] or systematic uncertainty, in contrast, reflects limitations in the current “state of knowledge” underlying models themselves, originates from competing theories or models, is not readily quantifiable, and is manifest by subjective confusion or indecision. It includes uncertainty due to measurement limitations, insufficient data, extrapolations and interpolations, and variability over time and space. Epistemic uncertainty is uncertainty about "...some determinate fact ... because of a lack of complete information. '''cite_note-26 [20]]''' Epistemic uncertainty can be classified into six main types:'''cite_note-27 [21]]'''
*''' Measurement error''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Observational_error Measurement error] is uncertainty that manifests itself as (apparently) random variation in the measurement of a quantity. Repeated measurements will vary and demonstrate classic statistical behavior.
* '''Systematic error''' - [https://en.wikipedia.org/wiki/Observational_error Systematic error] occurs due to bias in the measuring equipment, model, analysis, observation, or sampling procedure. It is formally defined as the difference between the true value of the quantity of interest and the value to which the mean of the results converges as sample or data sizes increase. Unlike measurement error, it is not (apparently) random, and therefore, results subject to systematic error alone do not vary about a true value. Systematic error can result from deliberate judgment to exclude (or include) data, parameters, or models that ought not to be excluded (or included).
** "The only way to deal with systematic error is to recognize a bias in the process, model, or procedure and remove it. Systematic error, however, is notoriously difficult to recognize except on theoretical grounds. Corrections may only be applied when the magnitude and direction of the bias are known. Such corrections underlie the application of double-sampling methods in environmental science." (Regan, et. al., op. cit.)
*'''Natural variation''' - [https://en.wikipedia.org/wiki/Natural_process_variation Natural variation] or underlying process variation occurs in systems that "...change (concerning time, space, or other variables) in ways that are difficult to predict...across the full range of temporal and spatial values (or other related variables)." '''note-28 [22]'''
** This form of uncertainty is reduceable utilizing tools such as [https://en.wikipedia.org/wiki/Statistical_process_control statistical process control] and replication of experiments or observations.
*'''Inherent randomness''' - [https://en.wikipedia.org/wiki/Randomness randomness] or [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty aleatory uncertainty] exists because the system is"...in principle, irreducible to a deterministic one (the most well-known case is described by [https://en.wikipedia.org/wiki/Uncertainty_principle Heisenberg’s uncertainty principle] in quantum mechanics)." '''note-29 [23], note-30 [Note 7]'''
** Even if the argument is accepted that it is unlikely that any given physical system at some level is inherently random, it is important for any taxonomy to distinguish between systems that appear random due to incomplete or inconsistent information and those that are intrinsically random. (Again, see Regan (2002)).
*'''Model uncertainty''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling Model uncertainty] occurs in the process of generating a model as a conceptual representation of a particular phenomenon to create a [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling#Overview simplified reflection of reality]. This selectivity of factors and variables creates uncertainty in at least three ways. (Regan (2002))
** '''Variables and processes''' that are regarded as relevant often represent a trade-off between system knowledge and assumed states and model objectives;
** '''Models''' depict observed processes using logical or mathematical constructs based on underlying theories about system states or dynamics using continuous equations to describe discrete processes.
** '''Curve fitting''' (including interpolation and extrapolation) with mathematical expressions using model variables where inputs are discrete data points.
** Regan (2002) argues that '''model uncertainty''' is "...notoriously difficult to quantify and impossible to eliminate ...(and)...(t)he only reliable way of determining how appropriate a model is for prediction is to perform validation studies."
** Subjective judgment occurs due to the interpretation of scarce or error-prone data.
*** The only way to address this type of uncertainty is to "...assign a degree of belief about an event in the form of a subjective probability." (Regan (2002))
*Linguistic uncertainty - Linguistic uncertainty, or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. '''note-31 [24]], note-32 [25]]'''
** '''Context dependence''' is uncertainty concerning the context in which a statement is to be understood;
** '''Ambiguity''' occurs when words have multiple meanings, and further reduction to a single meaning is not logically possible in a given context.
** '''Underspecificity''' occurs in the presence of "unwanted generality" or multiple interpretations, and reduction to a smaller set or even a single interpretation is not logically possible.
*Stochastic or statistical uncertainty - Stochastic or statistical uncertainty, which is sometimes known as [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Aleatoric uncertainty] as well as the subject of [https://en.wikipedia.org/wiki/Stochastic_control stochastic control] pertains to the parameters of a risk model, originates from sampling or measurement error, and can be quantified and mathematically expressed (e.g., using confidence intervals).
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== Uncertainty Taxonomies in a Legal Environment ==
From a legal perspective, a definition of taxonomy is dictionary-based, such as "the systematic distinguishing, ordering, and naming of type groups within a subject field." given by Walker (op. cit.). Such legal concepts of scientific or engineering taxonomies are not required to be epistemically complete. Still, they must cover "the most significant aspects of ...(current, good discipline practices) ... and of the descriptive information commonly encountered by decision-makers" in a logically distinct manner. (Walker, 1991, p. 571) These taxonomies must be sufficient to catalog the types of uncertainty associated with descriptive assertion, including the critical subset of cause and effect assertions. This legal/decision-maker framework has presented descriptive uncertainty as having six components (Walker, 1991). Although not part of the Walker taxonomy, underlying the scheme is linguistic uncertainty, or "vagueness," as a source of uncertainty, including uncertainties due to context dependence, ambiguity, and under-specificity. '''note-33 [26]]''' Arguably, these uncertainties underlie the most basic element in the taxonomy.
* '''Linguistic '''- or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. (Regan, 2002)
* '''Conceptual'''- the definition by choice and design of descriptive concepts or variables to be used as [https://en.wikipedia.org/wiki/Predicate_(grammar) predicates] where the subject of an assertion identifies what is being discussed and the predicate provides information about the topic or characterizations such as what the subject is, what the subject is doing, or what the subject is like (Wikipedia).
* "Conceptual uncertainty, or the potential for conceptual error, arises whenever predication occurs. Whenever a concept is used to describe something, using certain concepts instead of others begins to structure how we understand the object, event, or instance under discussion. Predication or conceptualization generates useful information about things, but it also can inhibit our ability to think about those things with concepts other than those selected. The concepts used may not be the most fruitful or the best designed- either for scientific purposes or for making wise, fair, effective, and efficient decisions." (Edited for reading, Walker, 1991)
* Similarly, choice and design of descriptive variables enhance and restrict data set membership, creating potentially incomplete, incompatible, or inconsistent data sets, "...the classification categories employed, and the relationships among those categories (nominal, ordinal or scalar)." '''note-34 [27]]'''
* Concept uncertainty '''note-35 [28]''' like systematic error discussed above is notoriously difficult to recognize, except on theoretical grounds, and the magnitude and direction of the bias are known.
Both terms could be analogized to that of [https://en.wikipedia.org/wiki/Accuracy_and_precision#Terminology_of_ISO_5725 accuracy] in [https://www.bipm.org/en/publications/guides/ ISO 5725] which looks at the ability of the concept or system's ability to produce results that are proximate to the "true" value or state of the phenomenon. Conceptual uncertainty and systemic bias are related to another measure of uncertainty: validity. Walker has defined validity as the ability of a concept to quantitatively describe and explain what the phenomenon objective "truly" is. Walker also ties the concepts together when he states, "Validity concerns the "accuracy" of the measurement data, not its precision." (Walker, 1998) 
Additionally, conceptual uncertainty and associated validity issues could be propagated throughout this taxonomy. Every component of action and decision in the face of uncertainty raises validity issues. Walker recognized this when the author defined epistemic choice for concepts used throughout the other uncertainty components or layers.
* '''Measurement''' uncertainty or Misclassification error- the application of the underlying concepts or variables to specific, individual cases and the uncertainty of the reliability (Walker, 1998) of the resultant data. (It is important to remember that assessment observation and measurement are used interchangeably in this analysis.) Thus, an assessment method or procedure is considered "reliable" or "precise" in the scientific terms of ISO 5715 if it repeatedly produces consistent results.
* Measurement uncertainty is logically distinct from conceptual uncertainty, which can only occur after any conceptual uncertainty has been established. Still, misclassification can occur even when the conceptual uncertainty has been minimized. Conversely, underlying errors or errors made in measurement or classification propagate thru the reasoning chain and reduce the quality or value of the final result.
* While uncertainties associated with quantitative measurements or assessments can be statistically evaluated and described, uncertainties related to qualitative assessments are more problematic.
**Working through the taxonomic chain from base concept to causal explanation requires amassing and integrating qualitative information and quantitative data into input parameters for mathematical models. Developing procedures for producing consistent, repeatable sets of information from assessments that minimize conceptual error or systemic bias requires professional efforts to "validate" such methods, models, and judgments at all layers in the process. Validation as a process is something that civil engineering as a practice needs to embrace more frequently.
* '''[https://en.wikipedia.org/wiki/Sampling Sampling]'''- in the classical statistics sense, is the selection of a limited subset of a larger population to use as a basis for making estimates about the population in the form of attributes, proprieties, or parameters. The resulting information would be determinative in making forecasts about future population sampling or possessing a desired amount of predictive power. The uncertainty about the ability of this information to correctly predict future samples is termed sampling error.
* '''Modeling'''- Modeling uncertainty arises whenever a claim is made that out of a class of candidate relationships and variable pairings, one variable pairing inclusive of constants is chosen that possesses a particular or persistent mathematical relationship to another variable X. Modeling errors arise in selecting the wrong variable pair and constants or by incorrectly specifying its constants. (Walker, 1991, pg. 599)
** '''Causal'''- In causal modeling and analysis, the relationship between causal variables is not strictly a mathematical function. Still, it makes testable predictions and explains "...how a system of variables works, why a system works the way it does, or why it makes sense to think of certain variables as a system" at all." (Walker, 1991, pg. 609)
** '''Epistemic'''- the choice of interpretations for fundamental, logical concepts used throughout the other components or layers.
The components of this uncertainty taxonomy can combine in different ways to "...produce different aggregate uncertainties." '''note-36 [29]'''
:''' Only the scientific and engineering professions possess the capacity to develop and validate procedures and models that combine the different kinds of qualitative '''note-37 [30]''' and quantitative information and their associated potential for error into an aggregate measure or "scalar variables" of uncertainty.'''note-38 [31]]
Scalar variables are quantitative variables whose categories are related by some measure of the relevant property's incremental frequency, degree, or amount. (Walker, 1991) Examples are Modulus of Elasticity, Compression stress in elastic materials, etc.

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=== Economics Uncertainty Taxonomies ===
Like scientific concepts of uncertainty, economics is environment-specific (in this case, read sector of economy or markets), phenomenon-oriented (in this case, read human activity and manufactured phenomena such as firms and markets guided by human judgment and decision), and model-centric.
:''' Economics is not interested in all forms of uncertainty, only specific, meaningful subsets possessing casual connections with material impacts, often labeled as 'risks'.'''
One of its earlier writers ([https://en.wikipedia.org/wiki/Frank_Knight#Life_and_career Knight], 1921, 1948, 1957) attempted to define "uncertainty" as the presence of "defects of managerial knowledge" (or [https://en.wikipedia.org/wiki/Knightian_uncertainty knightian uncertainty] and thus risk centric) as risk that is immeasurable, not possible to calculate.(Wikipedia) Risk in this sense was defined as "the ordinary risks of business activity which can ... be reduced... by applying the insurance principle." Knight does acknowledge that risk is a specific and unambiguous subset of uncertainty. (Risk, Uncertainty and Profit, 1957, pg. 19) For Knight, measuring uncertainty meant measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences, which Knight referred to as "the imperfection of knowledge". (Risk, Uncertainty and Profit, 1957, pg. 197) One of Knight's many contributions to this analysis was recognizing information and knowledge's role in economic activity.
:"''(W)e are concerned only to emphasize the fact that '''knowledge is in a sense variable in degree and that the practical problem may relate to the degree of knowledge rather than to its presence or absence in toto.''' We live only by knowing something about the future, while the problems of life, or conduct at least, arise from knowing so little. This is as true of business as of other spheres of activity. The situation's essence is action according to opinion, of greater or less foundation and value, neither entire ignorance nor complete and perfect information, but partial knowledge. If we are to understand the workings of the economic system, we must examine the meaning and significance of uncertainty; and to this end, some inquiry into the nature and function of knowledge itself is necessary.''" (Knight, pg. 199) Emphasis added
Knight also reinforced the difficulties in classifying uncertainties when he analyzed life insurance. In this case, accidental death was an uncertain phenomenon compared to sickness and accident, where an "...objective description and classification of cases was impossible..." (Knight, pg. 248) Another factor that made uncertainties ineligible as risks and, therefore, uninsurable because they were unclassifiable was the exercise of judgment in making decisions by the businessman. (Knight, pg. 251)
This is a simple classification of economic uncertainty and, as an idealization, is philosophically controversial. '''note-39 [32]'''
* Uncertainty and information about the economic environment are distinct from uncertainty about others’ behavior or choices.
* Risk as a specific subset of uncertainty implies that "...all possible acts are known, all possible outcomes arising from each act are known, and it is possible to assign probabilities to each act." '''note-40 [33]'''
Like the economic [https://en.wikipedia.org/wiki/Theory_of_the_firm theory of the firm], an economic theory of uncertainty seeks to explain and predict the nature of a specific subset of uncertainty (in this case, risk relating to physical phenomena and human activity), including its behavior, structure, and relationship to the universal uncertainty of physical phenomena. In this sense, uncertainty, like firms and markets, can establish price equilibriums under the right circumstances different from those that might have occurred in either of the other two. Such a theory of uncertainty offers a partitioning scheme that decomposes uncertainty into distinct and meaningful subsets, of which risk is a significant one. It also explains how the presence and variability of knowledge cause or " drive" economic consequences. Unlike physical phenomena, economic activity creates artificial objects such as products, firms, mediums of exchange, markets, economies, and knowledge about those activities. The economics of uncertainty exists as an alternative to "certainty" models that assume away imperfect knowledge and unknown choice/judgment preferences when it is more efficient for economic decision-making. This also allows the introduction of the logical concept of "hazard" versus "risk". Frank used hazard extensively in Risk, Uncertainty, and Profit, but not to the extent that one could say it was interchangeable. An example of this is in the term "[https://en.wikipedia.org/wiki/Moral_hazard Moral hazard]," where a firm could engage in "riskier" and potentially "uninsurable" behavior if the information were transparent, and, therefore, insurance coverage of losses is limited. note-41 [34]], note-42 [35]]

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=== Uncertainty and Risk in Policy and Regulatory Environment ===
:''"Hume's 'just reasoner', when faced with a difficult public policy decision in current times, would probably commission a risk analysis. But how could they incorporate 'a degree of doubt, caution, and modesty?"'' '''note-43 [36]'''
The problem of "decision-making in the face of uncertainty" is how to regulate based on incomplete information that has the potential to be materially inaccurate. A contextual assumption is that we need to evaluate decision rules for dealing with uncertainty. As a social enterprise, risk regulation, whatever its substantive objectives, should be as '''effective, efficient, and equitable''' as practically possible. These "three E's" form a set of "process objectives" or "meta-goals." From the uncertainty standpoint, causal information can be usefully divided into two major categories: information about groups and information about individuals.

Each category, group, and individual has its distinctive types of inherent uncertainty. These are logically distinct, generally independent, and cumulative, contributing to '''(?????)'''

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=== Engineering Uncertainty Taxonomies ===
Like scientific and economic concepts of uncertainty, engineering is environment-specific, phenomena-oriented, model-centric, external choice, and driven by decision-making in the face of uncertainty. Like economics and unlike science, engineering is not interested in all forms of uncertainty, only those with determinable and material consequences. Like economics, engineering conceives risk as a specific and unambiguous subset of uncertainty determined by measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences. Also, engineering recognizes the role information and, by extension, knowledge plays in constructing the built environment.
'''note-44 [37]]''' Like economics, engineering differs from economics and scientific uncertainty in its interest in behavioral uncertainty.
*"Behavioral or interaction uncertainty is how individuals or organizations act or interact. Behavioral uncertainty arises from four sources: design uncertainty, requirement uncertainty, volitional uncertainty, and human errors." '''note-45 [38]]'''
** A design uncertainty is a choice among alternatives over which an individual or group of individuals exercises direct control but has not yet decided upon.
** Requirement uncertainty includes parameters of interest to and determined by the stakeholder, independent of the engineer or designer.
** Volitional uncertainty is uncertainty about what the subject him/herself will decide. Other people’s future actions and conduct are not entirely predictable, particularly when dealing with other organizations.
** Human errors occur during the development of a system or project due to blunders or mistakes by an individual or individuals.

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==Practice Frameworks for Uncertainty and Risk in Civil Engineering Practice==
===PMIBoK===
PMI does not define uncertainty, but the PMI Body of Knowledge uses the term in 45 places (uncertainty) and as describing properties in 8 places (uncertainties). [See Note 8] PMI does define "risk" and uses the term over 1,300 times in the same document. In its glossary, PMI also defines terms such as "threat" and "opportunity" but not events.[391 The BoK contextually defines uncertainty when it states that project risk has its origins in the uncertainty present in all projects. (Op. Cit., pg 309) 
'''Uncertainty''' is contextually defined and taken as having the property of affecting project execution and the ability to meet stakeholder expectations. Uncertainty is more than the sum of all individual risks (known and unknown). The nature of this uncertainty is not defined, only its capacity to affect something else or its properties. 
'''Risk''' is defined as " ... an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives." (Ibid.) Beyond this formal definition, PMI adds context when, in its risk management section (Sec. 11 ), the BoK states that risk is a caused event or condition with multiple causes and impacts. The BoK even offers specific techniques for mapping cause-and-effect relationships and influence diagrams. (Sec. 11.2.2.5) Not as evident but equally important is the recognition that not only is risk caused but the impact is "triggered". The risk trigger is an event or situation that signals that it is about to occur (Glossary, pg. 566). Risk conditions are factors that affect or otherwise contribute to risk, such as project stakeholders. Risk implicitly retains some residual element of probability in it, or a value of less than 1.0 with risk that approaches a level of near certainty is termed "issues" or "realized risk" .(Op. Cit., pg 309). PMI also links risk to underlying variations in project outcomes. (Ibid.) Risk is based upon data and information which can be assessed as to its quality (Sec. 11.3.2.3). This analysis looks to determine the value of the information and examines the degree to which the risk information is understood as well as its " ... accuracy, quality, reliability and integrity ... " (Ibid.) Part of that is understanding the relationship between occurrence and impact as outlined in PMl's Figure 11-10 Risk Impact Matrix and developing a model for prioritizing risk products and setting the threshold for action.
====Commentary:====
For defining and identifying risk, PMI notes in Sec. 11.2.2.4 that " ... Every project and its plan is conceived and developed based on a set of hypotheses, scenarios, or assumptions." Part of the risk analysis process is identifying risks to the project, such as inaccuracy, instability, inconsistency, or incompleteness of assumptions. Similarly, the quality of management plans, as well as their consistency with others and the project objectives and assumptions are 11 •• .indicators of risk in the project." (Sec. 11.2.2.1) 
Similarly, the role of engineering economics in risk is embedded or implied in the PMI definition of risk when the BoK discusses risk identification as a process of assessing which risks out of the total risk exposure for the project "may affect" the project and distilling their characteristics. Embedded in this statement is the concept that not all risks can cause economic impacts (positive and negative) to the project. Also embedded is the requirement to understand risk characteristics to identify project risk (Cost and schedule). Lastly, risk identification is an iterative process of incrementing project risk documentation, much like project scope, cost, and schedule documentation. 
Lastly, PMI acknowledges the complex constitution of risk aggregates or the "sum" of all risks in its material, but is it a sum? Risk information has descriptive and explanatory content. These are knowledge and meta-knowledge components. Understanding project assumptions is project-specific knowledge, but identifying risks from assumption inaccuracy, instability, inconsistency, or incompleteness requires professional or program meta-knowledge. 
'''Uncertainty exists in all projects, but risk, a subset of uncertainty with material economic impacts, is a key meta-knowledge element for civil engineering practice.'''
===Software Development Process- Risk Focused===
The Unified Process requires the project team to focus on addressing the most critical risks early in the project life
cycle. The deliverables of each iteration, especially in the Elaboration phase, must be selected to ensure that the greatest risks are addressed first. <ref> Booch, Grady, Ivar Jacobson, and James Rumbaugh. "The unified software development process."
Reading: Addison Wesley (1999).</ref>
===American Society of Civil Engineers (ASCE)===
ASCE does not define uncertainty, but the ASCE CE Body of Knowledge (CEBoK) uses the term in 22 places (uncertainty) and describes properties in 13 places (uncertainties). 
The CEBoK does not use the phrase "uncertainty and risk" as PMI and AACE do in their publication. ASCE reverses the two (Risk and uncertainty) in twelve places regarding technical outcomes (Outcome 12). A fundamental difference between PMI and ISO is that risk is considered a subset of uncertainty. ASCE does not define "risk" but uses the term 25 times in the same document. Almost all are in the context of variation of design parameters and not in the classical context of cost, schedule uncertainty, and risk.
====Commentary:====
...
=== American Association of Cost Engineers (AACE) ===
AACE defined uncertainty, risk, and other related terms in its Risk Management Dictionary. <ref>Risk Management Committee uRisk Management Dictionary, Cost Engineering Vol. 37/No. 10, OCTOBER 1995. </ref>
AACE's overall strategy was to define risk and other terms that stem from base uncertainty. AACE first defined uncertainty as .... "(t)he total range of events that may happen and produce risks (including both threats and opportunities) affecting a project (see opportunities, events, conditions, risk, and threats" where the following sub-definitions apply:
** Biases--A lack of objectivity based on the individual's position or perspective. Systematic and predictable relationships between a person's opinion or statement and his/her underlying knowledge or circumstances. Note: there may be "system biases" as well as "individual biases."
** Condition (Uncertain Condition)-Any specific identifiable circumstance (such as the rate of inflation or the quality of labor available) that might affect the outcome of the project
** Event (Uncertain Condition)-is a specific identifiable action (such as a large government project being started in the same labor area as your project) or an act of nature that might happen and that (if it does happen) could affect the outcome of the project.
** Opportunities are Uncertain events that could improve the results or improve the probability that the desired outcome will happen
** Threats are Uncertain events that are potentially negative or reduce the probability that the desired outcome will happen.
*** AACE defines Risk as an "ambiguous term" (sic) that is synonymous with uncertainty or a negative subset such as "threats", or an overall negative impact of all possible uncertainties.
====Commentary:====
...
==Limitations of the definition==
...
==Working definition==
...
==Beneficial outcomes==
...
==See also==
Wiki articles on the project.

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==Notes==
<references group="note" />
<li> Implicit in this definition of uncertainty as a "state of' is ... </li>
: " ... a conceptualization of uncertainty as a subjective, cognitive experience of people--a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one's lack of knowledge, without which one could not feel uncertain; '''uncertainty is a form of "meta-cognition" ... ( or alternatively, its main component, meta-knowledge) ... -a knowing about knowing.'''" (Han, 2011, op. cit., Emphasis added)
</ol>
<ol start="5">
<li> Cox wanted his system to satisfy the following conditions:</li>
: Divisibility and comparability- The plausibility of a statement is a real number and depends on the information we have related to the statement.
: Common sense - Plausibilities should vary sensibly with the assessment of plausibilities in the model.
: Consistency - If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). -preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf
</ol>
<ol start="6">
<li> The utility of existing taxonomies of uncertainty in civil engineering and, by extension, "risk" has been unsatisfactory for this very reason. This is particularly true in cases where the source of the uncertainty is conflicting versus incomplete information. See Regan, et. al.,"A taxonomy and treatment of uncertainty for ecology and conservation biology" (2002)
for a survey of scientific taxonomies for uncertainty.</li>
<li> Regan et al. argue that " ... genuine examples of this kind of uncertainty are hard to find. Even classic cases of random experiments like coin tosses and the throwing of dice are deterministic; it is just that we do not have enough information about the dynamic processes and initial conditions to make any sensible estimates about the outcomes. Such processes are, for all intents and purposes, inherently random, but they are not genuinely inherently random. For similar reasons, complex systems such as ecosystems and weather patterns are unlikely to be inherently random. Similarly, chaotic systems are entirely deterministic. They are unpredictable because the deterministic processes generating them and the relevant initial conditions are hard
to fully specify (see Stewart 1989, Sugihara et al. 1990)." (Regan (2002) </li>
<li> The CEBoK uses the phrase "uncertainty and risk" twice in reference to scheduling (Section 6.5,2) and cost estimating (Section 7.2,2) as well as reversing the two (Risk and Uncertainty) also in two places in discussing project life cycle (both in Section 2.4.1) </li>
</ol>

==References==
<references />

Uncertainty and Risk in Civil Engineering Practice

2024-11-24T16:27:26Z

WikiSysop: /* Certainty and Uncertainty */

'''Life is short and the art long; the occasion instant, experiment perilous, decision difficult.''' (Hippocrates as quoted in Fox,)
:''See also [[Project Definition]] , [[Project Life Cycle and Phase Models]] , [[Project Delivery Methods]]''
:''See also [[Technical Outcomes for Civil Engineering:Risk and Uncertainty]]''
==Basic Considerations==
As human beings,'' ... being alive means seeking opportunities and taking risks.'' As knowledge professionals
living in the 21st century, this means coping with an increasingly complex number of uncertainties for humans
living in this environment. We seek to understand better how these uncertainties can be characterized and
managed. The essence of this article and the experience for engineers in general and civil engineers in particular is
that manageable uncertainty is, by definition, termed risk, and its kindred cousin is termed hazard. This causes us to experience the
:" ''... human dread of and fascination for risk and the increasingly important role of risk analysis within societies ...'' " <ref name="McDaniels"> McDaniels, Timothy, and Mitchell Small. Risk analysis and society: an interdisciplinary characterization of the field. Cambridge University Press, 2004, page 1 </ref> (Ibid.)
and, by extension, civil engineering. McDaniels et al. argue that risk management has been
fundamental to our social and governance development for the past 10,000 years. The scale and shape of the
uncertainties faced in this period shaped the societies that have developed today. The central thrust of this effort
over the centuries has been to reshape and re-frame our understanding and conception of uncertainty from one of
complete unknowing and simple acceptance as our fate in life to one of management (cf "''Against the Gods''"
concept in Bernstein's book <ref name = Bernstein">Bernstein, Peter L., and Jesse Boggs. Against the gods. Simon & Schuster 1997.</ref>.
: ''All the knowledge professions and disciplines have struggled with managing uncertainty, for it is impossible to manage unbounded uncertainty.'' <ref group="note">See McDaniels et al. 2004 for an extensive discussion and bibliography on the historical development of Risk Analysis and some key milestones in risk analysis in the 20th century.</ref>
As outlined below, professions such as civil engineering have successfully acted in the face of uncertainty. It lies in the profession's ability to reduce a wide variety of uncertainties into increasingly smaller and crucially bounded subsets that can be managed. These are called 'risks. More recently, this process has evolved, and individual disciplines such as Civil Engineering (CE) have developed their knowledge and models to perform risk analysis. Risk is also taught as a distinct discipline and specialty practice within civil engineering. Still, it has some unique features that set it apart from other more classical practices within CE. As such, it is one of the first of some very specialized CE practices that use knowledge about civil engineering knowledge, or meta-knowledge. Other examples of civil engineering metaknowledge are project controls and quality controls.
==Semantic, Epistemic and Logical frameworks==
The semantic, epistemic, and logical frameworks for uncertainty and risk have several dimensions and layers of logical frameworks. The semantics problems include interchangeable usages for risk and uncertainty and hazard, uncertain and imperfect, and a lack of definitional material or context. The semantical scheme for this article will be to proceed from the distinction between certainty and uncertainty through marginal refinements and reductions of uncertainty up to the point of causal uncertainties. Beyond this point of semantics will be the logic frameworks or the subject of taxonomies of professional knowledge. First, it is about simple, testable phenomena, and then, it moves on to complex taxonomy schemes for artificially constructed phenomena or engineering projects.
<ref group="note">TBD</ref>
==Semantics framework==
===Certainty and Uncertainty===
''''' There is no such thing as absolute certainty, but there is assurance sufficient for the purposes of human life.''''' ([https://en.wikipedia.org/wiki/John_Stuart_Mill John Stuart Mill]) 
'''''If you tried to doubt everything, you would not get as far as doubting anything. The game of doubting itself presupposes certainty.''''' ([https://en.wikipedia.org/wiki/Ludwig_Wittgenstein Wittgenstein] # 115 from On [https://en.wikipedia.org/wiki/On_Certainty Certainty]) 
It is important to note that the references to epistemic or epidemiological knowledge in this article are assumed to relate to three forms of knowledge, namely:
* Knowledge that ( [https://en.wikipedia.org/wiki/Descriptive_knowledge descriptive or declarative or propositional knowledge])
* Knowledge how ( or "[https://en.wikipedia.org/wiki/Procedural_knowledge know-how]"), and
* [https://en.wikipedia.org/wiki/Knowledge_by_acquaintance Knowledge by acquaintance].
'''Certainty''' has been defined as "an epistemic property" of knowledge in all its forms and the state of our beliefs about that knowledge.
<ref>Certainty, Stanford Encyclopedia of Philosophy, accessed on September 12, 2015, at http://plato.stanford.edu/entries/certainty/#ConCer </ref>
Certainty about any belief about knowledge implies that it is not subject to doubt or skepticism. This immunity to criticism can be dogmatically based, emphasizing the importance of a propositional-based sense of truth over experiential, sensory perceptions. <ref>Definition of [https://en.wikipedia.org/wiki/Dogmatic_theology Dogmatic theology or belief], accessed at Wikipedia</ref>
: '''The demarcation line between dogmatic and non-dogmatic beliefs lies in the presence and recognition of specific criteria and information that would make the believer change their beliefs.'''
An empirical framework of [https://en.wikipedia.org/wiki/Testability testability] and [https://en.wikipedia.org/wiki/Falsifiability falsification] is required to recognize this and limit dogmatism. For example, one could argue that people would change their minds if God asked them to. Similarly, one could construct a concept of epistemic as opposed to dogmatic belief certainty as the ability to know anything that one chooses to know and can be known or [https://en.wikipedia.org/wiki/Omniscience inherent omniscience].

A second kind of certainty is epistemic, when conviction reflects the highest possible support for a belief. In this sense, knowledge is separate from beliefs, although someone may have beliefs about a property, such as certainty of knowledge. Logically, it has been shown that there will be unprovable statements within the system for any such knowledge system. Secondly, the knowledge system cannot demonstrate its own consistency. ([https://en.wikipedia.org/wiki/Gödel%27s_incompleteness_theorems Gödel's incompleteness theorems])
: '''Certainty, in real life, is useless or often damaging (the idea is that "total security from error" is impossible in practice, and a complete "lack of doubt" is undesirable)''' ([[https://en.wikipedia.org/wiki/Certainty Physicist Carlo Rovelli]])
'''Uncertainty''', on the other hand, arises immediately in the slightest amount of doubt or criticism.
<ref group="note"> The Merriam-Webster dictionary defines uncertainty as “the quality or state of being uncertain,” which is something of a circular definition. Likewise, one could define uncertainty as the state of not being certain.

Synonyms are distrust, doubt, misgiving, mistrust, reservation, skepticism, and suspicion. Another writer added indefinite, indeterminate, not certain to occur, problematical, unreliable, untrustworthy, unknown beyond doubt, dubious, doubtful, not clearly identified or defined, not constant, variable, and fitful to the list.
:(Han, Paul KJ, William MP Klein, and Neeraj K. Arora. "Varieties of Uncertainty in Health Care: A Conceptual Taxonomy." Medical Decision Making 31.6 (2011): 828-838.)
Han et al. noted that any definition of uncertainty clearly encompasses "...numerous types, sources, and manifestations of uncertainty, and ...(any) ...useful working definition of uncertainty needs to specify the concept underlying these varied meanings of the term."</ref>
Implicit in this definition of uncertainty as a “state of” is ...
"...a conceptualization of uncertainty as a subjective, cognitive experience of people—a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one’s lack of knowledge, without which one could not feel uncertain; uncertainty is a form of “meta-cognition” ...(or its main component, meta-knowledge)... —a knowing about knowing." (Han, 2011, op. cit., Emphasis added)[Note 3]
Similarly, uncertainty could be defined as "...any departure from the unachievable goal of complete [https://en.wikipedia.org/wiki/Determinism determinism]." <ref name="Reference5">[5]</ref> Reasoning under uncertainty is very different than performing the same under certainty. In reasoning under certainty, one has complete knowledge and deduces without doubt and equally important, without limitation, thereby concluding free from error. Reasoning under uncertainty, one works in a state of incomplete, inconsistent, and limited knowledge; doubts cloud any statements or assertions and thereby taint any deductions/inferences resulting in the potential for error. <ref name="Note4">[Note 4]</ref>
:'''Knowledge professions reason in a state of incomplete, inconsistent and limited knowledge with doubts that cloud any statements or assertions and taint any deductions/inferences resulting in the potential for error.'''
:''' It is also important to note the semantical and logical correlation between 'reasoning under uncertainty' or 'acting in the face of uncertainty' with the 'potential for' or 'presence of' error. The presence of uncertainty is invariably linked to the potential presence of error.''' The [https://en.wikipedia.org/wiki/Contingency_(philosophy) contingent] nature of uncertainty logically implies the contingent nature of error.
:'''Knowledge Professions use error as a proxy for uncertainty such that within discipline knowledge frameworks, uncertainty can be managed.''' The rationale for this belief is that error can be reliably described, quantified, explained, and ultimately reduced in ways that uncertainty cannot.

'''Uncertainty''' implies a range of variation that is impossible in certainty. Concepts and definitions of uncertainty are unbounded and vast but start from the point of complete and total ignorance. In describing uncertainty, statements can range from complete ignorance to relatively high degrees of belief that there is less potential for error in our reasoning or action. The latter is based upon confidence in justified knowledge, favorable past experience, and reliable parameters/models. One can't make that statement about ranges in certainty. If certainty, by definition, precludes doubt of any type or nature, then how could that be graduated to any degree? 
Uncertainty, on the other hand, can be reduced through disciplined efforts to acquire knowledge. In short, we can reduce or even manage uncertainty (to a degree); how can certainty be improved? Are there higher degrees of perfection? The answer is no. We reason from an uncertain starting point. Yet this effort presumes, as Mill and Wittgenstein postulated, that we can develop or acquire an increasing degree of belief or conviction that there is less potential for error in our reasoning or action. This position assumes that some way exists to identify what a lesser degree of uncertainty would look like.
:'''Knowledge professions acquire an increasing belief or conviction that there is less potential for error in their reasoning or action based on justified knowledge, favorable past experience, and reliable parameters/models.'''
Uncertainty is, in some form, amenable to description and explanation through observation and analysis. It is explainable and predictable to such a degree that it interests professionals such as scientists and engineers. Professional knowledge of this type has explanatory or predictive power, albeit limited in scope and application to relevant subjects such as physical objects or phenomena and functionality.
:'''The very act of reducing the scope of uncertainty to a limited set of phenomena implies choice and, ultimately, the decision to act in the face of such uncertainty, but acting or judging in this manner adds more value than doing the same under relative ignorance.'''
'''Uncertainty''', as described above, implies a hybrid nature. Examples of this are economic or decision-theoretic applications. In economics, [https://en.wikipedia.org/wiki/Perfect_information perfect information] allows one within the limiting framework of perfect competition in economic games to make decisions with 'perfect knowledge.' The crucial difference here is the distinction between '[https://en.wikipedia.org/wiki/Perfection perfection],' broadly, a state of completeness and lawlessness, and 'perfect, which can be thought of as an analytical limit that is approachable but can never be attained. Arbitrarily delimiting the realm of knowledge into defined, finite boundaries allows the simulated production of perfect choice, assumed crucially, to be free from error. The hybrid nature of this form of 'simulated' certainty produced within boundaries defined by assumptions and, therefore, clouded by doubt is itself a form of uncertainty. The importance of this is the semantical and logical association that exists between certainty and perfection versus the hybrid concept of a perfect anything, whether knowledge, infraction, competition, etc., or in the case of engineering, elastic, permeable, conductive, etc., being contained within an admittedly imperfect matrix of uncertainty.
:'''Simply put, a finite, heavily bounded piece of uncertain knowledge can be improved through the simulated use of [https://www.merriam-webster.com/dictionary/assumed assumedly] perfect information, but the perfection of knowledge cannot be attained or simulated.'''
Engineering and Economics, for example, simulate finite elements of perfect knowledge or information, heavily bounded with assumptions and a range of applications. By doing so, these professions reduce the bounds and variability of uncertainty and thereby manage it. Using these hybrid models to acquire knowledge takes place in an economy (regulated or institutional) and environment (transparent and structured), which puts pressure on the various professions to recognize and use qualitative and quantitative knowledge better to reduce uncertainties in their activities. The challenges in this reflect the nature of the profession's or discipline's knowledge (e.g., scientific, engineering, or medical), the policies and structure of the relevant professional knowledge bodies, process objectives and constraints, and "the elusive demands of politics." [6] One common theme runs through all of these efforts to address the presence of uncertainty in professional activities. 
:"The available scientific information upon which a decision must be made is almost always a mixture of engineering knowledge and uncertainty. Regardless of the information or methodology, there is the potential for error. This is true regardless of the relevant science, whether archaeology or aeronautics, economics or engineering, pharmacology or toxicology or epidemiology. Achieving the best social decisions requires not only understanding and using what we know but also appreciating and weighing the extent of our uncertainty. In making the best use of ... information in ... decision-making, it is still true that the beginning of wisdom is knowing what it is we do not know." (Vern, Op. cit., Reformatted and engineering substituted for scientific) 
'''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty to develop explanations, calculate, and make predictions. This professional knowledge converges on but never reaches certainty, producing a "legitimacy" from the fruitfulness of its use.''' <ref>Randomness Is Unpredictability, Antony Eagle, The British Journal for the Philosophy of Science, Vol. 56, No. 4 (Dec., 2005), pp. 749-790 </ref> 
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=== Bias and Uncertainty ===
Up to this point, the concept of error, as outlined in uncertainty, could easily be simulated by a truly random variable. There should be no detectable differences around the unseen or specified statistical mean. However, several instances in experience point to a tendency towards one extreme or a pattern of error, a collective that is in itself an error of errors, namely [https://en.wikipedia.org/wiki/Bias bias].

Arguably, this could be part of the choice uncertainty discussion below, but it makes more sense to be looked at on its own. Acting in the face of uncertainty is an exercise of personal knowledge or experience. Whether using professional knowledge or individual experience, the potential is there to make a series of choices that, in some instances, make errors more likely than they would be if considered in the context of a statistical analysis.

This tendency for an individual to make the same error in a predictable or repeatable manner is termed bias. Personal experience, beliefs, knowledge, data, and models all have inherent built-in factors, errors, or defects that create predictable error patterns when applied in decision-making. Walker (1998) argues implicitly that such bias can also be considered systemic error. This type of uncertainty is difficult to identify except on theoretical grounds when the magnitude and direction of the bias are known. Most of these biases, whether systemic or knowledge-based, individual or practice-based, require disciplined efforts to "validate" such methods, models, and judgments at all layers while bounding the uncertainty.
: This is the challenge to professional knowledge that must be overcome, namely, developing and validating models and methods for managing bounded uncertainty that minimize this tendency towards repeatable groups of error or bias. Therefore, from this point forward, the discussion will refer to 'uncertainty and its proxies, error, and bias.'
===Propagating Error and Uncertainty===
Partitioning uncertainty and its proxies, error, and bias into logically distinct subsets involves or is associated with multiple-step execution or choice frameworks. Combining those measurements or choices into single parameters or choices involves, in some cases, assembling the data/information in a unique or limited range of sequences. This implies that associated errors in observing, calculating, or choosing will have unequal impacts or consequences depending on where in the chain the error occurs. Also implied in this is the possibility that subsequent errors will be causally linked to some degree. (In statistical practice, this depends upon whether the errors are independent of each other or correlated, specifically, co-variant.)
:'''Professions develop knowledge of how uncertainty and its proxies, error, and bias propagate through an ordered and normative series of observations or choices. This allows the profession to prioritize error and bias reduction to achieve an optimized reduction of uncertainty.'''
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===Cognition, Metacognition and Uncertainty===
The concept of choice and the resultant error in uncertainty can be further delimited depending on the role of [https://en.wikipedia.org/wiki/Cognition cognition], [https://en.wikipedia.org/wiki/Metacognition meta-cognition], and independent external elements. The rationale for this argument is that such choice activity in the face of uncertainty is a conscious, cognitive act.

An argument could be made that such cognition is a prerequisite for exercising choice and introducing error, as presented and discussed above. The counterfactual to this argument is the example of uninformed choice. Choices introduce as much as errors or more as informed choices could be made. (???) Choice, in this sense, is indifferent to knowledge. Purposeful choice in an environment of objectives requires knowledge, method, and experience, but uninformed, speculative choice does not.
:"''' Cognition''' is the set of all mental abilities and processes related to knowledge, attention, memory and working memory, judgment and evaluation, reasoning and "computation," problem-solving and decision making, comprehension and production of language, etc. Human cognition is conscious and unconscious, concrete or abstract, as well as intuitive (like knowledge of a language) and conceptual (like a model of a language). Cognitive processes use existing knowledge and generate new knowledge." (Wikipedia)
:"'''Metacognition''' is "cognition about cognition", or "knowing about knowing" and can take many forms. It includes knowledge about when and how to use particular strategies for learning or problem-solving. There are generally two aspects of [https://en.wikipedia.org/wiki/Metacognition metacognition]: knowledge about cognition and regulation of cognition."
Some types of metacognition knowledge are:
:'''Person knowledge''' (declarative knowledge), which is understanding one's own capabilities.
:'''Task knowledge''' (procedural knowledge), which is how one perceives the difficulty of a task, which is the content, length, and type of assignment.
:'''Strategic knowledge''' (conditional knowledge) is one's capability to use strategies to learn information. (Accessed at Wikipedia)
Like metacognitive knowledge, metacognitive regulation or "cognitive control" contains three essential skills.
:'''Planning:''' refers to the appropriate selection of strategies and the correct allocation of resources that affect task performance.
:'''Monitoring:''' refers to one's awareness of comprehension and task performance.
:'''Evaluating:''' refers to appraising the final product of a task and the efficiency at which the task was performed. This can include re-evaluating strategies that were used. (Accessed at Wikipedia)
Nothing has been said up to this point that limits this structure to an individual, or a collective, or two entities making cognitive choices that are incompatible or inconsistent to varying degrees. Interpretations of this scheme include an individual professional exercising judgment in a decision that, in turn, relies upon the collective judgment and decision of the discipline as an underlying basis versus the decision of another individual to oppose/protest or otherwise contest that decision. Thus, the framework of cognitive/meta-cognitive applies to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, economics of regulatory practice, and game theory.
:By solving useful problems in the face of inherent uncertainty, discipline-specific frameworks of cognitive/meta-cognitive activity apply to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, the economics of regulatory practice, and game theory.
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===Probability and Uncertainty===
Probability is a [https://en.wikipedia.org/wiki/Coherence_theory_of_truth coherent] approach to uncertainty in the physical and mathematics or "classical" domains such as engineering. [8] and is the "...is certainly the best-known and most widely used formalism for quantifying uncertainty.[9] [https://en.wikipedia.org/wiki/Cox%27s_theorem Cox’s theorem] ("Any measure of belief is [https://en.wikipedia.org/wiki/Isomorphism isomorphic] (but not necessarily equal) to a probability measure") is a well-known argument for the validity of that argument. [Note 5]

Another writer, [https://en.wikipedia.org/wiki/Frank_Knight Knight] (1921,1956), presented the following taxonomy of probabilities: [10]
: '''Classical 'a priori' probability:''' As an idealized model, numerical probabilities are computed based on generalized principles of assigning equal likelihoods and mutually exhaustive possible outcomes (Runde, 1998) to all set members. Such probabilities are assigned to "...outcomes based on a judgment of indifference between those outcomes, that is, based on the absence of any evidence of real influences in play that may render any one outcome more or less probable than any other." (Runde, op. cit.) This builds upon Knights's concept of an 'absolutely homogeneous classification of instances that are completely identical except for [https://en.wikipedia.org/wiki/Indeterminate_(variable) indeterminate] factors. This judgment of probability or logical probability is on the same logical plane as the propositions of mathematics and ultimately [https://en.wikipedia.org/wiki/Inductivism inductions] from experience.'(Knight, 1956, pg.225)
: '''Bayesian 'a priori' probability:''' In contrast to interpreting probability as the "frequency" or "propensity" of some phenomenon, [https://en.wikipedia.org/wiki/Bayesian_probability Bayesian probability] is a quantity that we assign to represent a state of knowledge or a state of belief. In this view, probability is assigned to a hypothesis, often using the basis of past or prior experience. In contrast, under the frequentist view, a hypothesis is typically tested without being assigned a probability based on prior experience. The Bayesian interpretation of probability can be seen as an extension of propositional logic that enables reasoning with hypotheses, i.e., propositions whose truth or falsity is uncertain. Bayesian probability belongs to the category of evidential probabilities; to evaluate the probability of a hypothesis, the Bayesian probabilist specifies some prior probability, which is then updated in the light of new, relevant data (evidence). The Bayesian interpretation provides a standard set of procedures and formulae to perform this calculation. [11]
: '''Statistical or 'a posterior probability:''' Empirical evaluation of the frequency of association between predicates, not analyzable into varying combinations of equally probable or logical, 'a priori' alternatives. Knight argued that any "high degree of confidence" that the judgment that experience will remain valid for future predictions is "...still based on an 'a priori" judgment of indeterminateness." (Knight, Op. Cit.) Knight's argument to support this is what will be discussed further below that first, "...the impossibility of eliminating all factors not really indeterminate; and, second, the impossibility of enumerating the equally probable alternatives involved and determining their mode of combination so as to evaluate the probability by a priori calculation." (Knight, Op. Cit.) Knight noted that the main difference between this form and that of logical or inductive probability was the presence of empirical information, which allowed the analyst to identify propensity and direction in the observed phenomena. In this case, 'a priori' probability values may be derived from first principles and "statistical probabilities are determined a posteriori by the empirical method of counting instances." (Runde [1998] quoting Knight, op. cit.)
:'''Estimates:''' The distinction here is that there is no valid basis ('a priori' or 'a posteriori') for any classifying phenomena. For Knight, this form of probability presented the greatest logical difficulties of all ... but its distinction from the other types must be emphasized, and some of its complicated relations indicated..." (pp. 224-5, emphasis in the original)

Arguably, Knight's concept of probability can be viewed as "..a continuum of probability situations, depending on the degree of homogeneity of the 'instances' in question" [12] ; i.e. going from a logically distinct but equally likely set of elements to a unique set with one member with statistical frequency in the middle. Knight's main motivation for distinguishing between a priori probability and statistical probability underscores his opinion that "(i) the 'mathematical or a priori type of probability is practically never met with in business, while the second is extremely common'; and (ii) 'the statistical treatment never gives closely accurate quantitative results' (Runde quoting Knight pp. 215-16). Runde argues that Knight couldn't conceive of (empirically tabulated) occurrences that are used in daily commerce classes of instances that we have to make do within the course of everyday economic life could be divided into subclasses of instances that are sufficiently homogeneous to permit the determination of what he calls 'real' probability (p. 217).
:'''In modeling degrees of uncertainty, any measure of belief is isomorphic but not necessarily equal to a probability or statistics measure'''.
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===Explanation, Prediction and Uncertainty===
It should be clear that the general notion of uncertainty synonymous with complete ignorance must be bounded as part of a scheme to produce a useful concept of uncertainty in professional practice such as civil engineering.
:The first principle that must be introduced is that such uncertainty must be bounded by reliable and valuable knowledge gained from past experience combined with analytical and computational capabilities to address an immediate and real problem of interest. This could be viewed as the economic interest argument. Only uncertainties associated with physical phenomena and economic scarcity will be addressed.
:The second principle is that the knowledge model is capable of producing statements or assertions in the form of hypotheses that themselves are testable. [https://en.wikipedia.org/wiki/Testability Testability], in this sense, is defined as the property applying to an empirical hypothesis and involves two components:
::The logical property that is variously described as contingency, defeasibility, or falsifiability, which means that counterexamples to the hypothesis are logically possible. Contrast this to a [https://en.wikipedia.org/wiki/Tautology_(logic) logical tautology], which is always true that is true in every possible interpretation. The practical feasibility of observing a reproducible series of such counterexamples if they do exist. In short, a hypothesis is testable if there is some real, non-zero expectation of deciding whether it is true or false of verifiable, reproducible experience. Upon this property of its constituent hypotheses rests the ability to decide whether a theory can be supported or falsified by actual experience data. (Source Wikipedia.)
'''Knowledge models must be capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria described above in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error and bias.'''
===Variability and Aleatory Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge referred to as epistemic uncertainty. Acting in the face of epistemic uncertainty introduces choice uncertainty (discussed below) and results in the production of explanations and predictions. It is implied that there will be testable hypotheses in the form of outcomes of interest to the knowledge professions. Inherent in nature is a variation of results, which may appear to uncertain but isn't. Observations of phenomena will not be the same, and part of the experimental process is to narrow those results down to within an acceptable level of tolerance. Statistical measures seek to identify underlying and unobserved measures of central tendency. These parameters are not directly observed but are derived to a degree of confidence.
In some cases, variation around a mean or within a statistical range may be perfectly acceptable to the knowledge profession. Examples of this are in the physical sciences and medicine. In Engineering and, to a lesser extent, economics, the process or phenomenon may be required to be more controlled to a narrower range than what a natural or "unmanaged" amount of variation would allow. Several courses of action present themselves.
:The first would be to ignore the aleatory phenomenon and choice, which is rejected outright.
:The second would be to assume that, in effect, such variation is itself a form of minimally managed uncertainty and treat it the same as epistemic uncertainty. This is not a desirable outcome as reducing Aleatory error and bias requires
:A third approach would be to acknowledge that process variation is, in some forms, manageable. This means making choices and acting in a way that presumes that the natural occurrence of error can be reduced, or more importantly, bias can be reduced. There is ample precedent for the third approach in civil engineering design and project management. In this sense, the argument is made that the aleatory response to managed efforts results in an elastic variation range. Presumably, an effective management effort either "shifts" the central tendency of the process results or reduces the variation range of occurrence or some other population parameter. Choosing to act and accept that narrower range rather than the broader natural variation may result in an outcome that, while well within the bounds of the expected variation, is outside the arbitrary control limits established by the choice and action. While this phenomenon mimics the broader uncertainty and its proxy error, it is not an error. The information on the variation was known, and the ability to produce narrower results was an error, albeit a pseudo-error, when compared to the broader classes of uncertainty discussed above, such as total ignorance.
::Looking at uncertainty using this third approach means that the error in choosing a new target parameter for this process may contain epistemic errors (our knowledge of the phenomenon may be erroneous, or the model flawed), aleatory uncertainties as discussed, or, more importantly, choice error or bias in thinking that we can move the process results to meet the requirements.
:'''In civil engineering practice, this may mean applying professional judgment in developing partitioning schemes or allocating uncertainty between the three dimensions (Epistemic, Aleatory, and Behavioral) of uncertainty.'''
Lastly, raising the topic like this brings the questions of effectiveness and efficiency to the fore. Advancing knowledge models that reduce error and bias at first are largely choice models resulting in justifications and favorable experiences. At some point in the process, the knowledge profession advances explanations and predictions that require reliable mathematical or logical concepts that are reduced to models and parameters. The question of efficiency becomes important; the broader uncertainty may have been reduced, but the process is economically inefficient. The profession is urged to become more frugal or, for example, reduce the variability of the outcomes. Knowledge professions often operate under conditions that require them to acquire knowledge and reduce uncertainty effectively and efficiently. Doing so requires understanding both epistemic uncertainty, as discussed above, and statistical process variation or aleatory variation.
:'''Knowledge professions manage and reduce uncertainty in the form of error and bias in an economically effective and efficient manner. This effort requires understanding both epistemic and aleatory uncertainty.'''
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===Choice and Behavioral Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge, referred to as epistemic uncertainty, as well as the inherent variations in results, or what was termed aleatory uncertainty. Acting in the face of that epistemic and aleatory uncertainty introduces yet another uncertainty into the process, namely choice uncertainty. This uncertainty reflects uncertainties associated with first-person and third-person choice. This is particularly relevant for engineering with project stakeholders.
===Summary of Semantics Framework for Uncertainty===
Professional disciplines have gradually re-framed their understanding of uncertainty and, thru experience and analysis, have arrived at the following meta-knowledge concepts of uncertainty:
* '''Uncertainty and its proxy, error, have three components: [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic epistemic uncertainty], [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic aleatory uncertainty], and behavioral uncertainty.'''
* '''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty using non-dogmatic beliefs, which recognize the existence of specific criteria and information that would make the believer change their beliefs using coherent approaches such as probability.'''
* '''Knowledge professions develop knowledge of how uncertainty and its proxy, error, propagate through an ordered and normative series of observations or choices that meet testability criteria in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error. An error can then be described, explained, and reduced in ways that uncertainty cannot. This allows the professions to prioritize error reduction to achieve an optimized reduction of uncertainty and produce a "legitimacy" from the fruitfulness of its use.'''
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===Uncertainty versus Risk/Hazard===
Up to this point, nothing has been said about risk or hazard. Now, risk can be defined. Simply put risk or hazard are finite, logically distinct subsets of the broader uncertainty. Risk and hazard are currently defined interchangeably. First and foremost, risk is a specific subset of general uncertainty. Any arbitrary subset of general uncertainty can be defined as a risk if it meets the following criteria:
* Uncertainty is associated with the potential for material error when acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena.
* Uncertainty and its proxy, error, can be partitioned into logically distinct subsets associated with multiple-step execution or choice frameworks in physical environments linked to a unique or limited range of sequences.
**Subcriteria 1:Resulting errors will have varying impacts or consequences depending on where the error occurs and the degree of causal linkage or influence in the chain.
**Subcriteria 2:Potential errors can be sequenced and prioritized to achieve an optimized reduction of error or bias and, by proxy, uncertainty over time.
* Uncertainty and its proxies, error bias, and environments are isomorphic to coherent approaches such as probability.
* Uncertainty and its proxies, error, and bias are isomorphic to coherent approaches such as knowledge models capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria in 'reasoning under uncertainty.'
This is a definition of general risk. There is no differentiation for engineering or economics, medicine or insurance. There is no difference between a 'good' risk consequence and a 'negative' one. Redefining general risk into simpler terms gives the following:
:'''General Risk is a subset of general uncertainty that is associated with the potential for material errors and biases when sequentially acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena; isomorphic to probability measures and methods and capable of testability.'''
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==Logical Framework for Specific Risk Taxonomies==
As noted above, at the most fundamental level, uncertainty is ".....the subjective perception of ignorance." [13] [Note 6] At this point in the discussion, the focus has moved from discussing uncertainty to its framed subset, risk.
:'''Further, the discussion moves from general risk to a professional or discipline-specific risk [https://en.wikipedia.org/wiki/Taxonomy taxonomy]. Beyond this point, risk will be used in place of uncertainty.'''
Han et al. note that, in general, "(t) taxonomies are valuable not only in their comprehensiveness but in their coherent reduction of uncertainty to conceptually discrete elements."
Uncertainty (and, by implication, risk or hazard) taxonomies offer an approach or tool to more precisely identify and define ontological uncertainty so that it can be quantified, analyzed, and communicated. Viewed this way, uncertainty is not a single, monolithic phenomenon but "...multi-dimensional with theoretically distinct domains and constructs that are potentially measurable and related to different outcomes, mechanisms of action, and management strategies." [14] Taxonomic schemes for classifying the different kinds of scientific and, by extension, engineering uncertainty require identifying the various kinds of potential error associated with descriptive scientific or engineering information and knowledge. An example of this is the following quote from a legal authority:
:"I would especially stress the need for an agency to disclose the uncertainty that surrounds its determinations. '''And by uncertainty, I mean the agency's ignorance as well as its quantitative estimates of error."''' (Emphasis added) [15]
'''Given the necessity of acting in the face of enormous uncertainties [16], discipline taxonomies must offer a clearly coherent and probabilistic approach to uncertainty that is sufficiently general in nature, logically distinct and exhaustive in scope and "...provide decision-makers with a foundation for understanding the nature of discipline information..."''' [17]
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=== Scientific Risk Taxonomies ===
The [https://en.wikipedia.org/wiki/Scientific_method scientific method] is a body of techniques for investigating phenomena in its environment, acquiring new knowledge, or correcting and integrating previous knowledge. Inherent in that process are uncertainties of many forms. Any one taxonomy of [https://en.wikipedia.org/wiki/Uncertainty scientific uncertainty] has largely been focused on statistical models used to assess and quantify sampling, errors, and parameters, although several different taxonomies are conceivable: note-24 [18]
====Environment-specific, Phenomena oriented, model-centric taxonomy of uncertainties====
** [https://en.wikipedia.org/wiki/Uncertainty_quantification#Sources_of_uncertainty '''Parameter uncertainty'''] the source of which is model parameters that are inputs to the computer model (mathematical model) but whose exact values are unknown and cannot be controlled, or whose values cannot be exactly inferred by statistical methods. Subsets of this type of uncertainty include but are not limited to:
***'''Experimental uncertainty''' is also known as observation error, which comes from the variability of experimental measurements. An example is repeating an experiment measurement several times using exactly the same settings for all inputs/variables and recording the variability. It is a subset of the larger uncertainty component, parameter uncertainty.
*** '''Parametric variability''' comes from the variability of the input variables of the phenomena model and is also a subset of the larger uncertainty component, parameter uncertainty.
** '''Structural uncertainty''', or model inadequacy, model [https://en.wikipedia.org/wiki/bias bias] or "[https://en.wikipedia.org/wiki/Systemic_bias systemic bias]", or model discrepancy, which comes from the lack of knowledge of the underlying true state of the phenomenon or its environment. It depends on how accurately a mathematical model describes the true state of the phenomenon or what is termed "model fitness". Due to the inherent nature of uncertainty in any knowledge body (scientific or engineering), models are only an approximation to reality. Therefore, any conclusions drawn from the model can be very misleading when the underlying basis is not plausible or lacks validity. '''note-25 [19]]'''
*** What is missing in this context is any concept of output variability due to the model itself. Part of this is due to the lack of recognition of "process" in scientific investigation. The concept of the scientific method started with a single investigator, such as Galileo or Michael Faraday, working in their laboratories. It has grown into planet and solar system scale experiments such as data gathered on planetary flybys such as Mars, Pluto, and Ceres to the hunt for the Higgs Boson. Ultimately, science has become more like engineering in the scale and complexity of its investigations and theory aggregates, such as the "Unified theory" of particle physics. Subsets of this type of uncertainty include but are not limited to:
*** '''Algorithmic uncertainty''', or numerical uncertainty, comes from numerical errors and numerical approximations per implementation of the computer model. Most models are too complicated to solve exactly. For example, the [https://en.wikipedia.org/wiki/Finite_element_method finite element method] or [https://en.wikipedia.org/wiki/Finite_difference_method finite difference method] [may be used to approximate a solution [https://en.wikipedia.org/wiki/Partial_differential_equation partial differential equation], which introduces numerical errors. Other examples are numerical integration and infinite sum truncation, which are necessary approximations in numerical implementation.
*** '''Interpolation uncertainty''' comes from a lack of available data collected from computer model simulations or experimental measurements. For other input settings that don't have simulation data or experimental measurements, one must interpolate or extrapolate to predict the corresponding responses.
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====Commentary====
A more comprehensive taxonomy of uncertainties that acknowledges knowledge, data, and linguistic uncertainties is:
*'''Epistemic uncertainty''' -[https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Epistemic uncertainty] or systematic uncertainty, in contrast, reflects limitations in the current “state of knowledge” underlying models themselves, originates from competing theories or models, is not readily quantifiable, and is manifest by subjective confusion or indecision. It includes uncertainty due to measurement limitations, insufficient data, extrapolations and interpolations, and variability over time and space. Epistemic uncertainty is uncertainty about "...some determinate fact ... because of a lack of complete information. '''cite_note-26 [20]]''' Epistemic uncertainty can be classified into six main types:'''cite_note-27 [21]]'''
*''' Measurement error''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Observational_error Measurement error] is uncertainty that manifests itself as (apparently) random variation in the measurement of a quantity. Repeated measurements will vary and demonstrate classic statistical behavior.
* '''Systematic error''' - [https://en.wikipedia.org/wiki/Observational_error Systematic error] occurs due to bias in the measuring equipment, model, analysis, observation, or sampling procedure. It is formally defined as the difference between the true value of the quantity of interest and the value to which the mean of the results converges as sample or data sizes increase. Unlike measurement error, it is not (apparently) random, and therefore, results subject to systematic error alone do not vary about a true value. Systematic error can result from deliberate judgment to exclude (or include) data, parameters, or models that ought not to be excluded (or included).
** "The only way to deal with systematic error is to recognize a bias in the process, model, or procedure and remove it. Systematic error, however, is notoriously difficult to recognize except on theoretical grounds. Corrections may only be applied when the magnitude and direction of the bias are known. Such corrections underlie the application of double-sampling methods in environmental science." (Regan, et. al., op. cit.)
*'''Natural variation''' - [https://en.wikipedia.org/wiki/Natural_process_variation Natural variation] or underlying process variation occurs in systems that "...change (concerning time, space, or other variables) in ways that are difficult to predict...across the full range of temporal and spatial values (or other related variables)." '''note-28 [22]'''
** This form of uncertainty is reduceable utilizing tools such as [https://en.wikipedia.org/wiki/Statistical_process_control statistical process control] and replication of experiments or observations.
*'''Inherent randomness''' - [https://en.wikipedia.org/wiki/Randomness randomness] or [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty aleatory uncertainty] exists because the system is"...in principle, irreducible to a deterministic one (the most well-known case is described by [https://en.wikipedia.org/wiki/Uncertainty_principle Heisenberg’s uncertainty principle] in quantum mechanics)." '''note-29 [23], note-30 [Note 7]'''
** Even if the argument is accepted that it is unlikely that any given physical system at some level is inherently random, it is important for any taxonomy to distinguish between systems that appear random due to incomplete or inconsistent information and those that are intrinsically random. (Again, see Regan (2002)).
*'''Model uncertainty''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling Model uncertainty] occurs in the process of generating a model as a conceptual representation of a particular phenomenon to create a [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling#Overview simplified reflection of reality]. This selectivity of factors and variables creates uncertainty in at least three ways. (Regan (2002))
** '''Variables and processes''' that are regarded as relevant often represent a trade-off between system knowledge and assumed states and model objectives;
** '''Models''' depict observed processes using logical or mathematical constructs based on underlying theories about system states or dynamics using continuous equations to describe discrete processes.
** '''Curve fitting''' (including interpolation and extrapolation) with mathematical expressions using model variables where inputs are discrete data points.
** Regan (2002) argues that '''model uncertainty''' is "...notoriously difficult to quantify and impossible to eliminate ...(and)...(t)he only reliable way of determining how appropriate a model is for prediction is to perform validation studies."
** Subjective judgment occurs due to the interpretation of scarce or error-prone data.
*** The only way to address this type of uncertainty is to "...assign a degree of belief about an event in the form of a subjective probability." (Regan (2002))
*Linguistic uncertainty - Linguistic uncertainty, or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. '''note-31 [24]], note-32 [25]]'''
** '''Context dependence''' is uncertainty concerning the context in which a statement is to be understood;
** '''Ambiguity''' occurs when words have multiple meanings, and further reduction to a single meaning is not logically possible in a given context.
** '''Underspecificity''' occurs in the presence of "unwanted generality" or multiple interpretations, and reduction to a smaller set or even a single interpretation is not logically possible.
*Stochastic or statistical uncertainty - Stochastic or statistical uncertainty, which is sometimes known as [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Aleatoric uncertainty] as well as the subject of [https://en.wikipedia.org/wiki/Stochastic_control stochastic control] pertains to the parameters of a risk model, originates from sampling or measurement error, and can be quantified and mathematically expressed (e.g., using confidence intervals).
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== Uncertainty Taxonomies in a Legal Environment ==
From a legal perspective, a definition of taxonomy is dictionary-based, such as "the systematic distinguishing, ordering, and naming of type groups within a subject field." given by Walker (op. cit.). Such legal concepts of scientific or engineering taxonomies are not required to be epistemically complete. Still, they must cover "the most significant aspects of ...(current, good discipline practices) ... and of the descriptive information commonly encountered by decision-makers" in a logically distinct manner. (Walker, 1991, p. 571) These taxonomies must be sufficient to catalog the types of uncertainty associated with descriptive assertion, including the critical subset of cause and effect assertions. This legal/decision-maker framework has presented descriptive uncertainty as having six components (Walker, 1991). Although not part of the Walker taxonomy, underlying the scheme is linguistic uncertainty, or "vagueness," as a source of uncertainty, including uncertainties due to context dependence, ambiguity, and under-specificity. '''note-33 [26]]''' Arguably, these uncertainties underlie the most basic element in the taxonomy.
* '''Linguistic '''- or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. (Regan, 2002)
* '''Conceptual'''- the definition by choice and design of descriptive concepts or variables to be used as [https://en.wikipedia.org/wiki/Predicate_(grammar) predicates] where the subject of an assertion identifies what is being discussed and the predicate provides information about the topic or characterizations such as what the subject is, what the subject is doing, or what the subject is like (Wikipedia).
* "Conceptual uncertainty, or the potential for conceptual error, arises whenever predication occurs. Whenever a concept is used to describe something, using certain concepts instead of others begins to structure how we understand the object, event, or instance under discussion. Predication or conceptualization generates useful information about things, but it also can inhibit our ability to think about those things with concepts other than those selected. The concepts used may not be the most fruitful or the best designed- either for scientific purposes or for making wise, fair, effective, and efficient decisions." (Edited for reading, Walker, 1991)
* Similarly, choice and design of descriptive variables enhance and restrict data set membership, creating potentially incomplete, incompatible, or inconsistent data sets, "...the classification categories employed, and the relationships among those categories (nominal, ordinal or scalar)." '''note-34 [27]]'''
* Concept uncertainty '''note-35 [28]''' like systematic error discussed above is notoriously difficult to recognize, except on theoretical grounds, and the magnitude and direction of the bias are known.
Both terms could be analogized to that of [https://en.wikipedia.org/wiki/Accuracy_and_precision#Terminology_of_ISO_5725 accuracy] in [https://www.bipm.org/en/publications/guides/ ISO 5725] which looks at the ability of the concept or system's ability to produce results that are proximate to the "true" value or state of the phenomenon. Conceptual uncertainty and systemic bias are related to another measure of uncertainty: validity. Walker has defined validity as the ability of a concept to quantitatively describe and explain what the phenomenon objective "truly" is. Walker also ties the concepts together when he states, "Validity concerns the "accuracy" of the measurement data, not its precision." (Walker, 1998) 
Additionally, conceptual uncertainty and associated validity issues could be propagated throughout this taxonomy. Every component of action and decision in the face of uncertainty raises validity issues. Walker recognized this when the author defined epistemic choice for concepts used throughout the other uncertainty components or layers.
* '''Measurement''' uncertainty or Misclassification error- the application of the underlying concepts or variables to specific, individual cases and the uncertainty of the reliability (Walker, 1998) of the resultant data. (It is important to remember that assessment observation and measurement are used interchangeably in this analysis.) Thus, an assessment method or procedure is considered "reliable" or "precise" in the scientific terms of ISO 5715 if it repeatedly produces consistent results.
* Measurement uncertainty is logically distinct from conceptual uncertainty, which can only occur after any conceptual uncertainty has been established. Still, misclassification can occur even when the conceptual uncertainty has been minimized. Conversely, underlying errors or errors made in measurement or classification propagate thru the reasoning chain and reduce the quality or value of the final result.
* While uncertainties associated with quantitative measurements or assessments can be statistically evaluated and described, uncertainties related to qualitative assessments are more problematic.
**Working through the taxonomic chain from base concept to causal explanation requires amassing and integrating qualitative information and quantitative data into input parameters for mathematical models. Developing procedures for producing consistent, repeatable sets of information from assessments that minimize conceptual error or systemic bias requires professional efforts to "validate" such methods, models, and judgments at all layers in the process. Validation as a process is something that civil engineering as a practice needs to embrace more frequently.
* '''[https://en.wikipedia.org/wiki/Sampling Sampling]'''- in the classical statistics sense, is the selection of a limited subset of a larger population to use as a basis for making estimates about the population in the form of attributes, proprieties, or parameters. The resulting information would be determinative in making forecasts about future population sampling or possessing a desired amount of predictive power. The uncertainty about the ability of this information to correctly predict future samples is termed sampling error.
* '''Modeling'''- Modeling uncertainty arises whenever a claim is made that out of a class of candidate relationships and variable pairings, one variable pairing inclusive of constants is chosen that possesses a particular or persistent mathematical relationship to another variable X. Modeling errors arise in selecting the wrong variable pair and constants or by incorrectly specifying its constants. (Walker, 1991, pg. 599)
** '''Causal'''- In causal modeling and analysis, the relationship between causal variables is not strictly a mathematical function. Still, it makes testable predictions and explains "...how a system of variables works, why a system works the way it does, or why it makes sense to think of certain variables as a system" at all." (Walker, 1991, pg. 609)
** '''Epistemic'''- the choice of interpretations for fundamental, logical concepts used throughout the other components or layers.
The components of this uncertainty taxonomy can combine in different ways to "...produce different aggregate uncertainties." '''note-36 [29]'''
:''' Only the scientific and engineering professions possess the capacity to develop and validate procedures and models that combine the different kinds of qualitative '''note-37 [30]''' and quantitative information and their associated potential for error into an aggregate measure or "scalar variables" of uncertainty.'''note-38 [31]]
Scalar variables are quantitative variables whose categories are related by some measure of the relevant property's incremental frequency, degree, or amount. (Walker, 1991) Examples are Modulus of Elasticity, Compression stress in elastic materials, etc.

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=== Economics Uncertainty Taxonomies ===
Like scientific concepts of uncertainty, economics is environment-specific (in this case, read sector of economy or markets), phenomenon-oriented (in this case, read human activity and manufactured phenomena such as firms and markets guided by human judgment and decision), and model-centric.
:''' Economics is not interested in all forms of uncertainty, only specific, meaningful subsets possessing casual connections with material impacts, often labeled as 'risks'.'''
One of its earlier writers ([https://en.wikipedia.org/wiki/Frank_Knight#Life_and_career Knight], 1921, 1948, 1957) attempted to define "uncertainty" as the presence of "defects of managerial knowledge" (or [https://en.wikipedia.org/wiki/Knightian_uncertainty knightian uncertainty] and thus risk centric) as risk that is immeasurable, not possible to calculate.(Wikipedia) Risk in this sense was defined as "the ordinary risks of business activity which can ... be reduced... by applying the insurance principle." Knight does acknowledge that risk is a specific and unambiguous subset of uncertainty. (Risk, Uncertainty and Profit, 1957, pg. 19) For Knight, measuring uncertainty meant measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences, which Knight referred to as "the imperfection of knowledge". (Risk, Uncertainty and Profit, 1957, pg. 197) One of Knight's many contributions to this analysis was recognizing information and knowledge's role in economic activity.
:"''(W)e are concerned only to emphasize the fact that '''knowledge is in a sense variable in degree and that the practical problem may relate to the degree of knowledge rather than to its presence or absence in toto.''' We live only by knowing something about the future, while the problems of life, or conduct at least, arise from knowing so little. This is as true of business as of other spheres of activity. The situation's essence is action according to opinion, of greater or less foundation and value, neither entire ignorance nor complete and perfect information, but partial knowledge. If we are to understand the workings of the economic system, we must examine the meaning and significance of uncertainty; and to this end, some inquiry into the nature and function of knowledge itself is necessary.''" (Knight, pg. 199) Emphasis added
Knight also reinforced the difficulties in classifying uncertainties when he analyzed life insurance. In this case, accidental death was an uncertain phenomenon compared to sickness and accident, where an "...objective description and classification of cases was impossible..." (Knight, pg. 248) Another factor that made uncertainties ineligible as risks and, therefore, uninsurable because they were unclassifiable was the exercise of judgment in making decisions by the businessman. (Knight, pg. 251)
This is a simple classification of economic uncertainty and, as an idealization, is philosophically controversial. '''note-39 [32]'''
* Uncertainty and information about the economic environment are distinct from uncertainty about others’ behavior or choices.
* Risk as a specific subset of uncertainty implies that "...all possible acts are known, all possible outcomes arising from each act are known, and it is possible to assign probabilities to each act." '''note-40 [33]'''
Like the economic [https://en.wikipedia.org/wiki/Theory_of_the_firm theory of the firm], an economic theory of uncertainty seeks to explain and predict the nature of a specific subset of uncertainty (in this case, risk relating to physical phenomena and human activity), including its behavior, structure, and relationship to the universal uncertainty of physical phenomena. In this sense, uncertainty, like firms and markets, can establish price equilibriums under the right circumstances different from those that might have occurred in either of the other two. Such a theory of uncertainty offers a partitioning scheme that decomposes uncertainty into distinct and meaningful subsets, of which risk is a significant one. It also explains how the presence and variability of knowledge cause or " drive" economic consequences. Unlike physical phenomena, economic activity creates artificial objects such as products, firms, mediums of exchange, markets, economies, and knowledge about those activities. The economics of uncertainty exists as an alternative to "certainty" models that assume away imperfect knowledge and unknown choice/judgment preferences when it is more efficient for economic decision-making. This also allows the introduction of the logical concept of "hazard" versus "risk". Frank used hazard extensively in Risk, Uncertainty, and Profit, but not to the extent that one could say it was interchangeable. An example of this is in the term "[https://en.wikipedia.org/wiki/Moral_hazard Moral hazard]," where a firm could engage in "riskier" and potentially "uninsurable" behavior if the information were transparent, and, therefore, insurance coverage of losses is limited. note-41 [34]], note-42 [35]]

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=== Uncertainty and Risk in Policy and Regulatory Environment ===
:''"Hume's 'just reasoner', when faced with a difficult public policy decision in current times, would probably commission a risk analysis. But how could they incorporate 'a degree of doubt, caution, and modesty?"'' '''note-43 [36]'''
The problem of "decision-making in the face of uncertainty" is how to regulate based on incomplete information that has the potential to be materially inaccurate. A contextual assumption is that we need to evaluate decision rules for dealing with uncertainty. As a social enterprise, risk regulation, whatever its substantive objectives, should be as '''effective, efficient, and equitable''' as practically possible. These "three E's" form a set of "process objectives" or "meta-goals." From the uncertainty standpoint, causal information can be usefully divided into two major categories: information about groups and information about individuals.

Each category, group, and individual has its distinctive types of inherent uncertainty. These are logically distinct, generally independent, and cumulative, contributing to '''(?????)'''

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=== Engineering Uncertainty Taxonomies ===
Like scientific and economic concepts of uncertainty, engineering is environment-specific, phenomena-oriented, model-centric, external choice, and driven by decision-making in the face of uncertainty. Like economics and unlike science, engineering is not interested in all forms of uncertainty, only those with determinable and material consequences. Like economics, engineering conceives risk as a specific and unambiguous subset of uncertainty determined by measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences. Also, engineering recognizes the role information and, by extension, knowledge plays in constructing the built environment.
'''note-44 [37]]''' Like economics, engineering differs from economics and scientific uncertainty in its interest in behavioral uncertainty.
*"Behavioral or interaction uncertainty is how individuals or organizations act or interact. Behavioral uncertainty arises from four sources: design uncertainty, requirement uncertainty, volitional uncertainty, and human errors." '''note-45 [38]]'''
** A design uncertainty is a choice among alternatives over which an individual or group of individuals exercises direct control but has not yet decided upon.
** Requirement uncertainty includes parameters of interest to and determined by the stakeholder, independent of the engineer or designer.
** Volitional uncertainty is uncertainty about what the subject him/herself will decide. Other people’s future actions and conduct are not entirely predictable, particularly when dealing with other organizations.
** Human errors occur during the development of a system or project due to blunders or mistakes by an individual or individuals.

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==Practice Frameworks for Uncertainty and Risk in Civil Engineering Practice==
===PMIBoK===
PMI does not define uncertainty, but the PMI Body of Knowledge uses the term in 45 places (uncertainty) and as describing properties in 8 places (uncertainties). [See Note 8] PMI does define "risk" and uses the term over 1,300 times in the same document. In its glossary, PMI also defines terms such as "threat" and "opportunity" but not events.[391 The BoK contextually defines uncertainty when it states that project risk has its origins in the uncertainty present in all projects. (Op. Cit., pg 309) 
'''Uncertainty''' is contextually defined and taken as having the property of affecting project execution and the ability to meet stakeholder expectations. Uncertainty is more than the sum of all individual risks (known and unknown). The nature of this uncertainty is not defined, only its capacity to affect something else or its properties. 
'''Risk''' is defined as " ... an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives." (Ibid.) Beyond this formal definition, PMI adds context when, in its risk management section (Sec. 11 ), the BoK states that risk is a caused event or condition with multiple causes and impacts. The BoK even offers specific techniques for mapping cause-and-effect relationships and influence diagrams. (Sec. 11.2.2.5) Not as evident but equally important is the recognition that not only is risk caused but the impact is "triggered". The risk trigger is an event or situation that signals that it is about to occur (Glossary, pg. 566). Risk conditions are factors that affect or otherwise contribute to risk, such as project stakeholders. Risk implicitly retains some residual element of probability in it, or a value of less than 1.0 with risk that approaches a level of near certainty is termed "issues" or "realized risk" .(Op. Cit., pg 309). PMI also links risk to underlying variations in project outcomes. (Ibid.) Risk is based upon data and information which can be assessed as to its quality (Sec. 11.3.2.3). This analysis looks to determine the value of the information and examines the degree to which the risk information is understood as well as its " ... accuracy, quality, reliability and integrity ... " (Ibid.) Part of that is understanding the relationship between occurrence and impact as outlined in PMl's Figure 11-10 Risk Impact Matrix and developing a model for prioritizing risk products and setting the threshold for action.
====Commentary:====
For defining and identifying risk, PMI notes in Sec. 11.2.2.4 that " ... Every project and its plan is conceived and developed based on a set of hypotheses, scenarios, or assumptions." Part of the risk analysis process is identifying risks to the project, such as inaccuracy, instability, inconsistency, or incompleteness of assumptions. Similarly, the quality of management plans, as well as their consistency with others and the project objectives and assumptions are 11 •• .indicators of risk in the project." (Sec. 11.2.2.1) 
Similarly, the role of engineering economics in risk is embedded or implied in the PMI definition of risk when the BoK discusses risk identification as a process of assessing which risks out of the total risk exposure for the project "may affect" the project and distilling their characteristics. Embedded in this statement is the concept that not all risks can cause economic impacts (positive and negative) to the project. Also embedded is the requirement to understand risk characteristics to identify project risk (Cost and schedule). Lastly, risk identification is an iterative process of incrementing project risk documentation, much like project scope, cost, and schedule documentation. 
Lastly, PMI acknowledges the complex constitution of risk aggregates or the "sum" of all risks in its material, but is it a sum? Risk information has descriptive and explanatory content. These are knowledge and meta-knowledge components. Understanding project assumptions is project-specific knowledge, but identifying risks from assumption inaccuracy, instability, inconsistency, or incompleteness requires professional or program meta-knowledge. 
'''Uncertainty exists in all projects, but risk, a subset of uncertainty with material economic impacts, is a key meta-knowledge element for civil engineering practice.'''
===Software Development Process- Risk Focused===
The Unified Process requires the project team to focus on addressing the most critical risks early in the project life
cycle. The deliverables of each iteration, especially in the Elaboration phase, must be selected to ensure that the greatest risks are addressed first. <ref> Booch, Grady, Ivar Jacobson, and James Rumbaugh. "The unified software development process."
Reading: Addison Wesley (1999).</ref>
===American Society of Civil Engineers (ASCE)===
ASCE does not define uncertainty, but the ASCE CE Body of Knowledge (CEBoK) uses the term in 22 places (uncertainty) and describes properties in 13 places (uncertainties). 
The CEBoK does not use the phrase "uncertainty and risk" as PMI and AACE do in their publication. ASCE reverses the two (Risk and uncertainty) in twelve places regarding technical outcomes (Outcome 12). A fundamental difference between PMI and ISO is that risk is considered a subset of uncertainty. ASCE does not define "risk" but uses the term 25 times in the same document. Almost all are in the context of variation of design parameters and not in the classical context of cost, schedule uncertainty, and risk.
====Commentary:====
...
=== American Association of Cost Engineers (AACE) ===
AACE defined uncertainty, risk, and other related terms in its Risk Management Dictionary. <ref>Risk Management Committee uRisk Management Dictionary, Cost Engineering Vol. 37/No. 10, OCTOBER 1995. </ref>
AACE's overall strategy was to define risk and other terms that stem from base uncertainty. AACE first defined uncertainty as .... "(t)he total range of events that may happen and produce risks (including both threats and opportunities) affecting a project (see opportunities, events, conditions, risk, and threats" where the following sub-definitions apply:
** Biases--A lack of objectivity based on the individual's position or perspective. Systematic and predictable relationships between a person's opinion or statement and his/her underlying knowledge or circumstances. Note: there may be "system biases" as well as "individual biases."
** Condition (Uncertain Condition)-Any specific identifiable circumstance (such as the rate of inflation or the quality of labor available) that might affect the outcome of the project
** Event (Uncertain Condition)-is a specific identifiable action (such as a large government project being started in the same labor area as your project) or an act of nature that might happen and that (if it does happen) could affect the outcome of the project.
** Opportunities are Uncertain events that could improve the results or improve the probability that the desired outcome will happen
** Threats are Uncertain events that are potentially negative or reduce the probability that the desired outcome will happen.
*** AACE defines Risk as an "ambiguous term" (sic) that is synonymous with uncertainty or a negative subset such as "threats", or an overall negative impact of all possible uncertainties.
====Commentary:====
...
==Limitations of the definition==
...
==Working definition==
...
==Beneficial outcomes==
...
==See also==
Wiki articles on the project.

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==Notes==
<references group="note" />
<li> Implicit in this definition of uncertainty as a "state of' is ... </li>
: " ... a conceptualization of uncertainty as a subjective, cognitive experience of people--a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one's lack of knowledge, without which one could not feel uncertain; '''uncertainty is a form of "meta-cognition" ... ( or alternatively, its main component, meta-knowledge) ... -a knowing about knowing.'''" (Han, 2011, op. cit., Emphasis added)
</ol>
<ol start="5">
<li> Cox wanted his system to satisfy the following conditions:</li>
: Divisibility and comparability- The plausibility of a statement is a real number and depends on the information we have related to the statement.
: Common sense - Plausibilities should vary sensibly with the assessment of plausibilities in the model.
: Consistency - If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). -preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf
</ol>
<ol start="6">
<li> The utility of existing taxonomies of uncertainty in civil engineering and, by extension, "risk" has been unsatisfactory for this very reason. This is particularly true in cases where the source of the uncertainty is conflicting versus incomplete information. See Regan, et. al.,"A taxonomy and treatment of uncertainty for ecology and conservation biology" (2002)
for a survey of scientific taxonomies for uncertainty.</li>
<li> Regan et al. argue that " ... genuine examples of this kind of uncertainty are hard to find. Even classic cases of random experiments like coin tosses and the throwing of dice are deterministic; it is just that we do not have enough information about the dynamic processes and initial conditions to make any sensible estimates about the outcomes. Such processes are, for all intents and purposes, inherently random, but they are not genuinely inherently random. For similar reasons, complex systems such as ecosystems and weather patterns are unlikely to be inherently random. Similarly, chaotic systems are entirely deterministic. They are unpredictable because the deterministic processes generating them and the relevant initial conditions are hard
to fully specify (see Stewart 1989, Sugihara et al. 1990)." (Regan (2002) </li>
<li> The CEBoK uses the phrase "uncertainty and risk" twice in reference to scheduling (Section 6.5,2) and cost estimating (Section 7.2,2) as well as reversing the two (Risk and Uncertainty) also in two places in discussing project life cycle (both in Section 2.4.1) </li>
</ol>

==References==
<references />

Uncertainty and Risk in Civil Engineering Practice

2024-11-24T16:25:47Z

WikiSysop: /* Notes */

'''Life is short and the art long; the occasion instant, experiment perilous, decision difficult.''' (Hippocrates as quoted in Fox,)
:''See also [[Project Definition]] , [[Project Life Cycle and Phase Models]] , [[Project Delivery Methods]]''
:''See also [[Technical Outcomes for Civil Engineering:Risk and Uncertainty]]''
==Basic Considerations==
As human beings,'' ... being alive means seeking opportunities and taking risks.'' As knowledge professionals
living in the 21st century, this means coping with an increasingly complex number of uncertainties for humans
living in this environment. We seek to understand better how these uncertainties can be characterized and
managed. The essence of this article and the experience for engineers in general and civil engineers in particular is
that manageable uncertainty is, by definition, termed risk, and its kindred cousin is termed hazard. This causes us to experience the
:" ''... human dread of and fascination for risk and the increasingly important role of risk analysis within societies ...'' " <ref name="McDaniels"> McDaniels, Timothy, and Mitchell Small. Risk analysis and society: an interdisciplinary characterization of the field. Cambridge University Press, 2004, page 1 </ref> (Ibid.)
and, by extension, civil engineering. McDaniels et al. argue that risk management has been
fundamental to our social and governance development for the past 10,000 years. The scale and shape of the
uncertainties faced in this period shaped the societies that have developed today. The central thrust of this effort
over the centuries has been to reshape and re-frame our understanding and conception of uncertainty from one of
complete unknowing and simple acceptance as our fate in life to one of management (cf "''Against the Gods''"
concept in Bernstein's book <ref name = Bernstein">Bernstein, Peter L., and Jesse Boggs. Against the gods. Simon & Schuster 1997.</ref>.
: ''All the knowledge professions and disciplines have struggled with managing uncertainty, for it is impossible to manage unbounded uncertainty.'' <ref group="note">See McDaniels et al. 2004 for an extensive discussion and bibliography on the historical development of Risk Analysis and some key milestones in risk analysis in the 20th century.</ref>
As outlined below, professions such as civil engineering have successfully acted in the face of uncertainty. It lies in the profession's ability to reduce a wide variety of uncertainties into increasingly smaller and crucially bounded subsets that can be managed. These are called 'risks. More recently, this process has evolved, and individual disciplines such as Civil Engineering (CE) have developed their knowledge and models to perform risk analysis. Risk is also taught as a distinct discipline and specialty practice within civil engineering. Still, it has some unique features that set it apart from other more classical practices within CE. As such, it is one of the first of some very specialized CE practices that use knowledge about civil engineering knowledge, or meta-knowledge. Other examples of civil engineering metaknowledge are project controls and quality controls.
==Semantic, Epistemic and Logical frameworks==
The semantic, epistemic, and logical frameworks for uncertainty and risk have several dimensions and layers of logical frameworks. The semantics problems include interchangeable usages for risk and uncertainty and hazard, uncertain and imperfect, and a lack of definitional material or context. The semantical scheme for this article will be to proceed from the distinction between certainty and uncertainty through marginal refinements and reductions of uncertainty up to the point of causal uncertainties. Beyond this point of semantics will be the logic frameworks or the subject of taxonomies of professional knowledge. First, it is about simple, testable phenomena, and then, it moves on to complex taxonomy schemes for artificially constructed phenomena or engineering projects.
<ref group="note">TBD</ref>
==Semantics framework==
===Certainty and Uncertainty===
''''' There is no such thing as absolute certainty, but there is assurance sufficient for the purposes of human life.''''' ([https://en.wikipedia.org/wiki/John_Stuart_Mill John Stuart Mill]) 
'''''If you tried to doubt everything, you would not get as far as doubting anything. The game of doubting itself presupposes certainty.''''' ([https://en.wikipedia.org/wiki/Ludwig_Wittgenstein Wittgenstein] # 115 from On [https://en.wikipedia.org/wiki/On_Certainty Certainty]) 
It is important to note that the references to epistemic or epidemiological knowledge in this article are assumed to relate to three forms of knowledge, namely:
* Knowledge that ( [https://en.wikipedia.org/wiki/Descriptive_knowledge descriptive or declarative or propositional knowledge])
* Knowledge how ( or "[https://en.wikipedia.org/wiki/Procedural_knowledge know-how]"), and
* [https://en.wikipedia.org/wiki/Knowledge_by_acquaintance Knowledge by acquaintance].
'''Certainty''' has been defined as "an epistemic property" of knowledge in all its forms and the state of our beliefs about that knowledge.
<ref>Certainty, Stanford Encyclopedia of Philosophy, accessed on September 12, 2015, at http://plato.stanford.edu/entries/certainty/#ConCer </ref>
Certainty about any belief about knowledge implies that it is not subject to doubt or skepticism. This immunity to criticism can be dogmatically based, emphasizing the importance of a propositional-based sense of truth over experiential, sensory perceptions. <ref>Definition of [https://en.wikipedia.org/wiki/Dogmatic_theology Dogmatic theology or belief], accessed at Wikipedia</ref>
: '''The demarcation line between dogmatic and non-dogmatic beliefs lies in the presence and recognition of specific criteria and information that would make the believer change their beliefs.'''
An empirical framework of [https://en.wikipedia.org/wiki/Testability testability] and [https://en.wikipedia.org/wiki/Falsifiability falsification] is required to recognize this and limit dogmatism. For example, one could argue that people would change their minds if God asked them to. Similarly, one could construct a concept of epistemic as opposed to dogmatic belief certainty as the ability to know anything that one chooses to know and can be known or [https://en.wikipedia.org/wiki/Omniscience inherent omniscience].

A second kind of certainty is epistemic, when conviction reflects the highest possible support for a belief. In this sense, knowledge is separate from beliefs, although someone may have beliefs about a property, such as certainty of knowledge. Logically, it has been shown that there will be unprovable statements within the system for any such knowledge system. Secondly, the knowledge system cannot demonstrate its own consistency. ([https://en.wikipedia.org/wiki/Gödel%27s_incompleteness_theorems Gödel's incompleteness theorems])
: '''Certainty, in real life, is useless or often damaging (the idea is that "total security from error" is impossible in practice, and a complete "lack of doubt" is undesirable)''' ([[https://en.wikipedia.org/wiki/Certainty Physicist Carlo Rovelli]])
'''Uncertainty''', on the other hand, arises immediately in the slightest amount of doubt or criticism.
<ref group="note"> The Merriam-Webster dictionary defines uncertainty as “the quality or state of being uncertain,” which is something of a circular definition. Likewise, one could define uncertainty as the state of not being certain. Synonyms are distrust, doubt, misgiving, mistrust, reservation, skepticism, and suspicion. Another writer added indefinite, indeterminate, not certain to occur, problematical, unreliable, untrustworthy, unknown beyond doubt, dubious, doubtful, not clearly identified or defined, not constant, variable, and fitful to the list. (Han, Paul KJ, William MP Klein, and Neeraj K. Arora. "Varieties of Uncertainty in Health Care: A Conceptual Taxonomy." Medical Decision Making 31.6 (2011): 828-838.)Han, et. al. noted that any definition of uncertainty clearly encompasses "...numerous types, sources, and manifestations of uncertainty, and ...(any) ...useful working definition of uncertainty needs to specify the concept underlying these varied meanings of the term."</ref>
Implicit in this definition of uncertainty as a “state of” is ...
"...a conceptualization of uncertainty as a subjective, cognitive experience of people—a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one’s lack of knowledge, without which one could not feel uncertain; uncertainty is a form of “meta-cognition” ...(or its main component, meta-knowledge)... —a knowing about knowing." (Han, 2011, op. cit., Emphasis added)[Note 3]
Similarly, uncertainty could be defined as "...any departure from the unachievable goal of complete [https://en.wikipedia.org/wiki/Determinism determinism]." <ref name="Reference5">[5]</ref> Reasoning under uncertainty is very different than performing the same under certainty. In reasoning under certainty, one has complete knowledge and deduces without doubt and equally important, without limitation, thereby concluding free from error. Reasoning under uncertainty, one works in a state of incomplete, inconsistent, and limited knowledge; doubts cloud any statements or assertions and thereby taint any deductions/inferences resulting in the potential for error. <ref name="Note4">[Note 4]</ref>
:'''Knowledge professions reason in a state of incomplete, inconsistent and limited knowledge with doubts that cloud any statements or assertions and taint any deductions/inferences resulting in the potential for error.'''
:''' It is also important to note the semantical and logical correlation between 'reasoning under uncertainty' or 'acting in the face of uncertainty' with the 'potential for' or 'presence of' error. The presence of uncertainty is invariably linked to the potential presence of error.''' The [https://en.wikipedia.org/wiki/Contingency_(philosophy) contingent] nature of uncertainty logically implies the contingent nature of error.
:'''Knowledge Professions use error as a proxy for uncertainty such that within discipline knowledge frameworks, uncertainty can be managed.''' The rationale for this belief is that error can be reliably described, quantified, explained, and ultimately reduced in ways that uncertainty cannot.

'''Uncertainty''' implies a range of variation that is impossible in certainty. Concepts and definitions of uncertainty are unbounded and vast but start from the point of complete and total ignorance. In describing uncertainty, statements can range from complete ignorance to relatively high degrees of belief that there is less potential for error in our reasoning or action. The latter is based upon confidence in justified knowledge, favorable past experience, and reliable parameters/models. One can't make that statement about ranges in certainty. If certainty, by definition, precludes doubt of any type or nature, then how could that be graduated to any degree? 
Uncertainty, on the other hand, can be reduced through disciplined efforts to acquire knowledge. In short, we can reduce or even manage uncertainty (to a degree); how can certainty be improved? Are there higher degrees of perfection? The answer is no. We reason from an uncertain starting point. Yet this effort presumes, as Mill and Wittgenstein postulated, that we can develop or acquire an increasing degree of belief or conviction that there is less potential for error in our reasoning or action. This position assumes that some way exists to identify what a lesser degree of uncertainty would look like.
:'''Knowledge professions acquire an increasing belief or conviction that there is less potential for error in their reasoning or action based on justified knowledge, favorable past experience, and reliable parameters/models.'''
Uncertainty is, in some form, amenable to description and explanation through observation and analysis. It is explainable and predictable to such a degree that it interests professionals such as scientists and engineers. Professional knowledge of this type has explanatory or predictive power, albeit limited in scope and application to relevant subjects such as physical objects or phenomena and functionality.
:'''The very act of reducing the scope of uncertainty to a limited set of phenomena implies choice and, ultimately, the decision to act in the face of such uncertainty, but acting or judging in this manner adds more value than doing the same under relative ignorance.'''
'''Uncertainty''', as described above, implies a hybrid nature. Examples of this are economic or decision-theoretic applications. In economics, [https://en.wikipedia.org/wiki/Perfect_information perfect information] allows one within the limiting framework of perfect competition in economic games to make decisions with 'perfect knowledge.' The crucial difference here is the distinction between '[https://en.wikipedia.org/wiki/Perfection perfection],' broadly, a state of completeness and lawlessness, and 'perfect, which can be thought of as an analytical limit that is approachable but can never be attained. Arbitrarily delimiting the realm of knowledge into defined, finite boundaries allows the simulated production of perfect choice, assumed crucially, to be free from error. The hybrid nature of this form of 'simulated' certainty produced within boundaries defined by assumptions and, therefore, clouded by doubt is itself a form of uncertainty. The importance of this is the semantical and logical association that exists between certainty and perfection versus the hybrid concept of a perfect anything, whether knowledge, infraction, competition, etc., or in the case of engineering, elastic, permeable, conductive, etc., being contained within an admittedly imperfect matrix of uncertainty.
:'''Simply put, a finite, heavily bounded piece of uncertain knowledge can be improved through the simulated use of [https://www.merriam-webster.com/dictionary/assumed assumedly] perfect information, but the perfection of knowledge cannot be attained or simulated.'''
Engineering and Economics, for example, simulate finite elements of perfect knowledge or information, heavily bounded with assumptions and a range of applications. By doing so, these professions reduce the bounds and variability of uncertainty and thereby manage it. Using these hybrid models to acquire knowledge takes place in an economy (regulated or institutional) and environment (transparent and structured), which puts pressure on the various professions to recognize and use qualitative and quantitative knowledge better to reduce uncertainties in their activities. The challenges in this reflect the nature of the profession's or discipline's knowledge (e.g., scientific, engineering, or medical), the policies and structure of the relevant professional knowledge bodies, process objectives and constraints, and "the elusive demands of politics." [6] One common theme runs through all of these efforts to address the presence of uncertainty in professional activities. 
:"The available scientific information upon which a decision must be made is almost always a mixture of engineering knowledge and uncertainty. Regardless of the information or methodology, there is the potential for error. This is true regardless of the relevant science, whether archaeology or aeronautics, economics or engineering, pharmacology or toxicology or epidemiology. Achieving the best social decisions requires not only understanding and using what we know but also appreciating and weighing the extent of our uncertainty. In making the best use of ... information in ... decision-making, it is still true that the beginning of wisdom is knowing what it is we do not know." (Vern, Op. cit., Reformatted and engineering substituted for scientific) 
'''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty to develop explanations, calculate, and make predictions. This professional knowledge converges on but never reaches certainty, producing a "legitimacy" from the fruitfulness of its use.''' <ref>Randomness Is Unpredictability, Antony Eagle, The British Journal for the Philosophy of Science, Vol. 56, No. 4 (Dec., 2005), pp. 749-790 </ref> 
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=== Bias and Uncertainty ===
Up to this point, the concept of error, as outlined in uncertainty, could easily be simulated by a truly random variable. There should be no detectable differences around the unseen or specified statistical mean. However, several instances in experience point to a tendency towards one extreme or a pattern of error, a collective that is in itself an error of errors, namely [https://en.wikipedia.org/wiki/Bias bias].

Arguably, this could be part of the choice uncertainty discussion below, but it makes more sense to be looked at on its own. Acting in the face of uncertainty is an exercise of personal knowledge or experience. Whether using professional knowledge or individual experience, the potential is there to make a series of choices that, in some instances, make errors more likely than they would be if considered in the context of a statistical analysis.

This tendency for an individual to make the same error in a predictable or repeatable manner is termed bias. Personal experience, beliefs, knowledge, data, and models all have inherent built-in factors, errors, or defects that create predictable error patterns when applied in decision-making. Walker (1998) argues implicitly that such bias can also be considered systemic error. This type of uncertainty is difficult to identify except on theoretical grounds when the magnitude and direction of the bias are known. Most of these biases, whether systemic or knowledge-based, individual or practice-based, require disciplined efforts to "validate" such methods, models, and judgments at all layers while bounding the uncertainty.
: This is the challenge to professional knowledge that must be overcome, namely, developing and validating models and methods for managing bounded uncertainty that minimize this tendency towards repeatable groups of error or bias. Therefore, from this point forward, the discussion will refer to 'uncertainty and its proxies, error, and bias.'
===Propagating Error and Uncertainty===
Partitioning uncertainty and its proxies, error, and bias into logically distinct subsets involves or is associated with multiple-step execution or choice frameworks. Combining those measurements or choices into single parameters or choices involves, in some cases, assembling the data/information in a unique or limited range of sequences. This implies that associated errors in observing, calculating, or choosing will have unequal impacts or consequences depending on where in the chain the error occurs. Also implied in this is the possibility that subsequent errors will be causally linked to some degree. (In statistical practice, this depends upon whether the errors are independent of each other or correlated, specifically, co-variant.)
:'''Professions develop knowledge of how uncertainty and its proxies, error, and bias propagate through an ordered and normative series of observations or choices. This allows the profession to prioritize error and bias reduction to achieve an optimized reduction of uncertainty.'''
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===Cognition, Metacognition and Uncertainty===
The concept of choice and the resultant error in uncertainty can be further delimited depending on the role of [https://en.wikipedia.org/wiki/Cognition cognition], [https://en.wikipedia.org/wiki/Metacognition meta-cognition], and independent external elements. The rationale for this argument is that such choice activity in the face of uncertainty is a conscious, cognitive act.

An argument could be made that such cognition is a prerequisite for exercising choice and introducing error, as presented and discussed above. The counterfactual to this argument is the example of uninformed choice. Choices introduce as much as errors or more as informed choices could be made. (???) Choice, in this sense, is indifferent to knowledge. Purposeful choice in an environment of objectives requires knowledge, method, and experience, but uninformed, speculative choice does not.
:"''' Cognition''' is the set of all mental abilities and processes related to knowledge, attention, memory and working memory, judgment and evaluation, reasoning and "computation," problem-solving and decision making, comprehension and production of language, etc. Human cognition is conscious and unconscious, concrete or abstract, as well as intuitive (like knowledge of a language) and conceptual (like a model of a language). Cognitive processes use existing knowledge and generate new knowledge." (Wikipedia)
:"'''Metacognition''' is "cognition about cognition", or "knowing about knowing" and can take many forms. It includes knowledge about when and how to use particular strategies for learning or problem-solving. There are generally two aspects of [https://en.wikipedia.org/wiki/Metacognition metacognition]: knowledge about cognition and regulation of cognition."
Some types of metacognition knowledge are:
:'''Person knowledge''' (declarative knowledge), which is understanding one's own capabilities.
:'''Task knowledge''' (procedural knowledge), which is how one perceives the difficulty of a task, which is the content, length, and type of assignment.
:'''Strategic knowledge''' (conditional knowledge) is one's capability to use strategies to learn information. (Accessed at Wikipedia)
Like metacognitive knowledge, metacognitive regulation or "cognitive control" contains three essential skills.
:'''Planning:''' refers to the appropriate selection of strategies and the correct allocation of resources that affect task performance.
:'''Monitoring:''' refers to one's awareness of comprehension and task performance.
:'''Evaluating:''' refers to appraising the final product of a task and the efficiency at which the task was performed. This can include re-evaluating strategies that were used. (Accessed at Wikipedia)
Nothing has been said up to this point that limits this structure to an individual, or a collective, or two entities making cognitive choices that are incompatible or inconsistent to varying degrees. Interpretations of this scheme include an individual professional exercising judgment in a decision that, in turn, relies upon the collective judgment and decision of the discipline as an underlying basis versus the decision of another individual to oppose/protest or otherwise contest that decision. Thus, the framework of cognitive/meta-cognitive applies to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, economics of regulatory practice, and game theory.
:By solving useful problems in the face of inherent uncertainty, discipline-specific frameworks of cognitive/meta-cognitive activity apply to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, the economics of regulatory practice, and game theory.
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===Probability and Uncertainty===
Probability is a [https://en.wikipedia.org/wiki/Coherence_theory_of_truth coherent] approach to uncertainty in the physical and mathematics or "classical" domains such as engineering. [8] and is the "...is certainly the best-known and most widely used formalism for quantifying uncertainty.[9] [https://en.wikipedia.org/wiki/Cox%27s_theorem Cox’s theorem] ("Any measure of belief is [https://en.wikipedia.org/wiki/Isomorphism isomorphic] (but not necessarily equal) to a probability measure") is a well-known argument for the validity of that argument. [Note 5]

Another writer, [https://en.wikipedia.org/wiki/Frank_Knight Knight] (1921,1956), presented the following taxonomy of probabilities: [10]
: '''Classical 'a priori' probability:''' As an idealized model, numerical probabilities are computed based on generalized principles of assigning equal likelihoods and mutually exhaustive possible outcomes (Runde, 1998) to all set members. Such probabilities are assigned to "...outcomes based on a judgment of indifference between those outcomes, that is, based on the absence of any evidence of real influences in play that may render any one outcome more or less probable than any other." (Runde, op. cit.) This builds upon Knights's concept of an 'absolutely homogeneous classification of instances that are completely identical except for [https://en.wikipedia.org/wiki/Indeterminate_(variable) indeterminate] factors. This judgment of probability or logical probability is on the same logical plane as the propositions of mathematics and ultimately [https://en.wikipedia.org/wiki/Inductivism inductions] from experience.'(Knight, 1956, pg.225)
: '''Bayesian 'a priori' probability:''' In contrast to interpreting probability as the "frequency" or "propensity" of some phenomenon, [https://en.wikipedia.org/wiki/Bayesian_probability Bayesian probability] is a quantity that we assign to represent a state of knowledge or a state of belief. In this view, probability is assigned to a hypothesis, often using the basis of past or prior experience. In contrast, under the frequentist view, a hypothesis is typically tested without being assigned a probability based on prior experience. The Bayesian interpretation of probability can be seen as an extension of propositional logic that enables reasoning with hypotheses, i.e., propositions whose truth or falsity is uncertain. Bayesian probability belongs to the category of evidential probabilities; to evaluate the probability of a hypothesis, the Bayesian probabilist specifies some prior probability, which is then updated in the light of new, relevant data (evidence). The Bayesian interpretation provides a standard set of procedures and formulae to perform this calculation. [11]
: '''Statistical or 'a posterior probability:''' Empirical evaluation of the frequency of association between predicates, not analyzable into varying combinations of equally probable or logical, 'a priori' alternatives. Knight argued that any "high degree of confidence" that the judgment that experience will remain valid for future predictions is "...still based on an 'a priori" judgment of indeterminateness." (Knight, Op. Cit.) Knight's argument to support this is what will be discussed further below that first, "...the impossibility of eliminating all factors not really indeterminate; and, second, the impossibility of enumerating the equally probable alternatives involved and determining their mode of combination so as to evaluate the probability by a priori calculation." (Knight, Op. Cit.) Knight noted that the main difference between this form and that of logical or inductive probability was the presence of empirical information, which allowed the analyst to identify propensity and direction in the observed phenomena. In this case, 'a priori' probability values may be derived from first principles and "statistical probabilities are determined a posteriori by the empirical method of counting instances." (Runde [1998] quoting Knight, op. cit.)
:'''Estimates:''' The distinction here is that there is no valid basis ('a priori' or 'a posteriori') for any classifying phenomena. For Knight, this form of probability presented the greatest logical difficulties of all ... but its distinction from the other types must be emphasized, and some of its complicated relations indicated..." (pp. 224-5, emphasis in the original)

Arguably, Knight's concept of probability can be viewed as "..a continuum of probability situations, depending on the degree of homogeneity of the 'instances' in question" [12] ; i.e. going from a logically distinct but equally likely set of elements to a unique set with one member with statistical frequency in the middle. Knight's main motivation for distinguishing between a priori probability and statistical probability underscores his opinion that "(i) the 'mathematical or a priori type of probability is practically never met with in business, while the second is extremely common'; and (ii) 'the statistical treatment never gives closely accurate quantitative results' (Runde quoting Knight pp. 215-16). Runde argues that Knight couldn't conceive of (empirically tabulated) occurrences that are used in daily commerce classes of instances that we have to make do within the course of everyday economic life could be divided into subclasses of instances that are sufficiently homogeneous to permit the determination of what he calls 'real' probability (p. 217).
:'''In modeling degrees of uncertainty, any measure of belief is isomorphic but not necessarily equal to a probability or statistics measure'''.
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===Explanation, Prediction and Uncertainty===
It should be clear that the general notion of uncertainty synonymous with complete ignorance must be bounded as part of a scheme to produce a useful concept of uncertainty in professional practice such as civil engineering.
:The first principle that must be introduced is that such uncertainty must be bounded by reliable and valuable knowledge gained from past experience combined with analytical and computational capabilities to address an immediate and real problem of interest. This could be viewed as the economic interest argument. Only uncertainties associated with physical phenomena and economic scarcity will be addressed.
:The second principle is that the knowledge model is capable of producing statements or assertions in the form of hypotheses that themselves are testable. [https://en.wikipedia.org/wiki/Testability Testability], in this sense, is defined as the property applying to an empirical hypothesis and involves two components:
::The logical property that is variously described as contingency, defeasibility, or falsifiability, which means that counterexamples to the hypothesis are logically possible. Contrast this to a [https://en.wikipedia.org/wiki/Tautology_(logic) logical tautology], which is always true that is true in every possible interpretation. The practical feasibility of observing a reproducible series of such counterexamples if they do exist. In short, a hypothesis is testable if there is some real, non-zero expectation of deciding whether it is true or false of verifiable, reproducible experience. Upon this property of its constituent hypotheses rests the ability to decide whether a theory can be supported or falsified by actual experience data. (Source Wikipedia.)
'''Knowledge models must be capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria described above in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error and bias.'''
===Variability and Aleatory Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge referred to as epistemic uncertainty. Acting in the face of epistemic uncertainty introduces choice uncertainty (discussed below) and results in the production of explanations and predictions. It is implied that there will be testable hypotheses in the form of outcomes of interest to the knowledge professions. Inherent in nature is a variation of results, which may appear to uncertain but isn't. Observations of phenomena will not be the same, and part of the experimental process is to narrow those results down to within an acceptable level of tolerance. Statistical measures seek to identify underlying and unobserved measures of central tendency. These parameters are not directly observed but are derived to a degree of confidence.
In some cases, variation around a mean or within a statistical range may be perfectly acceptable to the knowledge profession. Examples of this are in the physical sciences and medicine. In Engineering and, to a lesser extent, economics, the process or phenomenon may be required to be more controlled to a narrower range than what a natural or "unmanaged" amount of variation would allow. Several courses of action present themselves.
:The first would be to ignore the aleatory phenomenon and choice, which is rejected outright.
:The second would be to assume that, in effect, such variation is itself a form of minimally managed uncertainty and treat it the same as epistemic uncertainty. This is not a desirable outcome as reducing Aleatory error and bias requires
:A third approach would be to acknowledge that process variation is, in some forms, manageable. This means making choices and acting in a way that presumes that the natural occurrence of error can be reduced, or more importantly, bias can be reduced. There is ample precedent for the third approach in civil engineering design and project management. In this sense, the argument is made that the aleatory response to managed efforts results in an elastic variation range. Presumably, an effective management effort either "shifts" the central tendency of the process results or reduces the variation range of occurrence or some other population parameter. Choosing to act and accept that narrower range rather than the broader natural variation may result in an outcome that, while well within the bounds of the expected variation, is outside the arbitrary control limits established by the choice and action. While this phenomenon mimics the broader uncertainty and its proxy error, it is not an error. The information on the variation was known, and the ability to produce narrower results was an error, albeit a pseudo-error, when compared to the broader classes of uncertainty discussed above, such as total ignorance.
::Looking at uncertainty using this third approach means that the error in choosing a new target parameter for this process may contain epistemic errors (our knowledge of the phenomenon may be erroneous, or the model flawed), aleatory uncertainties as discussed, or, more importantly, choice error or bias in thinking that we can move the process results to meet the requirements.
:'''In civil engineering practice, this may mean applying professional judgment in developing partitioning schemes or allocating uncertainty between the three dimensions (Epistemic, Aleatory, and Behavioral) of uncertainty.'''
Lastly, raising the topic like this brings the questions of effectiveness and efficiency to the fore. Advancing knowledge models that reduce error and bias at first are largely choice models resulting in justifications and favorable experiences. At some point in the process, the knowledge profession advances explanations and predictions that require reliable mathematical or logical concepts that are reduced to models and parameters. The question of efficiency becomes important; the broader uncertainty may have been reduced, but the process is economically inefficient. The profession is urged to become more frugal or, for example, reduce the variability of the outcomes. Knowledge professions often operate under conditions that require them to acquire knowledge and reduce uncertainty effectively and efficiently. Doing so requires understanding both epistemic uncertainty, as discussed above, and statistical process variation or aleatory variation.
:'''Knowledge professions manage and reduce uncertainty in the form of error and bias in an economically effective and efficient manner. This effort requires understanding both epistemic and aleatory uncertainty.'''
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===Choice and Behavioral Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge, referred to as epistemic uncertainty, as well as the inherent variations in results, or what was termed aleatory uncertainty. Acting in the face of that epistemic and aleatory uncertainty introduces yet another uncertainty into the process, namely choice uncertainty. This uncertainty reflects uncertainties associated with first-person and third-person choice. This is particularly relevant for engineering with project stakeholders.
===Summary of Semantics Framework for Uncertainty===
Professional disciplines have gradually re-framed their understanding of uncertainty and, thru experience and analysis, have arrived at the following meta-knowledge concepts of uncertainty:
* '''Uncertainty and its proxy, error, have three components: [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic epistemic uncertainty], [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic aleatory uncertainty], and behavioral uncertainty.'''
* '''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty using non-dogmatic beliefs, which recognize the existence of specific criteria and information that would make the believer change their beliefs using coherent approaches such as probability.'''
* '''Knowledge professions develop knowledge of how uncertainty and its proxy, error, propagate through an ordered and normative series of observations or choices that meet testability criteria in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error. An error can then be described, explained, and reduced in ways that uncertainty cannot. This allows the professions to prioritize error reduction to achieve an optimized reduction of uncertainty and produce a "legitimacy" from the fruitfulness of its use.'''
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===Uncertainty versus Risk/Hazard===
Up to this point, nothing has been said about risk or hazard. Now, risk can be defined. Simply put risk or hazard are finite, logically distinct subsets of the broader uncertainty. Risk and hazard are currently defined interchangeably. First and foremost, risk is a specific subset of general uncertainty. Any arbitrary subset of general uncertainty can be defined as a risk if it meets the following criteria:
* Uncertainty is associated with the potential for material error when acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena.
* Uncertainty and its proxy, error, can be partitioned into logically distinct subsets associated with multiple-step execution or choice frameworks in physical environments linked to a unique or limited range of sequences.
**Subcriteria 1:Resulting errors will have varying impacts or consequences depending on where the error occurs and the degree of causal linkage or influence in the chain.
**Subcriteria 2:Potential errors can be sequenced and prioritized to achieve an optimized reduction of error or bias and, by proxy, uncertainty over time.
* Uncertainty and its proxies, error bias, and environments are isomorphic to coherent approaches such as probability.
* Uncertainty and its proxies, error, and bias are isomorphic to coherent approaches such as knowledge models capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria in 'reasoning under uncertainty.'
This is a definition of general risk. There is no differentiation for engineering or economics, medicine or insurance. There is no difference between a 'good' risk consequence and a 'negative' one. Redefining general risk into simpler terms gives the following:
:'''General Risk is a subset of general uncertainty that is associated with the potential for material errors and biases when sequentially acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena; isomorphic to probability measures and methods and capable of testability.'''
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==Logical Framework for Specific Risk Taxonomies==
As noted above, at the most fundamental level, uncertainty is ".....the subjective perception of ignorance." [13] [Note 6] At this point in the discussion, the focus has moved from discussing uncertainty to its framed subset, risk.
:'''Further, the discussion moves from general risk to a professional or discipline-specific risk [https://en.wikipedia.org/wiki/Taxonomy taxonomy]. Beyond this point, risk will be used in place of uncertainty.'''
Han et al. note that, in general, "(t) taxonomies are valuable not only in their comprehensiveness but in their coherent reduction of uncertainty to conceptually discrete elements."
Uncertainty (and, by implication, risk or hazard) taxonomies offer an approach or tool to more precisely identify and define ontological uncertainty so that it can be quantified, analyzed, and communicated. Viewed this way, uncertainty is not a single, monolithic phenomenon but "...multi-dimensional with theoretically distinct domains and constructs that are potentially measurable and related to different outcomes, mechanisms of action, and management strategies." [14] Taxonomic schemes for classifying the different kinds of scientific and, by extension, engineering uncertainty require identifying the various kinds of potential error associated with descriptive scientific or engineering information and knowledge. An example of this is the following quote from a legal authority:
:"I would especially stress the need for an agency to disclose the uncertainty that surrounds its determinations. '''And by uncertainty, I mean the agency's ignorance as well as its quantitative estimates of error."''' (Emphasis added) [15]
'''Given the necessity of acting in the face of enormous uncertainties [16], discipline taxonomies must offer a clearly coherent and probabilistic approach to uncertainty that is sufficiently general in nature, logically distinct and exhaustive in scope and "...provide decision-makers with a foundation for understanding the nature of discipline information..."''' [17]
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=== Scientific Risk Taxonomies ===
The [https://en.wikipedia.org/wiki/Scientific_method scientific method] is a body of techniques for investigating phenomena in its environment, acquiring new knowledge, or correcting and integrating previous knowledge. Inherent in that process are uncertainties of many forms. Any one taxonomy of [https://en.wikipedia.org/wiki/Uncertainty scientific uncertainty] has largely been focused on statistical models used to assess and quantify sampling, errors, and parameters, although several different taxonomies are conceivable: note-24 [18]
====Environment-specific, Phenomena oriented, model-centric taxonomy of uncertainties====
** [https://en.wikipedia.org/wiki/Uncertainty_quantification#Sources_of_uncertainty '''Parameter uncertainty'''] the source of which is model parameters that are inputs to the computer model (mathematical model) but whose exact values are unknown and cannot be controlled, or whose values cannot be exactly inferred by statistical methods. Subsets of this type of uncertainty include but are not limited to:
***'''Experimental uncertainty''' is also known as observation error, which comes from the variability of experimental measurements. An example is repeating an experiment measurement several times using exactly the same settings for all inputs/variables and recording the variability. It is a subset of the larger uncertainty component, parameter uncertainty.
*** '''Parametric variability''' comes from the variability of the input variables of the phenomena model and is also a subset of the larger uncertainty component, parameter uncertainty.
** '''Structural uncertainty''', or model inadequacy, model [https://en.wikipedia.org/wiki/bias bias] or "[https://en.wikipedia.org/wiki/Systemic_bias systemic bias]", or model discrepancy, which comes from the lack of knowledge of the underlying true state of the phenomenon or its environment. It depends on how accurately a mathematical model describes the true state of the phenomenon or what is termed "model fitness". Due to the inherent nature of uncertainty in any knowledge body (scientific or engineering), models are only an approximation to reality. Therefore, any conclusions drawn from the model can be very misleading when the underlying basis is not plausible or lacks validity. '''note-25 [19]]'''
*** What is missing in this context is any concept of output variability due to the model itself. Part of this is due to the lack of recognition of "process" in scientific investigation. The concept of the scientific method started with a single investigator, such as Galileo or Michael Faraday, working in their laboratories. It has grown into planet and solar system scale experiments such as data gathered on planetary flybys such as Mars, Pluto, and Ceres to the hunt for the Higgs Boson. Ultimately, science has become more like engineering in the scale and complexity of its investigations and theory aggregates, such as the "Unified theory" of particle physics. Subsets of this type of uncertainty include but are not limited to:
*** '''Algorithmic uncertainty''', or numerical uncertainty, comes from numerical errors and numerical approximations per implementation of the computer model. Most models are too complicated to solve exactly. For example, the [https://en.wikipedia.org/wiki/Finite_element_method finite element method] or [https://en.wikipedia.org/wiki/Finite_difference_method finite difference method] [may be used to approximate a solution [https://en.wikipedia.org/wiki/Partial_differential_equation partial differential equation], which introduces numerical errors. Other examples are numerical integration and infinite sum truncation, which are necessary approximations in numerical implementation.
*** '''Interpolation uncertainty''' comes from a lack of available data collected from computer model simulations or experimental measurements. For other input settings that don't have simulation data or experimental measurements, one must interpolate or extrapolate to predict the corresponding responses.
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====Commentary====
A more comprehensive taxonomy of uncertainties that acknowledges knowledge, data, and linguistic uncertainties is:
*'''Epistemic uncertainty''' -[https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Epistemic uncertainty] or systematic uncertainty, in contrast, reflects limitations in the current “state of knowledge” underlying models themselves, originates from competing theories or models, is not readily quantifiable, and is manifest by subjective confusion or indecision. It includes uncertainty due to measurement limitations, insufficient data, extrapolations and interpolations, and variability over time and space. Epistemic uncertainty is uncertainty about "...some determinate fact ... because of a lack of complete information. '''cite_note-26 [20]]''' Epistemic uncertainty can be classified into six main types:'''cite_note-27 [21]]'''
*''' Measurement error''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Observational_error Measurement error] is uncertainty that manifests itself as (apparently) random variation in the measurement of a quantity. Repeated measurements will vary and demonstrate classic statistical behavior.
* '''Systematic error''' - [https://en.wikipedia.org/wiki/Observational_error Systematic error] occurs due to bias in the measuring equipment, model, analysis, observation, or sampling procedure. It is formally defined as the difference between the true value of the quantity of interest and the value to which the mean of the results converges as sample or data sizes increase. Unlike measurement error, it is not (apparently) random, and therefore, results subject to systematic error alone do not vary about a true value. Systematic error can result from deliberate judgment to exclude (or include) data, parameters, or models that ought not to be excluded (or included).
** "The only way to deal with systematic error is to recognize a bias in the process, model, or procedure and remove it. Systematic error, however, is notoriously difficult to recognize except on theoretical grounds. Corrections may only be applied when the magnitude and direction of the bias are known. Such corrections underlie the application of double-sampling methods in environmental science." (Regan, et. al., op. cit.)
*'''Natural variation''' - [https://en.wikipedia.org/wiki/Natural_process_variation Natural variation] or underlying process variation occurs in systems that "...change (concerning time, space, or other variables) in ways that are difficult to predict...across the full range of temporal and spatial values (or other related variables)." '''note-28 [22]'''
** This form of uncertainty is reduceable utilizing tools such as [https://en.wikipedia.org/wiki/Statistical_process_control statistical process control] and replication of experiments or observations.
*'''Inherent randomness''' - [https://en.wikipedia.org/wiki/Randomness randomness] or [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty aleatory uncertainty] exists because the system is"...in principle, irreducible to a deterministic one (the most well-known case is described by [https://en.wikipedia.org/wiki/Uncertainty_principle Heisenberg’s uncertainty principle] in quantum mechanics)." '''note-29 [23], note-30 [Note 7]'''
** Even if the argument is accepted that it is unlikely that any given physical system at some level is inherently random, it is important for any taxonomy to distinguish between systems that appear random due to incomplete or inconsistent information and those that are intrinsically random. (Again, see Regan (2002)).
*'''Model uncertainty''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling Model uncertainty] occurs in the process of generating a model as a conceptual representation of a particular phenomenon to create a [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling#Overview simplified reflection of reality]. This selectivity of factors and variables creates uncertainty in at least three ways. (Regan (2002))
** '''Variables and processes''' that are regarded as relevant often represent a trade-off between system knowledge and assumed states and model objectives;
** '''Models''' depict observed processes using logical or mathematical constructs based on underlying theories about system states or dynamics using continuous equations to describe discrete processes.
** '''Curve fitting''' (including interpolation and extrapolation) with mathematical expressions using model variables where inputs are discrete data points.
** Regan (2002) argues that '''model uncertainty''' is "...notoriously difficult to quantify and impossible to eliminate ...(and)...(t)he only reliable way of determining how appropriate a model is for prediction is to perform validation studies."
** Subjective judgment occurs due to the interpretation of scarce or error-prone data.
*** The only way to address this type of uncertainty is to "...assign a degree of belief about an event in the form of a subjective probability." (Regan (2002))
*Linguistic uncertainty - Linguistic uncertainty, or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. '''note-31 [24]], note-32 [25]]'''
** '''Context dependence''' is uncertainty concerning the context in which a statement is to be understood;
** '''Ambiguity''' occurs when words have multiple meanings, and further reduction to a single meaning is not logically possible in a given context.
** '''Underspecificity''' occurs in the presence of "unwanted generality" or multiple interpretations, and reduction to a smaller set or even a single interpretation is not logically possible.
*Stochastic or statistical uncertainty - Stochastic or statistical uncertainty, which is sometimes known as [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Aleatoric uncertainty] as well as the subject of [https://en.wikipedia.org/wiki/Stochastic_control stochastic control] pertains to the parameters of a risk model, originates from sampling or measurement error, and can be quantified and mathematically expressed (e.g., using confidence intervals).
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== Uncertainty Taxonomies in a Legal Environment ==
From a legal perspective, a definition of taxonomy is dictionary-based, such as "the systematic distinguishing, ordering, and naming of type groups within a subject field." given by Walker (op. cit.). Such legal concepts of scientific or engineering taxonomies are not required to be epistemically complete. Still, they must cover "the most significant aspects of ...(current, good discipline practices) ... and of the descriptive information commonly encountered by decision-makers" in a logically distinct manner. (Walker, 1991, p. 571) These taxonomies must be sufficient to catalog the types of uncertainty associated with descriptive assertion, including the critical subset of cause and effect assertions. This legal/decision-maker framework has presented descriptive uncertainty as having six components (Walker, 1991). Although not part of the Walker taxonomy, underlying the scheme is linguistic uncertainty, or "vagueness," as a source of uncertainty, including uncertainties due to context dependence, ambiguity, and under-specificity. '''note-33 [26]]''' Arguably, these uncertainties underlie the most basic element in the taxonomy.
* '''Linguistic '''- or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. (Regan, 2002)
* '''Conceptual'''- the definition by choice and design of descriptive concepts or variables to be used as [https://en.wikipedia.org/wiki/Predicate_(grammar) predicates] where the subject of an assertion identifies what is being discussed and the predicate provides information about the topic or characterizations such as what the subject is, what the subject is doing, or what the subject is like (Wikipedia).
* "Conceptual uncertainty, or the potential for conceptual error, arises whenever predication occurs. Whenever a concept is used to describe something, using certain concepts instead of others begins to structure how we understand the object, event, or instance under discussion. Predication or conceptualization generates useful information about things, but it also can inhibit our ability to think about those things with concepts other than those selected. The concepts used may not be the most fruitful or the best designed- either for scientific purposes or for making wise, fair, effective, and efficient decisions." (Edited for reading, Walker, 1991)
* Similarly, choice and design of descriptive variables enhance and restrict data set membership, creating potentially incomplete, incompatible, or inconsistent data sets, "...the classification categories employed, and the relationships among those categories (nominal, ordinal or scalar)." '''note-34 [27]]'''
* Concept uncertainty '''note-35 [28]''' like systematic error discussed above is notoriously difficult to recognize, except on theoretical grounds, and the magnitude and direction of the bias are known.
Both terms could be analogized to that of [https://en.wikipedia.org/wiki/Accuracy_and_precision#Terminology_of_ISO_5725 accuracy] in [https://www.bipm.org/en/publications/guides/ ISO 5725] which looks at the ability of the concept or system's ability to produce results that are proximate to the "true" value or state of the phenomenon. Conceptual uncertainty and systemic bias are related to another measure of uncertainty: validity. Walker has defined validity as the ability of a concept to quantitatively describe and explain what the phenomenon objective "truly" is. Walker also ties the concepts together when he states, "Validity concerns the "accuracy" of the measurement data, not its precision." (Walker, 1998) 
Additionally, conceptual uncertainty and associated validity issues could be propagated throughout this taxonomy. Every component of action and decision in the face of uncertainty raises validity issues. Walker recognized this when the author defined epistemic choice for concepts used throughout the other uncertainty components or layers.
* '''Measurement''' uncertainty or Misclassification error- the application of the underlying concepts or variables to specific, individual cases and the uncertainty of the reliability (Walker, 1998) of the resultant data. (It is important to remember that assessment observation and measurement are used interchangeably in this analysis.) Thus, an assessment method or procedure is considered "reliable" or "precise" in the scientific terms of ISO 5715 if it repeatedly produces consistent results.
* Measurement uncertainty is logically distinct from conceptual uncertainty, which can only occur after any conceptual uncertainty has been established. Still, misclassification can occur even when the conceptual uncertainty has been minimized. Conversely, underlying errors or errors made in measurement or classification propagate thru the reasoning chain and reduce the quality or value of the final result.
* While uncertainties associated with quantitative measurements or assessments can be statistically evaluated and described, uncertainties related to qualitative assessments are more problematic.
**Working through the taxonomic chain from base concept to causal explanation requires amassing and integrating qualitative information and quantitative data into input parameters for mathematical models. Developing procedures for producing consistent, repeatable sets of information from assessments that minimize conceptual error or systemic bias requires professional efforts to "validate" such methods, models, and judgments at all layers in the process. Validation as a process is something that civil engineering as a practice needs to embrace more frequently.
* '''[https://en.wikipedia.org/wiki/Sampling Sampling]'''- in the classical statistics sense, is the selection of a limited subset of a larger population to use as a basis for making estimates about the population in the form of attributes, proprieties, or parameters. The resulting information would be determinative in making forecasts about future population sampling or possessing a desired amount of predictive power. The uncertainty about the ability of this information to correctly predict future samples is termed sampling error.
* '''Modeling'''- Modeling uncertainty arises whenever a claim is made that out of a class of candidate relationships and variable pairings, one variable pairing inclusive of constants is chosen that possesses a particular or persistent mathematical relationship to another variable X. Modeling errors arise in selecting the wrong variable pair and constants or by incorrectly specifying its constants. (Walker, 1991, pg. 599)
** '''Causal'''- In causal modeling and analysis, the relationship between causal variables is not strictly a mathematical function. Still, it makes testable predictions and explains "...how a system of variables works, why a system works the way it does, or why it makes sense to think of certain variables as a system" at all." (Walker, 1991, pg. 609)
** '''Epistemic'''- the choice of interpretations for fundamental, logical concepts used throughout the other components or layers.
The components of this uncertainty taxonomy can combine in different ways to "...produce different aggregate uncertainties." '''note-36 [29]'''
:''' Only the scientific and engineering professions possess the capacity to develop and validate procedures and models that combine the different kinds of qualitative '''note-37 [30]''' and quantitative information and their associated potential for error into an aggregate measure or "scalar variables" of uncertainty.'''note-38 [31]]
Scalar variables are quantitative variables whose categories are related by some measure of the relevant property's incremental frequency, degree, or amount. (Walker, 1991) Examples are Modulus of Elasticity, Compression stress in elastic materials, etc.

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=== Economics Uncertainty Taxonomies ===
Like scientific concepts of uncertainty, economics is environment-specific (in this case, read sector of economy or markets), phenomenon-oriented (in this case, read human activity and manufactured phenomena such as firms and markets guided by human judgment and decision), and model-centric.
:''' Economics is not interested in all forms of uncertainty, only specific, meaningful subsets possessing casual connections with material impacts, often labeled as 'risks'.'''
One of its earlier writers ([https://en.wikipedia.org/wiki/Frank_Knight#Life_and_career Knight], 1921, 1948, 1957) attempted to define "uncertainty" as the presence of "defects of managerial knowledge" (or [https://en.wikipedia.org/wiki/Knightian_uncertainty knightian uncertainty] and thus risk centric) as risk that is immeasurable, not possible to calculate.(Wikipedia) Risk in this sense was defined as "the ordinary risks of business activity which can ... be reduced... by applying the insurance principle." Knight does acknowledge that risk is a specific and unambiguous subset of uncertainty. (Risk, Uncertainty and Profit, 1957, pg. 19) For Knight, measuring uncertainty meant measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences, which Knight referred to as "the imperfection of knowledge". (Risk, Uncertainty and Profit, 1957, pg. 197) One of Knight's many contributions to this analysis was recognizing information and knowledge's role in economic activity.
:"''(W)e are concerned only to emphasize the fact that '''knowledge is in a sense variable in degree and that the practical problem may relate to the degree of knowledge rather than to its presence or absence in toto.''' We live only by knowing something about the future, while the problems of life, or conduct at least, arise from knowing so little. This is as true of business as of other spheres of activity. The situation's essence is action according to opinion, of greater or less foundation and value, neither entire ignorance nor complete and perfect information, but partial knowledge. If we are to understand the workings of the economic system, we must examine the meaning and significance of uncertainty; and to this end, some inquiry into the nature and function of knowledge itself is necessary.''" (Knight, pg. 199) Emphasis added
Knight also reinforced the difficulties in classifying uncertainties when he analyzed life insurance. In this case, accidental death was an uncertain phenomenon compared to sickness and accident, where an "...objective description and classification of cases was impossible..." (Knight, pg. 248) Another factor that made uncertainties ineligible as risks and, therefore, uninsurable because they were unclassifiable was the exercise of judgment in making decisions by the businessman. (Knight, pg. 251)
This is a simple classification of economic uncertainty and, as an idealization, is philosophically controversial. '''note-39 [32]'''
* Uncertainty and information about the economic environment are distinct from uncertainty about others’ behavior or choices.
* Risk as a specific subset of uncertainty implies that "...all possible acts are known, all possible outcomes arising from each act are known, and it is possible to assign probabilities to each act." '''note-40 [33]'''
Like the economic [https://en.wikipedia.org/wiki/Theory_of_the_firm theory of the firm], an economic theory of uncertainty seeks to explain and predict the nature of a specific subset of uncertainty (in this case, risk relating to physical phenomena and human activity), including its behavior, structure, and relationship to the universal uncertainty of physical phenomena. In this sense, uncertainty, like firms and markets, can establish price equilibriums under the right circumstances different from those that might have occurred in either of the other two. Such a theory of uncertainty offers a partitioning scheme that decomposes uncertainty into distinct and meaningful subsets, of which risk is a significant one. It also explains how the presence and variability of knowledge cause or " drive" economic consequences. Unlike physical phenomena, economic activity creates artificial objects such as products, firms, mediums of exchange, markets, economies, and knowledge about those activities. The economics of uncertainty exists as an alternative to "certainty" models that assume away imperfect knowledge and unknown choice/judgment preferences when it is more efficient for economic decision-making. This also allows the introduction of the logical concept of "hazard" versus "risk". Frank used hazard extensively in Risk, Uncertainty, and Profit, but not to the extent that one could say it was interchangeable. An example of this is in the term "[https://en.wikipedia.org/wiki/Moral_hazard Moral hazard]," where a firm could engage in "riskier" and potentially "uninsurable" behavior if the information were transparent, and, therefore, insurance coverage of losses is limited. note-41 [34]], note-42 [35]]

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=== Uncertainty and Risk in Policy and Regulatory Environment ===
:''"Hume's 'just reasoner', when faced with a difficult public policy decision in current times, would probably commission a risk analysis. But how could they incorporate 'a degree of doubt, caution, and modesty?"'' '''note-43 [36]'''
The problem of "decision-making in the face of uncertainty" is how to regulate based on incomplete information that has the potential to be materially inaccurate. A contextual assumption is that we need to evaluate decision rules for dealing with uncertainty. As a social enterprise, risk regulation, whatever its substantive objectives, should be as '''effective, efficient, and equitable''' as practically possible. These "three E's" form a set of "process objectives" or "meta-goals." From the uncertainty standpoint, causal information can be usefully divided into two major categories: information about groups and information about individuals.

Each category, group, and individual has its distinctive types of inherent uncertainty. These are logically distinct, generally independent, and cumulative, contributing to '''(?????)'''

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=== Engineering Uncertainty Taxonomies ===
Like scientific and economic concepts of uncertainty, engineering is environment-specific, phenomena-oriented, model-centric, external choice, and driven by decision-making in the face of uncertainty. Like economics and unlike science, engineering is not interested in all forms of uncertainty, only those with determinable and material consequences. Like economics, engineering conceives risk as a specific and unambiguous subset of uncertainty determined by measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences. Also, engineering recognizes the role information and, by extension, knowledge plays in constructing the built environment.
'''note-44 [37]]''' Like economics, engineering differs from economics and scientific uncertainty in its interest in behavioral uncertainty.
*"Behavioral or interaction uncertainty is how individuals or organizations act or interact. Behavioral uncertainty arises from four sources: design uncertainty, requirement uncertainty, volitional uncertainty, and human errors." '''note-45 [38]]'''
** A design uncertainty is a choice among alternatives over which an individual or group of individuals exercises direct control but has not yet decided upon.
** Requirement uncertainty includes parameters of interest to and determined by the stakeholder, independent of the engineer or designer.
** Volitional uncertainty is uncertainty about what the subject him/herself will decide. Other people’s future actions and conduct are not entirely predictable, particularly when dealing with other organizations.
** Human errors occur during the development of a system or project due to blunders or mistakes by an individual or individuals.

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==Practice Frameworks for Uncertainty and Risk in Civil Engineering Practice==
===PMIBoK===
PMI does not define uncertainty, but the PMI Body of Knowledge uses the term in 45 places (uncertainty) and as describing properties in 8 places (uncertainties). [See Note 8] PMI does define "risk" and uses the term over 1,300 times in the same document. In its glossary, PMI also defines terms such as "threat" and "opportunity" but not events.[391 The BoK contextually defines uncertainty when it states that project risk has its origins in the uncertainty present in all projects. (Op. Cit., pg 309) 
'''Uncertainty''' is contextually defined and taken as having the property of affecting project execution and the ability to meet stakeholder expectations. Uncertainty is more than the sum of all individual risks (known and unknown). The nature of this uncertainty is not defined, only its capacity to affect something else or its properties. 
'''Risk''' is defined as " ... an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives." (Ibid.) Beyond this formal definition, PMI adds context when, in its risk management section (Sec. 11 ), the BoK states that risk is a caused event or condition with multiple causes and impacts. The BoK even offers specific techniques for mapping cause-and-effect relationships and influence diagrams. (Sec. 11.2.2.5) Not as evident but equally important is the recognition that not only is risk caused but the impact is "triggered". The risk trigger is an event or situation that signals that it is about to occur (Glossary, pg. 566). Risk conditions are factors that affect or otherwise contribute to risk, such as project stakeholders. Risk implicitly retains some residual element of probability in it, or a value of less than 1.0 with risk that approaches a level of near certainty is termed "issues" or "realized risk" .(Op. Cit., pg 309). PMI also links risk to underlying variations in project outcomes. (Ibid.) Risk is based upon data and information which can be assessed as to its quality (Sec. 11.3.2.3). This analysis looks to determine the value of the information and examines the degree to which the risk information is understood as well as its " ... accuracy, quality, reliability and integrity ... " (Ibid.) Part of that is understanding the relationship between occurrence and impact as outlined in PMl's Figure 11-10 Risk Impact Matrix and developing a model for prioritizing risk products and setting the threshold for action.
====Commentary:====
For defining and identifying risk, PMI notes in Sec. 11.2.2.4 that " ... Every project and its plan is conceived and developed based on a set of hypotheses, scenarios, or assumptions." Part of the risk analysis process is identifying risks to the project, such as inaccuracy, instability, inconsistency, or incompleteness of assumptions. Similarly, the quality of management plans, as well as their consistency with others and the project objectives and assumptions are 11 •• .indicators of risk in the project." (Sec. 11.2.2.1) 
Similarly, the role of engineering economics in risk is embedded or implied in the PMI definition of risk when the BoK discusses risk identification as a process of assessing which risks out of the total risk exposure for the project "may affect" the project and distilling their characteristics. Embedded in this statement is the concept that not all risks can cause economic impacts (positive and negative) to the project. Also embedded is the requirement to understand risk characteristics to identify project risk (Cost and schedule). Lastly, risk identification is an iterative process of incrementing project risk documentation, much like project scope, cost, and schedule documentation. 
Lastly, PMI acknowledges the complex constitution of risk aggregates or the "sum" of all risks in its material, but is it a sum? Risk information has descriptive and explanatory content. These are knowledge and meta-knowledge components. Understanding project assumptions is project-specific knowledge, but identifying risks from assumption inaccuracy, instability, inconsistency, or incompleteness requires professional or program meta-knowledge. 
'''Uncertainty exists in all projects, but risk, a subset of uncertainty with material economic impacts, is a key meta-knowledge element for civil engineering practice.'''
===Software Development Process- Risk Focused===
The Unified Process requires the project team to focus on addressing the most critical risks early in the project life
cycle. The deliverables of each iteration, especially in the Elaboration phase, must be selected to ensure that the greatest risks are addressed first. <ref> Booch, Grady, Ivar Jacobson, and James Rumbaugh. "The unified software development process."
Reading: Addison Wesley (1999).</ref>
===American Society of Civil Engineers (ASCE)===
ASCE does not define uncertainty, but the ASCE CE Body of Knowledge (CEBoK) uses the term in 22 places (uncertainty) and describes properties in 13 places (uncertainties). 
The CEBoK does not use the phrase "uncertainty and risk" as PMI and AACE do in their publication. ASCE reverses the two (Risk and uncertainty) in twelve places regarding technical outcomes (Outcome 12). A fundamental difference between PMI and ISO is that risk is considered a subset of uncertainty. ASCE does not define "risk" but uses the term 25 times in the same document. Almost all are in the context of variation of design parameters and not in the classical context of cost, schedule uncertainty, and risk.
====Commentary:====
...
=== American Association of Cost Engineers (AACE) ===
AACE defined uncertainty, risk, and other related terms in its Risk Management Dictionary. <ref>Risk Management Committee uRisk Management Dictionary, Cost Engineering Vol. 37/No. 10, OCTOBER 1995. </ref>
AACE's overall strategy was to define risk and other terms that stem from base uncertainty. AACE first defined uncertainty as .... "(t)he total range of events that may happen and produce risks (including both threats and opportunities) affecting a project (see opportunities, events, conditions, risk, and threats" where the following sub-definitions apply:
** Biases--A lack of objectivity based on the individual's position or perspective. Systematic and predictable relationships between a person's opinion or statement and his/her underlying knowledge or circumstances. Note: there may be "system biases" as well as "individual biases."
** Condition (Uncertain Condition)-Any specific identifiable circumstance (such as the rate of inflation or the quality of labor available) that might affect the outcome of the project
** Event (Uncertain Condition)-is a specific identifiable action (such as a large government project being started in the same labor area as your project) or an act of nature that might happen and that (if it does happen) could affect the outcome of the project.
** Opportunities are Uncertain events that could improve the results or improve the probability that the desired outcome will happen
** Threats are Uncertain events that are potentially negative or reduce the probability that the desired outcome will happen.
*** AACE defines Risk as an "ambiguous term" (sic) that is synonymous with uncertainty or a negative subset such as "threats", or an overall negative impact of all possible uncertainties.
====Commentary:====
...
==Limitations of the definition==
...
==Working definition==
...
==Beneficial outcomes==
...
==See also==
Wiki articles on the project.

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==Notes==
<references group="note" />
<li> Implicit in this definition of uncertainty as a "state of' is ... </li>
: " ... a conceptualization of uncertainty as a subjective, cognitive experience of people--a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one's lack of knowledge, without which one could not feel uncertain; '''uncertainty is a form of "meta-cognition" ... ( or alternatively, its main component, meta-knowledge) ... -a knowing about knowing.'''" (Han, 2011, op. cit., Emphasis added)
</ol>
<ol start="5">
<li> Cox wanted his system to satisfy the following conditions:</li>
: Divisibility and comparability- The plausibility of a statement is a real number and depends on the information we have related to the statement.
: Common sense - Plausibilities should vary sensibly with the assessment of plausibilities in the model.
: Consistency - If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). -preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf
</ol>
<ol start="6">
<li> The utility of existing taxonomies of uncertainty in civil engineering and, by extension, "risk" has been unsatisfactory for this very reason. This is particularly true in cases where the source of the uncertainty is conflicting versus incomplete information. See Regan, et. al.,"A taxonomy and treatment of uncertainty for ecology and conservation biology" (2002)
for a survey of scientific taxonomies for uncertainty.</li>
<li> Regan et al. argue that " ... genuine examples of this kind of uncertainty are hard to find. Even classic cases of random experiments like coin tosses and the throwing of dice are deterministic; it is just that we do not have enough information about the dynamic processes and initial conditions to make any sensible estimates about the outcomes. Such processes are, for all intents and purposes, inherently random, but they are not genuinely inherently random. For similar reasons, complex systems such as ecosystems and weather patterns are unlikely to be inherently random. Similarly, chaotic systems are entirely deterministic. They are unpredictable because the deterministic processes generating them and the relevant initial conditions are hard
to fully specify (see Stewart 1989, Sugihara et al. 1990)." (Regan (2002) </li>
<li> The CEBoK uses the phrase "uncertainty and risk" twice in reference to scheduling (Section 6.5,2) and cost estimating (Section 7.2,2) as well as reversing the two (Risk and Uncertainty) also in two places in discussing project life cycle (both in Section 2.4.1) </li>
</ol>

==References==
<references />

Uncertainty and Risk in Civil Engineering Practice

2024-11-24T16:24:56Z

WikiSysop:

'''Life is short and the art long; the occasion instant, experiment perilous, decision difficult.''' (Hippocrates as quoted in Fox,)
:''See also [[Project Definition]] , [[Project Life Cycle and Phase Models]] , [[Project Delivery Methods]]''
:''See also [[Technical Outcomes for Civil Engineering:Risk and Uncertainty]]''
==Basic Considerations==
As human beings,'' ... being alive means seeking opportunities and taking risks.'' As knowledge professionals
living in the 21st century, this means coping with an increasingly complex number of uncertainties for humans
living in this environment. We seek to understand better how these uncertainties can be characterized and
managed. The essence of this article and the experience for engineers in general and civil engineers in particular is
that manageable uncertainty is, by definition, termed risk, and its kindred cousin is termed hazard. This causes us to experience the
:" ''... human dread of and fascination for risk and the increasingly important role of risk analysis within societies ...'' " <ref name="McDaniels"> McDaniels, Timothy, and Mitchell Small. Risk analysis and society: an interdisciplinary characterization of the field. Cambridge University Press, 2004, page 1 </ref> (Ibid.)
and, by extension, civil engineering. McDaniels et al. argue that risk management has been
fundamental to our social and governance development for the past 10,000 years. The scale and shape of the
uncertainties faced in this period shaped the societies that have developed today. The central thrust of this effort
over the centuries has been to reshape and re-frame our understanding and conception of uncertainty from one of
complete unknowing and simple acceptance as our fate in life to one of management (cf "''Against the Gods''"
concept in Bernstein's book <ref name = Bernstein">Bernstein, Peter L., and Jesse Boggs. Against the gods. Simon & Schuster 1997.</ref>.
: ''All the knowledge professions and disciplines have struggled with managing uncertainty, for it is impossible to manage unbounded uncertainty.'' <ref group="note">See McDaniels et al. 2004 for an extensive discussion and bibliography on the historical development of Risk Analysis and some key milestones in risk analysis in the 20th century.</ref>
As outlined below, professions such as civil engineering have successfully acted in the face of uncertainty. It lies in the profession's ability to reduce a wide variety of uncertainties into increasingly smaller and crucially bounded subsets that can be managed. These are called 'risks. More recently, this process has evolved, and individual disciplines such as Civil Engineering (CE) have developed their knowledge and models to perform risk analysis. Risk is also taught as a distinct discipline and specialty practice within civil engineering. Still, it has some unique features that set it apart from other more classical practices within CE. As such, it is one of the first of some very specialized CE practices that use knowledge about civil engineering knowledge, or meta-knowledge. Other examples of civil engineering metaknowledge are project controls and quality controls.
==Semantic, Epistemic and Logical frameworks==
The semantic, epistemic, and logical frameworks for uncertainty and risk have several dimensions and layers of logical frameworks. The semantics problems include interchangeable usages for risk and uncertainty and hazard, uncertain and imperfect, and a lack of definitional material or context. The semantical scheme for this article will be to proceed from the distinction between certainty and uncertainty through marginal refinements and reductions of uncertainty up to the point of causal uncertainties. Beyond this point of semantics will be the logic frameworks or the subject of taxonomies of professional knowledge. First, it is about simple, testable phenomena, and then, it moves on to complex taxonomy schemes for artificially constructed phenomena or engineering projects.
<ref group="note">TBD</ref>
==Semantics framework==
===Certainty and Uncertainty===
''''' There is no such thing as absolute certainty, but there is assurance sufficient for the purposes of human life.''''' ([https://en.wikipedia.org/wiki/John_Stuart_Mill John Stuart Mill]) 
'''''If you tried to doubt everything, you would not get as far as doubting anything. The game of doubting itself presupposes certainty.''''' ([https://en.wikipedia.org/wiki/Ludwig_Wittgenstein Wittgenstein] # 115 from On [https://en.wikipedia.org/wiki/On_Certainty Certainty]) 
It is important to note that the references to epistemic or epidemiological knowledge in this article are assumed to relate to three forms of knowledge, namely:
* Knowledge that ( [https://en.wikipedia.org/wiki/Descriptive_knowledge descriptive or declarative or propositional knowledge])
* Knowledge how ( or "[https://en.wikipedia.org/wiki/Procedural_knowledge know-how]"), and
* [https://en.wikipedia.org/wiki/Knowledge_by_acquaintance Knowledge by acquaintance].
'''Certainty''' has been defined as "an epistemic property" of knowledge in all its forms and the state of our beliefs about that knowledge.
<ref>Certainty, Stanford Encyclopedia of Philosophy, accessed on September 12, 2015, at http://plato.stanford.edu/entries/certainty/#ConCer </ref>
Certainty about any belief about knowledge implies that it is not subject to doubt or skepticism. This immunity to criticism can be dogmatically based, emphasizing the importance of a propositional-based sense of truth over experiential, sensory perceptions. <ref>Definition of [https://en.wikipedia.org/wiki/Dogmatic_theology Dogmatic theology or belief], accessed at Wikipedia</ref>
: '''The demarcation line between dogmatic and non-dogmatic beliefs lies in the presence and recognition of specific criteria and information that would make the believer change their beliefs.'''
An empirical framework of [https://en.wikipedia.org/wiki/Testability testability] and [https://en.wikipedia.org/wiki/Falsifiability falsification] is required to recognize this and limit dogmatism. For example, one could argue that people would change their minds if God asked them to. Similarly, one could construct a concept of epistemic as opposed to dogmatic belief certainty as the ability to know anything that one chooses to know and can be known or [https://en.wikipedia.org/wiki/Omniscience inherent omniscience].

A second kind of certainty is epistemic, when conviction reflects the highest possible support for a belief. In this sense, knowledge is separate from beliefs, although someone may have beliefs about a property, such as certainty of knowledge. Logically, it has been shown that there will be unprovable statements within the system for any such knowledge system. Secondly, the knowledge system cannot demonstrate its own consistency. ([https://en.wikipedia.org/wiki/Gödel%27s_incompleteness_theorems Gödel's incompleteness theorems])
: '''Certainty, in real life, is useless or often damaging (the idea is that "total security from error" is impossible in practice, and a complete "lack of doubt" is undesirable)''' ([[https://en.wikipedia.org/wiki/Certainty Physicist Carlo Rovelli]])
'''Uncertainty''', on the other hand, arises immediately in the slightest amount of doubt or criticism.
<ref group="note"> The Merriam-Webster dictionary defines uncertainty as “the quality or state of being uncertain,” which is something of a circular definition. Likewise, one could define uncertainty as the state of not being certain. Synonyms are distrust, doubt, misgiving, mistrust, reservation, skepticism, and suspicion. Another writer added indefinite, indeterminate, not certain to occur, problematical, unreliable, untrustworthy, unknown beyond doubt, dubious, doubtful, not clearly identified or defined, not constant, variable, and fitful to the list. (Han, Paul KJ, William MP Klein, and Neeraj K. Arora. "Varieties of Uncertainty in Health Care: A Conceptual Taxonomy." Medical Decision Making 31.6 (2011): 828-838.)Han, et. al. noted that any definition of uncertainty clearly encompasses "...numerous types, sources, and manifestations of uncertainty, and ...(any) ...useful working definition of uncertainty needs to specify the concept underlying these varied meanings of the term."</ref>
Implicit in this definition of uncertainty as a “state of” is ...
"...a conceptualization of uncertainty as a subjective, cognitive experience of people—a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one’s lack of knowledge, without which one could not feel uncertain; uncertainty is a form of “meta-cognition” ...(or its main component, meta-knowledge)... —a knowing about knowing." (Han, 2011, op. cit., Emphasis added)[Note 3]
Similarly, uncertainty could be defined as "...any departure from the unachievable goal of complete [https://en.wikipedia.org/wiki/Determinism determinism]." <ref name="Reference5">[5]</ref> Reasoning under uncertainty is very different than performing the same under certainty. In reasoning under certainty, one has complete knowledge and deduces without doubt and equally important, without limitation, thereby concluding free from error. Reasoning under uncertainty, one works in a state of incomplete, inconsistent, and limited knowledge; doubts cloud any statements or assertions and thereby taint any deductions/inferences resulting in the potential for error. <ref name="Note4">[Note 4]</ref>
:'''Knowledge professions reason in a state of incomplete, inconsistent and limited knowledge with doubts that cloud any statements or assertions and taint any deductions/inferences resulting in the potential for error.'''
:''' It is also important to note the semantical and logical correlation between 'reasoning under uncertainty' or 'acting in the face of uncertainty' with the 'potential for' or 'presence of' error. The presence of uncertainty is invariably linked to the potential presence of error.''' The [https://en.wikipedia.org/wiki/Contingency_(philosophy) contingent] nature of uncertainty logically implies the contingent nature of error.
:'''Knowledge Professions use error as a proxy for uncertainty such that within discipline knowledge frameworks, uncertainty can be managed.''' The rationale for this belief is that error can be reliably described, quantified, explained, and ultimately reduced in ways that uncertainty cannot.

'''Uncertainty''' implies a range of variation that is impossible in certainty. Concepts and definitions of uncertainty are unbounded and vast but start from the point of complete and total ignorance. In describing uncertainty, statements can range from complete ignorance to relatively high degrees of belief that there is less potential for error in our reasoning or action. The latter is based upon confidence in justified knowledge, favorable past experience, and reliable parameters/models. One can't make that statement about ranges in certainty. If certainty, by definition, precludes doubt of any type or nature, then how could that be graduated to any degree? 
Uncertainty, on the other hand, can be reduced through disciplined efforts to acquire knowledge. In short, we can reduce or even manage uncertainty (to a degree); how can certainty be improved? Are there higher degrees of perfection? The answer is no. We reason from an uncertain starting point. Yet this effort presumes, as Mill and Wittgenstein postulated, that we can develop or acquire an increasing degree of belief or conviction that there is less potential for error in our reasoning or action. This position assumes that some way exists to identify what a lesser degree of uncertainty would look like.
:'''Knowledge professions acquire an increasing belief or conviction that there is less potential for error in their reasoning or action based on justified knowledge, favorable past experience, and reliable parameters/models.'''
Uncertainty is, in some form, amenable to description and explanation through observation and analysis. It is explainable and predictable to such a degree that it interests professionals such as scientists and engineers. Professional knowledge of this type has explanatory or predictive power, albeit limited in scope and application to relevant subjects such as physical objects or phenomena and functionality.
:'''The very act of reducing the scope of uncertainty to a limited set of phenomena implies choice and, ultimately, the decision to act in the face of such uncertainty, but acting or judging in this manner adds more value than doing the same under relative ignorance.'''
'''Uncertainty''', as described above, implies a hybrid nature. Examples of this are economic or decision-theoretic applications. In economics, [https://en.wikipedia.org/wiki/Perfect_information perfect information] allows one within the limiting framework of perfect competition in economic games to make decisions with 'perfect knowledge.' The crucial difference here is the distinction between '[https://en.wikipedia.org/wiki/Perfection perfection],' broadly, a state of completeness and lawlessness, and 'perfect, which can be thought of as an analytical limit that is approachable but can never be attained. Arbitrarily delimiting the realm of knowledge into defined, finite boundaries allows the simulated production of perfect choice, assumed crucially, to be free from error. The hybrid nature of this form of 'simulated' certainty produced within boundaries defined by assumptions and, therefore, clouded by doubt is itself a form of uncertainty. The importance of this is the semantical and logical association that exists between certainty and perfection versus the hybrid concept of a perfect anything, whether knowledge, infraction, competition, etc., or in the case of engineering, elastic, permeable, conductive, etc., being contained within an admittedly imperfect matrix of uncertainty.
:'''Simply put, a finite, heavily bounded piece of uncertain knowledge can be improved through the simulated use of [https://www.merriam-webster.com/dictionary/assumed assumedly] perfect information, but the perfection of knowledge cannot be attained or simulated.'''
Engineering and Economics, for example, simulate finite elements of perfect knowledge or information, heavily bounded with assumptions and a range of applications. By doing so, these professions reduce the bounds and variability of uncertainty and thereby manage it. Using these hybrid models to acquire knowledge takes place in an economy (regulated or institutional) and environment (transparent and structured), which puts pressure on the various professions to recognize and use qualitative and quantitative knowledge better to reduce uncertainties in their activities. The challenges in this reflect the nature of the profession's or discipline's knowledge (e.g., scientific, engineering, or medical), the policies and structure of the relevant professional knowledge bodies, process objectives and constraints, and "the elusive demands of politics." [6] One common theme runs through all of these efforts to address the presence of uncertainty in professional activities. 
:"The available scientific information upon which a decision must be made is almost always a mixture of engineering knowledge and uncertainty. Regardless of the information or methodology, there is the potential for error. This is true regardless of the relevant science, whether archaeology or aeronautics, economics or engineering, pharmacology or toxicology or epidemiology. Achieving the best social decisions requires not only understanding and using what we know but also appreciating and weighing the extent of our uncertainty. In making the best use of ... information in ... decision-making, it is still true that the beginning of wisdom is knowing what it is we do not know." (Vern, Op. cit., Reformatted and engineering substituted for scientific) 
'''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty to develop explanations, calculate, and make predictions. This professional knowledge converges on but never reaches certainty, producing a "legitimacy" from the fruitfulness of its use.''' <ref>Randomness Is Unpredictability, Antony Eagle, The British Journal for the Philosophy of Science, Vol. 56, No. 4 (Dec., 2005), pp. 749-790 </ref> 
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=== Bias and Uncertainty ===
Up to this point, the concept of error, as outlined in uncertainty, could easily be simulated by a truly random variable. There should be no detectable differences around the unseen or specified statistical mean. However, several instances in experience point to a tendency towards one extreme or a pattern of error, a collective that is in itself an error of errors, namely [https://en.wikipedia.org/wiki/Bias bias].

Arguably, this could be part of the choice uncertainty discussion below, but it makes more sense to be looked at on its own. Acting in the face of uncertainty is an exercise of personal knowledge or experience. Whether using professional knowledge or individual experience, the potential is there to make a series of choices that, in some instances, make errors more likely than they would be if considered in the context of a statistical analysis.

This tendency for an individual to make the same error in a predictable or repeatable manner is termed bias. Personal experience, beliefs, knowledge, data, and models all have inherent built-in factors, errors, or defects that create predictable error patterns when applied in decision-making. Walker (1998) argues implicitly that such bias can also be considered systemic error. This type of uncertainty is difficult to identify except on theoretical grounds when the magnitude and direction of the bias are known. Most of these biases, whether systemic or knowledge-based, individual or practice-based, require disciplined efforts to "validate" such methods, models, and judgments at all layers while bounding the uncertainty.
: This is the challenge to professional knowledge that must be overcome, namely, developing and validating models and methods for managing bounded uncertainty that minimize this tendency towards repeatable groups of error or bias. Therefore, from this point forward, the discussion will refer to 'uncertainty and its proxies, error, and bias.'
===Propagating Error and Uncertainty===
Partitioning uncertainty and its proxies, error, and bias into logically distinct subsets involves or is associated with multiple-step execution or choice frameworks. Combining those measurements or choices into single parameters or choices involves, in some cases, assembling the data/information in a unique or limited range of sequences. This implies that associated errors in observing, calculating, or choosing will have unequal impacts or consequences depending on where in the chain the error occurs. Also implied in this is the possibility that subsequent errors will be causally linked to some degree. (In statistical practice, this depends upon whether the errors are independent of each other or correlated, specifically, co-variant.)
:'''Professions develop knowledge of how uncertainty and its proxies, error, and bias propagate through an ordered and normative series of observations or choices. This allows the profession to prioritize error and bias reduction to achieve an optimized reduction of uncertainty.'''
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===Cognition, Metacognition and Uncertainty===
The concept of choice and the resultant error in uncertainty can be further delimited depending on the role of [https://en.wikipedia.org/wiki/Cognition cognition], [https://en.wikipedia.org/wiki/Metacognition meta-cognition], and independent external elements. The rationale for this argument is that such choice activity in the face of uncertainty is a conscious, cognitive act.

An argument could be made that such cognition is a prerequisite for exercising choice and introducing error, as presented and discussed above. The counterfactual to this argument is the example of uninformed choice. Choices introduce as much as errors or more as informed choices could be made. (???) Choice, in this sense, is indifferent to knowledge. Purposeful choice in an environment of objectives requires knowledge, method, and experience, but uninformed, speculative choice does not.
:"''' Cognition''' is the set of all mental abilities and processes related to knowledge, attention, memory and working memory, judgment and evaluation, reasoning and "computation," problem-solving and decision making, comprehension and production of language, etc. Human cognition is conscious and unconscious, concrete or abstract, as well as intuitive (like knowledge of a language) and conceptual (like a model of a language). Cognitive processes use existing knowledge and generate new knowledge." (Wikipedia)
:"'''Metacognition''' is "cognition about cognition", or "knowing about knowing" and can take many forms. It includes knowledge about when and how to use particular strategies for learning or problem-solving. There are generally two aspects of [https://en.wikipedia.org/wiki/Metacognition metacognition]: knowledge about cognition and regulation of cognition."
Some types of metacognition knowledge are:
:'''Person knowledge''' (declarative knowledge), which is understanding one's own capabilities.
:'''Task knowledge''' (procedural knowledge), which is how one perceives the difficulty of a task, which is the content, length, and type of assignment.
:'''Strategic knowledge''' (conditional knowledge) is one's capability to use strategies to learn information. (Accessed at Wikipedia)
Like metacognitive knowledge, metacognitive regulation or "cognitive control" contains three essential skills.
:'''Planning:''' refers to the appropriate selection of strategies and the correct allocation of resources that affect task performance.
:'''Monitoring:''' refers to one's awareness of comprehension and task performance.
:'''Evaluating:''' refers to appraising the final product of a task and the efficiency at which the task was performed. This can include re-evaluating strategies that were used. (Accessed at Wikipedia)
Nothing has been said up to this point that limits this structure to an individual, or a collective, or two entities making cognitive choices that are incompatible or inconsistent to varying degrees. Interpretations of this scheme include an individual professional exercising judgment in a decision that, in turn, relies upon the collective judgment and decision of the discipline as an underlying basis versus the decision of another individual to oppose/protest or otherwise contest that decision. Thus, the framework of cognitive/meta-cognitive applies to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, economics of regulatory practice, and game theory.
:By solving useful problems in the face of inherent uncertainty, discipline-specific frameworks of cognitive/meta-cognitive activity apply to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, the economics of regulatory practice, and game theory.
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===Probability and Uncertainty===
Probability is a [https://en.wikipedia.org/wiki/Coherence_theory_of_truth coherent] approach to uncertainty in the physical and mathematics or "classical" domains such as engineering. [8] and is the "...is certainly the best-known and most widely used formalism for quantifying uncertainty.[9] [https://en.wikipedia.org/wiki/Cox%27s_theorem Cox’s theorem] ("Any measure of belief is [https://en.wikipedia.org/wiki/Isomorphism isomorphic] (but not necessarily equal) to a probability measure") is a well-known argument for the validity of that argument. [Note 5]

Another writer, [https://en.wikipedia.org/wiki/Frank_Knight Knight] (1921,1956), presented the following taxonomy of probabilities: [10]
: '''Classical 'a priori' probability:''' As an idealized model, numerical probabilities are computed based on generalized principles of assigning equal likelihoods and mutually exhaustive possible outcomes (Runde, 1998) to all set members. Such probabilities are assigned to "...outcomes based on a judgment of indifference between those outcomes, that is, based on the absence of any evidence of real influences in play that may render any one outcome more or less probable than any other." (Runde, op. cit.) This builds upon Knights's concept of an 'absolutely homogeneous classification of instances that are completely identical except for [https://en.wikipedia.org/wiki/Indeterminate_(variable) indeterminate] factors. This judgment of probability or logical probability is on the same logical plane as the propositions of mathematics and ultimately [https://en.wikipedia.org/wiki/Inductivism inductions] from experience.'(Knight, 1956, pg.225)
: '''Bayesian 'a priori' probability:''' In contrast to interpreting probability as the "frequency" or "propensity" of some phenomenon, [https://en.wikipedia.org/wiki/Bayesian_probability Bayesian probability] is a quantity that we assign to represent a state of knowledge or a state of belief. In this view, probability is assigned to a hypothesis, often using the basis of past or prior experience. In contrast, under the frequentist view, a hypothesis is typically tested without being assigned a probability based on prior experience. The Bayesian interpretation of probability can be seen as an extension of propositional logic that enables reasoning with hypotheses, i.e., propositions whose truth or falsity is uncertain. Bayesian probability belongs to the category of evidential probabilities; to evaluate the probability of a hypothesis, the Bayesian probabilist specifies some prior probability, which is then updated in the light of new, relevant data (evidence). The Bayesian interpretation provides a standard set of procedures and formulae to perform this calculation. [11]
: '''Statistical or 'a posterior probability:''' Empirical evaluation of the frequency of association between predicates, not analyzable into varying combinations of equally probable or logical, 'a priori' alternatives. Knight argued that any "high degree of confidence" that the judgment that experience will remain valid for future predictions is "...still based on an 'a priori" judgment of indeterminateness." (Knight, Op. Cit.) Knight's argument to support this is what will be discussed further below that first, "...the impossibility of eliminating all factors not really indeterminate; and, second, the impossibility of enumerating the equally probable alternatives involved and determining their mode of combination so as to evaluate the probability by a priori calculation." (Knight, Op. Cit.) Knight noted that the main difference between this form and that of logical or inductive probability was the presence of empirical information, which allowed the analyst to identify propensity and direction in the observed phenomena. In this case, 'a priori' probability values may be derived from first principles and "statistical probabilities are determined a posteriori by the empirical method of counting instances." (Runde [1998] quoting Knight, op. cit.)
:'''Estimates:''' The distinction here is that there is no valid basis ('a priori' or 'a posteriori') for any classifying phenomena. For Knight, this form of probability presented the greatest logical difficulties of all ... but its distinction from the other types must be emphasized, and some of its complicated relations indicated..." (pp. 224-5, emphasis in the original)

Arguably, Knight's concept of probability can be viewed as "..a continuum of probability situations, depending on the degree of homogeneity of the 'instances' in question" [12] ; i.e. going from a logically distinct but equally likely set of elements to a unique set with one member with statistical frequency in the middle. Knight's main motivation for distinguishing between a priori probability and statistical probability underscores his opinion that "(i) the 'mathematical or a priori type of probability is practically never met with in business, while the second is extremely common'; and (ii) 'the statistical treatment never gives closely accurate quantitative results' (Runde quoting Knight pp. 215-16). Runde argues that Knight couldn't conceive of (empirically tabulated) occurrences that are used in daily commerce classes of instances that we have to make do within the course of everyday economic life could be divided into subclasses of instances that are sufficiently homogeneous to permit the determination of what he calls 'real' probability (p. 217).
:'''In modeling degrees of uncertainty, any measure of belief is isomorphic but not necessarily equal to a probability or statistics measure'''.
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===Explanation, Prediction and Uncertainty===
It should be clear that the general notion of uncertainty synonymous with complete ignorance must be bounded as part of a scheme to produce a useful concept of uncertainty in professional practice such as civil engineering.
:The first principle that must be introduced is that such uncertainty must be bounded by reliable and valuable knowledge gained from past experience combined with analytical and computational capabilities to address an immediate and real problem of interest. This could be viewed as the economic interest argument. Only uncertainties associated with physical phenomena and economic scarcity will be addressed.
:The second principle is that the knowledge model is capable of producing statements or assertions in the form of hypotheses that themselves are testable. [https://en.wikipedia.org/wiki/Testability Testability], in this sense, is defined as the property applying to an empirical hypothesis and involves two components:
::The logical property that is variously described as contingency, defeasibility, or falsifiability, which means that counterexamples to the hypothesis are logically possible. Contrast this to a [https://en.wikipedia.org/wiki/Tautology_(logic) logical tautology], which is always true that is true in every possible interpretation. The practical feasibility of observing a reproducible series of such counterexamples if they do exist. In short, a hypothesis is testable if there is some real, non-zero expectation of deciding whether it is true or false of verifiable, reproducible experience. Upon this property of its constituent hypotheses rests the ability to decide whether a theory can be supported or falsified by actual experience data. (Source Wikipedia.)
'''Knowledge models must be capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria described above in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error and bias.'''
===Variability and Aleatory Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge referred to as epistemic uncertainty. Acting in the face of epistemic uncertainty introduces choice uncertainty (discussed below) and results in the production of explanations and predictions. It is implied that there will be testable hypotheses in the form of outcomes of interest to the knowledge professions. Inherent in nature is a variation of results, which may appear to uncertain but isn't. Observations of phenomena will not be the same, and part of the experimental process is to narrow those results down to within an acceptable level of tolerance. Statistical measures seek to identify underlying and unobserved measures of central tendency. These parameters are not directly observed but are derived to a degree of confidence.
In some cases, variation around a mean or within a statistical range may be perfectly acceptable to the knowledge profession. Examples of this are in the physical sciences and medicine. In Engineering and, to a lesser extent, economics, the process or phenomenon may be required to be more controlled to a narrower range than what a natural or "unmanaged" amount of variation would allow. Several courses of action present themselves.
:The first would be to ignore the aleatory phenomenon and choice, which is rejected outright.
:The second would be to assume that, in effect, such variation is itself a form of minimally managed uncertainty and treat it the same as epistemic uncertainty. This is not a desirable outcome as reducing Aleatory error and bias requires
:A third approach would be to acknowledge that process variation is, in some forms, manageable. This means making choices and acting in a way that presumes that the natural occurrence of error can be reduced, or more importantly, bias can be reduced. There is ample precedent for the third approach in civil engineering design and project management. In this sense, the argument is made that the aleatory response to managed efforts results in an elastic variation range. Presumably, an effective management effort either "shifts" the central tendency of the process results or reduces the variation range of occurrence or some other population parameter. Choosing to act and accept that narrower range rather than the broader natural variation may result in an outcome that, while well within the bounds of the expected variation, is outside the arbitrary control limits established by the choice and action. While this phenomenon mimics the broader uncertainty and its proxy error, it is not an error. The information on the variation was known, and the ability to produce narrower results was an error, albeit a pseudo-error, when compared to the broader classes of uncertainty discussed above, such as total ignorance.
::Looking at uncertainty using this third approach means that the error in choosing a new target parameter for this process may contain epistemic errors (our knowledge of the phenomenon may be erroneous, or the model flawed), aleatory uncertainties as discussed, or, more importantly, choice error or bias in thinking that we can move the process results to meet the requirements.
:'''In civil engineering practice, this may mean applying professional judgment in developing partitioning schemes or allocating uncertainty between the three dimensions (Epistemic, Aleatory, and Behavioral) of uncertainty.'''
Lastly, raising the topic like this brings the questions of effectiveness and efficiency to the fore. Advancing knowledge models that reduce error and bias at first are largely choice models resulting in justifications and favorable experiences. At some point in the process, the knowledge profession advances explanations and predictions that require reliable mathematical or logical concepts that are reduced to models and parameters. The question of efficiency becomes important; the broader uncertainty may have been reduced, but the process is economically inefficient. The profession is urged to become more frugal or, for example, reduce the variability of the outcomes. Knowledge professions often operate under conditions that require them to acquire knowledge and reduce uncertainty effectively and efficiently. Doing so requires understanding both epistemic uncertainty, as discussed above, and statistical process variation or aleatory variation.
:'''Knowledge professions manage and reduce uncertainty in the form of error and bias in an economically effective and efficient manner. This effort requires understanding both epistemic and aleatory uncertainty.'''
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===Choice and Behavioral Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge, referred to as epistemic uncertainty, as well as the inherent variations in results, or what was termed aleatory uncertainty. Acting in the face of that epistemic and aleatory uncertainty introduces yet another uncertainty into the process, namely choice uncertainty. This uncertainty reflects uncertainties associated with first-person and third-person choice. This is particularly relevant for engineering with project stakeholders.
===Summary of Semantics Framework for Uncertainty===
Professional disciplines have gradually re-framed their understanding of uncertainty and, thru experience and analysis, have arrived at the following meta-knowledge concepts of uncertainty:
* '''Uncertainty and its proxy, error, have three components: [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic epistemic uncertainty], [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic aleatory uncertainty], and behavioral uncertainty.'''
* '''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty using non-dogmatic beliefs, which recognize the existence of specific criteria and information that would make the believer change their beliefs using coherent approaches such as probability.'''
* '''Knowledge professions develop knowledge of how uncertainty and its proxy, error, propagate through an ordered and normative series of observations or choices that meet testability criteria in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error. An error can then be described, explained, and reduced in ways that uncertainty cannot. This allows the professions to prioritize error reduction to achieve an optimized reduction of uncertainty and produce a "legitimacy" from the fruitfulness of its use.'''
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===Uncertainty versus Risk/Hazard===
Up to this point, nothing has been said about risk or hazard. Now, risk can be defined. Simply put risk or hazard are finite, logically distinct subsets of the broader uncertainty. Risk and hazard are currently defined interchangeably. First and foremost, risk is a specific subset of general uncertainty. Any arbitrary subset of general uncertainty can be defined as a risk if it meets the following criteria:
* Uncertainty is associated with the potential for material error when acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena.
* Uncertainty and its proxy, error, can be partitioned into logically distinct subsets associated with multiple-step execution or choice frameworks in physical environments linked to a unique or limited range of sequences.
**Subcriteria 1:Resulting errors will have varying impacts or consequences depending on where the error occurs and the degree of causal linkage or influence in the chain.
**Subcriteria 2:Potential errors can be sequenced and prioritized to achieve an optimized reduction of error or bias and, by proxy, uncertainty over time.
* Uncertainty and its proxies, error bias, and environments are isomorphic to coherent approaches such as probability.
* Uncertainty and its proxies, error, and bias are isomorphic to coherent approaches such as knowledge models capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria in 'reasoning under uncertainty.'
This is a definition of general risk. There is no differentiation for engineering or economics, medicine or insurance. There is no difference between a 'good' risk consequence and a 'negative' one. Redefining general risk into simpler terms gives the following:
:'''General Risk is a subset of general uncertainty that is associated with the potential for material errors and biases when sequentially acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena; isomorphic to probability measures and methods and capable of testability.'''
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==Logical Framework for Specific Risk Taxonomies==
As noted above, at the most fundamental level, uncertainty is ".....the subjective perception of ignorance." [13] [Note 6] At this point in the discussion, the focus has moved from discussing uncertainty to its framed subset, risk.
:'''Further, the discussion moves from general risk to a professional or discipline-specific risk [https://en.wikipedia.org/wiki/Taxonomy taxonomy]. Beyond this point, risk will be used in place of uncertainty.'''
Han et al. note that, in general, "(t) taxonomies are valuable not only in their comprehensiveness but in their coherent reduction of uncertainty to conceptually discrete elements."
Uncertainty (and, by implication, risk or hazard) taxonomies offer an approach or tool to more precisely identify and define ontological uncertainty so that it can be quantified, analyzed, and communicated. Viewed this way, uncertainty is not a single, monolithic phenomenon but "...multi-dimensional with theoretically distinct domains and constructs that are potentially measurable and related to different outcomes, mechanisms of action, and management strategies." [14] Taxonomic schemes for classifying the different kinds of scientific and, by extension, engineering uncertainty require identifying the various kinds of potential error associated with descriptive scientific or engineering information and knowledge. An example of this is the following quote from a legal authority:
:"I would especially stress the need for an agency to disclose the uncertainty that surrounds its determinations. '''And by uncertainty, I mean the agency's ignorance as well as its quantitative estimates of error."''' (Emphasis added) [15]
'''Given the necessity of acting in the face of enormous uncertainties [16], discipline taxonomies must offer a clearly coherent and probabilistic approach to uncertainty that is sufficiently general in nature, logically distinct and exhaustive in scope and "...provide decision-makers with a foundation for understanding the nature of discipline information..."''' [17]
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=== Scientific Risk Taxonomies ===
The [https://en.wikipedia.org/wiki/Scientific_method scientific method] is a body of techniques for investigating phenomena in its environment, acquiring new knowledge, or correcting and integrating previous knowledge. Inherent in that process are uncertainties of many forms. Any one taxonomy of [https://en.wikipedia.org/wiki/Uncertainty scientific uncertainty] has largely been focused on statistical models used to assess and quantify sampling, errors, and parameters, although several different taxonomies are conceivable: note-24 [18]
====Environment-specific, Phenomena oriented, model-centric taxonomy of uncertainties====
** [https://en.wikipedia.org/wiki/Uncertainty_quantification#Sources_of_uncertainty '''Parameter uncertainty'''] the source of which is model parameters that are inputs to the computer model (mathematical model) but whose exact values are unknown and cannot be controlled, or whose values cannot be exactly inferred by statistical methods. Subsets of this type of uncertainty include but are not limited to:
***'''Experimental uncertainty''' is also known as observation error, which comes from the variability of experimental measurements. An example is repeating an experiment measurement several times using exactly the same settings for all inputs/variables and recording the variability. It is a subset of the larger uncertainty component, parameter uncertainty.
*** '''Parametric variability''' comes from the variability of the input variables of the phenomena model and is also a subset of the larger uncertainty component, parameter uncertainty.
** '''Structural uncertainty''', or model inadequacy, model [https://en.wikipedia.org/wiki/bias bias] or "[https://en.wikipedia.org/wiki/Systemic_bias systemic bias]", or model discrepancy, which comes from the lack of knowledge of the underlying true state of the phenomenon or its environment. It depends on how accurately a mathematical model describes the true state of the phenomenon or what is termed "model fitness". Due to the inherent nature of uncertainty in any knowledge body (scientific or engineering), models are only an approximation to reality. Therefore, any conclusions drawn from the model can be very misleading when the underlying basis is not plausible or lacks validity. '''note-25 [19]]'''
*** What is missing in this context is any concept of output variability due to the model itself. Part of this is due to the lack of recognition of "process" in scientific investigation. The concept of the scientific method started with a single investigator, such as Galileo or Michael Faraday, working in their laboratories. It has grown into planet and solar system scale experiments such as data gathered on planetary flybys such as Mars, Pluto, and Ceres to the hunt for the Higgs Boson. Ultimately, science has become more like engineering in the scale and complexity of its investigations and theory aggregates, such as the "Unified theory" of particle physics. Subsets of this type of uncertainty include but are not limited to:
*** '''Algorithmic uncertainty''', or numerical uncertainty, comes from numerical errors and numerical approximations per implementation of the computer model. Most models are too complicated to solve exactly. For example, the [https://en.wikipedia.org/wiki/Finite_element_method finite element method] or [https://en.wikipedia.org/wiki/Finite_difference_method finite difference method] [may be used to approximate a solution [https://en.wikipedia.org/wiki/Partial_differential_equation partial differential equation], which introduces numerical errors. Other examples are numerical integration and infinite sum truncation, which are necessary approximations in numerical implementation.
*** '''Interpolation uncertainty''' comes from a lack of available data collected from computer model simulations or experimental measurements. For other input settings that don't have simulation data or experimental measurements, one must interpolate or extrapolate to predict the corresponding responses.
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====Commentary====
A more comprehensive taxonomy of uncertainties that acknowledges knowledge, data, and linguistic uncertainties is:
*'''Epistemic uncertainty''' -[https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Epistemic uncertainty] or systematic uncertainty, in contrast, reflects limitations in the current “state of knowledge” underlying models themselves, originates from competing theories or models, is not readily quantifiable, and is manifest by subjective confusion or indecision. It includes uncertainty due to measurement limitations, insufficient data, extrapolations and interpolations, and variability over time and space. Epistemic uncertainty is uncertainty about "...some determinate fact ... because of a lack of complete information. '''cite_note-26 [20]]''' Epistemic uncertainty can be classified into six main types:'''cite_note-27 [21]]'''
*''' Measurement error''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Observational_error Measurement error] is uncertainty that manifests itself as (apparently) random variation in the measurement of a quantity. Repeated measurements will vary and demonstrate classic statistical behavior.
* '''Systematic error''' - [https://en.wikipedia.org/wiki/Observational_error Systematic error] occurs due to bias in the measuring equipment, model, analysis, observation, or sampling procedure. It is formally defined as the difference between the true value of the quantity of interest and the value to which the mean of the results converges as sample or data sizes increase. Unlike measurement error, it is not (apparently) random, and therefore, results subject to systematic error alone do not vary about a true value. Systematic error can result from deliberate judgment to exclude (or include) data, parameters, or models that ought not to be excluded (or included).
** "The only way to deal with systematic error is to recognize a bias in the process, model, or procedure and remove it. Systematic error, however, is notoriously difficult to recognize except on theoretical grounds. Corrections may only be applied when the magnitude and direction of the bias are known. Such corrections underlie the application of double-sampling methods in environmental science." (Regan, et. al., op. cit.)
*'''Natural variation''' - [https://en.wikipedia.org/wiki/Natural_process_variation Natural variation] or underlying process variation occurs in systems that "...change (concerning time, space, or other variables) in ways that are difficult to predict...across the full range of temporal and spatial values (or other related variables)." '''note-28 [22]'''
** This form of uncertainty is reduceable utilizing tools such as [https://en.wikipedia.org/wiki/Statistical_process_control statistical process control] and replication of experiments or observations.
*'''Inherent randomness''' - [https://en.wikipedia.org/wiki/Randomness randomness] or [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty aleatory uncertainty] exists because the system is"...in principle, irreducible to a deterministic one (the most well-known case is described by [https://en.wikipedia.org/wiki/Uncertainty_principle Heisenberg’s uncertainty principle] in quantum mechanics)." '''note-29 [23], note-30 [Note 7]'''
** Even if the argument is accepted that it is unlikely that any given physical system at some level is inherently random, it is important for any taxonomy to distinguish between systems that appear random due to incomplete or inconsistent information and those that are intrinsically random. (Again, see Regan (2002)).
*'''Model uncertainty''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling Model uncertainty] occurs in the process of generating a model as a conceptual representation of a particular phenomenon to create a [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling#Overview simplified reflection of reality]. This selectivity of factors and variables creates uncertainty in at least three ways. (Regan (2002))
** '''Variables and processes''' that are regarded as relevant often represent a trade-off between system knowledge and assumed states and model objectives;
** '''Models''' depict observed processes using logical or mathematical constructs based on underlying theories about system states or dynamics using continuous equations to describe discrete processes.
** '''Curve fitting''' (including interpolation and extrapolation) with mathematical expressions using model variables where inputs are discrete data points.
** Regan (2002) argues that '''model uncertainty''' is "...notoriously difficult to quantify and impossible to eliminate ...(and)...(t)he only reliable way of determining how appropriate a model is for prediction is to perform validation studies."
** Subjective judgment occurs due to the interpretation of scarce or error-prone data.
*** The only way to address this type of uncertainty is to "...assign a degree of belief about an event in the form of a subjective probability." (Regan (2002))
*Linguistic uncertainty - Linguistic uncertainty, or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. '''note-31 [24]], note-32 [25]]'''
** '''Context dependence''' is uncertainty concerning the context in which a statement is to be understood;
** '''Ambiguity''' occurs when words have multiple meanings, and further reduction to a single meaning is not logically possible in a given context.
** '''Underspecificity''' occurs in the presence of "unwanted generality" or multiple interpretations, and reduction to a smaller set or even a single interpretation is not logically possible.
*Stochastic or statistical uncertainty - Stochastic or statistical uncertainty, which is sometimes known as [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Aleatoric uncertainty] as well as the subject of [https://en.wikipedia.org/wiki/Stochastic_control stochastic control] pertains to the parameters of a risk model, originates from sampling or measurement error, and can be quantified and mathematically expressed (e.g., using confidence intervals).
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== Uncertainty Taxonomies in a Legal Environment ==
From a legal perspective, a definition of taxonomy is dictionary-based, such as "the systematic distinguishing, ordering, and naming of type groups within a subject field." given by Walker (op. cit.). Such legal concepts of scientific or engineering taxonomies are not required to be epistemically complete. Still, they must cover "the most significant aspects of ...(current, good discipline practices) ... and of the descriptive information commonly encountered by decision-makers" in a logically distinct manner. (Walker, 1991, p. 571) These taxonomies must be sufficient to catalog the types of uncertainty associated with descriptive assertion, including the critical subset of cause and effect assertions. This legal/decision-maker framework has presented descriptive uncertainty as having six components (Walker, 1991). Although not part of the Walker taxonomy, underlying the scheme is linguistic uncertainty, or "vagueness," as a source of uncertainty, including uncertainties due to context dependence, ambiguity, and under-specificity. '''note-33 [26]]''' Arguably, these uncertainties underlie the most basic element in the taxonomy.
* '''Linguistic '''- or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. (Regan, 2002)
* '''Conceptual'''- the definition by choice and design of descriptive concepts or variables to be used as [https://en.wikipedia.org/wiki/Predicate_(grammar) predicates] where the subject of an assertion identifies what is being discussed and the predicate provides information about the topic or characterizations such as what the subject is, what the subject is doing, or what the subject is like (Wikipedia).
* "Conceptual uncertainty, or the potential for conceptual error, arises whenever predication occurs. Whenever a concept is used to describe something, using certain concepts instead of others begins to structure how we understand the object, event, or instance under discussion. Predication or conceptualization generates useful information about things, but it also can inhibit our ability to think about those things with concepts other than those selected. The concepts used may not be the most fruitful or the best designed- either for scientific purposes or for making wise, fair, effective, and efficient decisions." (Edited for reading, Walker, 1991)
* Similarly, choice and design of descriptive variables enhance and restrict data set membership, creating potentially incomplete, incompatible, or inconsistent data sets, "...the classification categories employed, and the relationships among those categories (nominal, ordinal or scalar)." '''note-34 [27]]'''
* Concept uncertainty '''note-35 [28]''' like systematic error discussed above is notoriously difficult to recognize, except on theoretical grounds, and the magnitude and direction of the bias are known.
Both terms could be analogized to that of [https://en.wikipedia.org/wiki/Accuracy_and_precision#Terminology_of_ISO_5725 accuracy] in [https://www.bipm.org/en/publications/guides/ ISO 5725] which looks at the ability of the concept or system's ability to produce results that are proximate to the "true" value or state of the phenomenon. Conceptual uncertainty and systemic bias are related to another measure of uncertainty: validity. Walker has defined validity as the ability of a concept to quantitatively describe and explain what the phenomenon objective "truly" is. Walker also ties the concepts together when he states, "Validity concerns the "accuracy" of the measurement data, not its precision." (Walker, 1998) 
Additionally, conceptual uncertainty and associated validity issues could be propagated throughout this taxonomy. Every component of action and decision in the face of uncertainty raises validity issues. Walker recognized this when the author defined epistemic choice for concepts used throughout the other uncertainty components or layers.
* '''Measurement''' uncertainty or Misclassification error- the application of the underlying concepts or variables to specific, individual cases and the uncertainty of the reliability (Walker, 1998) of the resultant data. (It is important to remember that assessment observation and measurement are used interchangeably in this analysis.) Thus, an assessment method or procedure is considered "reliable" or "precise" in the scientific terms of ISO 5715 if it repeatedly produces consistent results.
* Measurement uncertainty is logically distinct from conceptual uncertainty, which can only occur after any conceptual uncertainty has been established. Still, misclassification can occur even when the conceptual uncertainty has been minimized. Conversely, underlying errors or errors made in measurement or classification propagate thru the reasoning chain and reduce the quality or value of the final result.
* While uncertainties associated with quantitative measurements or assessments can be statistically evaluated and described, uncertainties related to qualitative assessments are more problematic.
**Working through the taxonomic chain from base concept to causal explanation requires amassing and integrating qualitative information and quantitative data into input parameters for mathematical models. Developing procedures for producing consistent, repeatable sets of information from assessments that minimize conceptual error or systemic bias requires professional efforts to "validate" such methods, models, and judgments at all layers in the process. Validation as a process is something that civil engineering as a practice needs to embrace more frequently.
* '''[https://en.wikipedia.org/wiki/Sampling Sampling]'''- in the classical statistics sense, is the selection of a limited subset of a larger population to use as a basis for making estimates about the population in the form of attributes, proprieties, or parameters. The resulting information would be determinative in making forecasts about future population sampling or possessing a desired amount of predictive power. The uncertainty about the ability of this information to correctly predict future samples is termed sampling error.
* '''Modeling'''- Modeling uncertainty arises whenever a claim is made that out of a class of candidate relationships and variable pairings, one variable pairing inclusive of constants is chosen that possesses a particular or persistent mathematical relationship to another variable X. Modeling errors arise in selecting the wrong variable pair and constants or by incorrectly specifying its constants. (Walker, 1991, pg. 599)
** '''Causal'''- In causal modeling and analysis, the relationship between causal variables is not strictly a mathematical function. Still, it makes testable predictions and explains "...how a system of variables works, why a system works the way it does, or why it makes sense to think of certain variables as a system" at all." (Walker, 1991, pg. 609)
** '''Epistemic'''- the choice of interpretations for fundamental, logical concepts used throughout the other components or layers.
The components of this uncertainty taxonomy can combine in different ways to "...produce different aggregate uncertainties." '''note-36 [29]'''
:''' Only the scientific and engineering professions possess the capacity to develop and validate procedures and models that combine the different kinds of qualitative '''note-37 [30]''' and quantitative information and their associated potential for error into an aggregate measure or "scalar variables" of uncertainty.'''note-38 [31]]
Scalar variables are quantitative variables whose categories are related by some measure of the relevant property's incremental frequency, degree, or amount. (Walker, 1991) Examples are Modulus of Elasticity, Compression stress in elastic materials, etc.

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=== Economics Uncertainty Taxonomies ===
Like scientific concepts of uncertainty, economics is environment-specific (in this case, read sector of economy or markets), phenomenon-oriented (in this case, read human activity and manufactured phenomena such as firms and markets guided by human judgment and decision), and model-centric.
:''' Economics is not interested in all forms of uncertainty, only specific, meaningful subsets possessing casual connections with material impacts, often labeled as 'risks'.'''
One of its earlier writers ([https://en.wikipedia.org/wiki/Frank_Knight#Life_and_career Knight], 1921, 1948, 1957) attempted to define "uncertainty" as the presence of "defects of managerial knowledge" (or [https://en.wikipedia.org/wiki/Knightian_uncertainty knightian uncertainty] and thus risk centric) as risk that is immeasurable, not possible to calculate.(Wikipedia) Risk in this sense was defined as "the ordinary risks of business activity which can ... be reduced... by applying the insurance principle." Knight does acknowledge that risk is a specific and unambiguous subset of uncertainty. (Risk, Uncertainty and Profit, 1957, pg. 19) For Knight, measuring uncertainty meant measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences, which Knight referred to as "the imperfection of knowledge". (Risk, Uncertainty and Profit, 1957, pg. 197) One of Knight's many contributions to this analysis was recognizing information and knowledge's role in economic activity.
:"''(W)e are concerned only to emphasize the fact that '''knowledge is in a sense variable in degree and that the practical problem may relate to the degree of knowledge rather than to its presence or absence in toto.''' We live only by knowing something about the future, while the problems of life, or conduct at least, arise from knowing so little. This is as true of business as of other spheres of activity. The situation's essence is action according to opinion, of greater or less foundation and value, neither entire ignorance nor complete and perfect information, but partial knowledge. If we are to understand the workings of the economic system, we must examine the meaning and significance of uncertainty; and to this end, some inquiry into the nature and function of knowledge itself is necessary.''" (Knight, pg. 199) Emphasis added
Knight also reinforced the difficulties in classifying uncertainties when he analyzed life insurance. In this case, accidental death was an uncertain phenomenon compared to sickness and accident, where an "...objective description and classification of cases was impossible..." (Knight, pg. 248) Another factor that made uncertainties ineligible as risks and, therefore, uninsurable because they were unclassifiable was the exercise of judgment in making decisions by the businessman. (Knight, pg. 251)
This is a simple classification of economic uncertainty and, as an idealization, is philosophically controversial. '''note-39 [32]'''
* Uncertainty and information about the economic environment are distinct from uncertainty about others’ behavior or choices.
* Risk as a specific subset of uncertainty implies that "...all possible acts are known, all possible outcomes arising from each act are known, and it is possible to assign probabilities to each act." '''note-40 [33]'''
Like the economic [https://en.wikipedia.org/wiki/Theory_of_the_firm theory of the firm], an economic theory of uncertainty seeks to explain and predict the nature of a specific subset of uncertainty (in this case, risk relating to physical phenomena and human activity), including its behavior, structure, and relationship to the universal uncertainty of physical phenomena. In this sense, uncertainty, like firms and markets, can establish price equilibriums under the right circumstances different from those that might have occurred in either of the other two. Such a theory of uncertainty offers a partitioning scheme that decomposes uncertainty into distinct and meaningful subsets, of which risk is a significant one. It also explains how the presence and variability of knowledge cause or " drive" economic consequences. Unlike physical phenomena, economic activity creates artificial objects such as products, firms, mediums of exchange, markets, economies, and knowledge about those activities. The economics of uncertainty exists as an alternative to "certainty" models that assume away imperfect knowledge and unknown choice/judgment preferences when it is more efficient for economic decision-making. This also allows the introduction of the logical concept of "hazard" versus "risk". Frank used hazard extensively in Risk, Uncertainty, and Profit, but not to the extent that one could say it was interchangeable. An example of this is in the term "[https://en.wikipedia.org/wiki/Moral_hazard Moral hazard]," where a firm could engage in "riskier" and potentially "uninsurable" behavior if the information were transparent, and, therefore, insurance coverage of losses is limited. note-41 [34]], note-42 [35]]

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=== Uncertainty and Risk in Policy and Regulatory Environment ===
:''"Hume's 'just reasoner', when faced with a difficult public policy decision in current times, would probably commission a risk analysis. But how could they incorporate 'a degree of doubt, caution, and modesty?"'' '''note-43 [36]'''
The problem of "decision-making in the face of uncertainty" is how to regulate based on incomplete information that has the potential to be materially inaccurate. A contextual assumption is that we need to evaluate decision rules for dealing with uncertainty. As a social enterprise, risk regulation, whatever its substantive objectives, should be as '''effective, efficient, and equitable''' as practically possible. These "three E's" form a set of "process objectives" or "meta-goals." From the uncertainty standpoint, causal information can be usefully divided into two major categories: information about groups and information about individuals.

Each category, group, and individual has its distinctive types of inherent uncertainty. These are logically distinct, generally independent, and cumulative, contributing to '''(?????)'''

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=== Engineering Uncertainty Taxonomies ===
Like scientific and economic concepts of uncertainty, engineering is environment-specific, phenomena-oriented, model-centric, external choice, and driven by decision-making in the face of uncertainty. Like economics and unlike science, engineering is not interested in all forms of uncertainty, only those with determinable and material consequences. Like economics, engineering conceives risk as a specific and unambiguous subset of uncertainty determined by measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences. Also, engineering recognizes the role information and, by extension, knowledge plays in constructing the built environment.
'''note-44 [37]]''' Like economics, engineering differs from economics and scientific uncertainty in its interest in behavioral uncertainty.
*"Behavioral or interaction uncertainty is how individuals or organizations act or interact. Behavioral uncertainty arises from four sources: design uncertainty, requirement uncertainty, volitional uncertainty, and human errors." '''note-45 [38]]'''
** A design uncertainty is a choice among alternatives over which an individual or group of individuals exercises direct control but has not yet decided upon.
** Requirement uncertainty includes parameters of interest to and determined by the stakeholder, independent of the engineer or designer.
** Volitional uncertainty is uncertainty about what the subject him/herself will decide. Other people’s future actions and conduct are not entirely predictable, particularly when dealing with other organizations.
** Human errors occur during the development of a system or project due to blunders or mistakes by an individual or individuals.

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==Practice Frameworks for Uncertainty and Risk in Civil Engineering Practice==
===PMIBoK===
PMI does not define uncertainty, but the PMI Body of Knowledge uses the term in 45 places (uncertainty) and as describing properties in 8 places (uncertainties). [See Note 8] PMI does define "risk" and uses the term over 1,300 times in the same document. In its glossary, PMI also defines terms such as "threat" and "opportunity" but not events.[391 The BoK contextually defines uncertainty when it states that project risk has its origins in the uncertainty present in all projects. (Op. Cit., pg 309) 
'''Uncertainty''' is contextually defined and taken as having the property of affecting project execution and the ability to meet stakeholder expectations. Uncertainty is more than the sum of all individual risks (known and unknown). The nature of this uncertainty is not defined, only its capacity to affect something else or its properties. 
'''Risk''' is defined as " ... an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives." (Ibid.) Beyond this formal definition, PMI adds context when, in its risk management section (Sec. 11 ), the BoK states that risk is a caused event or condition with multiple causes and impacts. The BoK even offers specific techniques for mapping cause-and-effect relationships and influence diagrams. (Sec. 11.2.2.5) Not as evident but equally important is the recognition that not only is risk caused but the impact is "triggered". The risk trigger is an event or situation that signals that it is about to occur (Glossary, pg. 566). Risk conditions are factors that affect or otherwise contribute to risk, such as project stakeholders. Risk implicitly retains some residual element of probability in it, or a value of less than 1.0 with risk that approaches a level of near certainty is termed "issues" or "realized risk" .(Op. Cit., pg 309). PMI also links risk to underlying variations in project outcomes. (Ibid.) Risk is based upon data and information which can be assessed as to its quality (Sec. 11.3.2.3). This analysis looks to determine the value of the information and examines the degree to which the risk information is understood as well as its " ... accuracy, quality, reliability and integrity ... " (Ibid.) Part of that is understanding the relationship between occurrence and impact as outlined in PMl's Figure 11-10 Risk Impact Matrix and developing a model for prioritizing risk products and setting the threshold for action.
====Commentary:====
For defining and identifying risk, PMI notes in Sec. 11.2.2.4 that " ... Every project and its plan is conceived and developed based on a set of hypotheses, scenarios, or assumptions." Part of the risk analysis process is identifying risks to the project, such as inaccuracy, instability, inconsistency, or incompleteness of assumptions. Similarly, the quality of management plans, as well as their consistency with others and the project objectives and assumptions are 11 •• .indicators of risk in the project." (Sec. 11.2.2.1) 
Similarly, the role of engineering economics in risk is embedded or implied in the PMI definition of risk when the BoK discusses risk identification as a process of assessing which risks out of the total risk exposure for the project "may affect" the project and distilling their characteristics. Embedded in this statement is the concept that not all risks can cause economic impacts (positive and negative) to the project. Also embedded is the requirement to understand risk characteristics to identify project risk (Cost and schedule). Lastly, risk identification is an iterative process of incrementing project risk documentation, much like project scope, cost, and schedule documentation. 
Lastly, PMI acknowledges the complex constitution of risk aggregates or the "sum" of all risks in its material, but is it a sum? Risk information has descriptive and explanatory content. These are knowledge and meta-knowledge components. Understanding project assumptions is project-specific knowledge, but identifying risks from assumption inaccuracy, instability, inconsistency, or incompleteness requires professional or program meta-knowledge. 
'''Uncertainty exists in all projects, but risk, a subset of uncertainty with material economic impacts, is a key meta-knowledge element for civil engineering practice.'''
===Software Development Process- Risk Focused===
The Unified Process requires the project team to focus on addressing the most critical risks early in the project life
cycle. The deliverables of each iteration, especially in the Elaboration phase, must be selected to ensure that the greatest risks are addressed first. <ref> Booch, Grady, Ivar Jacobson, and James Rumbaugh. "The unified software development process."
Reading: Addison Wesley (1999).</ref>
===American Society of Civil Engineers (ASCE)===
ASCE does not define uncertainty, but the ASCE CE Body of Knowledge (CEBoK) uses the term in 22 places (uncertainty) and describes properties in 13 places (uncertainties). 
The CEBoK does not use the phrase "uncertainty and risk" as PMI and AACE do in their publication. ASCE reverses the two (Risk and uncertainty) in twelve places regarding technical outcomes (Outcome 12). A fundamental difference between PMI and ISO is that risk is considered a subset of uncertainty. ASCE does not define "risk" but uses the term 25 times in the same document. Almost all are in the context of variation of design parameters and not in the classical context of cost, schedule uncertainty, and risk.
====Commentary:====
...
=== American Association of Cost Engineers (AACE) ===
AACE defined uncertainty, risk, and other related terms in its Risk Management Dictionary. <ref>Risk Management Committee uRisk Management Dictionary, Cost Engineering Vol. 37/No. 10, OCTOBER 1995. </ref>
AACE's overall strategy was to define risk and other terms that stem from base uncertainty. AACE first defined uncertainty as .... "(t)he total range of events that may happen and produce risks (including both threats and opportunities) affecting a project (see opportunities, events, conditions, risk, and threats" where the following sub-definitions apply:
** Biases--A lack of objectivity based on the individual's position or perspective. Systematic and predictable relationships between a person's opinion or statement and his/her underlying knowledge or circumstances. Note: there may be "system biases" as well as "individual biases."
** Condition (Uncertain Condition)-Any specific identifiable circumstance (such as the rate of inflation or the quality of labor available) that might affect the outcome of the project
** Event (Uncertain Condition)-is a specific identifiable action (such as a large government project being started in the same labor area as your project) or an act of nature that might happen and that (if it does happen) could affect the outcome of the project.
** Opportunities are Uncertain events that could improve the results or improve the probability that the desired outcome will happen
** Threats are Uncertain events that are potentially negative or reduce the probability that the desired outcome will happen.
*** AACE defines Risk as an "ambiguous term" (sic) that is synonymous with uncertainty or a negative subset such as "threats", or an overall negative impact of all possible uncertainties.
====Commentary:====
...
==Limitations of the definition==
...
==Working definition==
...
==Beneficial outcomes==
...
==See also==
Wiki articles on the project.

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==Notes==
<references group="note" />

# See McDaniels et al. 2004 for an extensive discussion and bibliography on the historical development of Risk Analysis and some key milestones in the 20th century.
# Open item
# The Merriam-Webster dictionary defines uncertainty:
::"the quality or state of being uncertain," which is a circular definition. Likewise, one could define uncertainty as the state of not being certain. Synonyms are distrust, doubt, misgiving, mistrust, reservation, skepticism, and suspicion. Another writer added indefinite, indeterminate, not certain to occur, problematical, unreliable, untrustworthy, unknown beyond doubt, dubious, doubtful, not clearly identified or defined, not constant, variable, and fitful to the list.
::Han, Paul KJ, William MP Klein, and Neeraj K. Arora. "Varieties of Uncertainty in Health Care: A Conceptual Taxonomy." Medical Decision Making 31.6, (2011 ): 828-838.
::: Han, ct. al. noted that any definition of uncertainty encompasses " ... numerous types, sources, and manifestations of uncertainty, and ... (any) ... useful working definition of uncertainty needs to specify the concept underlying these varied meanings of the term." (Ibid.)
<ol start="4">
<li> Implicit in this definition of uncertainty as a "state of' is ... </li>
: " ... a conceptualization of uncertainty as a subjective, cognitive experience of people--a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one's lack of knowledge, without which one could not feel uncertain; '''uncertainty is a form of "meta-cognition" ... ( or alternatively, its main component, meta-knowledge) ... -a knowing about knowing.'''" (Han, 2011, op. cit., Emphasis added)
</ol>
<ol start="5">
<li> Cox wanted his system to satisfy the following conditions:</li>
: Divisibility and comparability- The plausibility of a statement is a real number and depends on the information we have related to the statement.
: Common sense - Plausibilities should vary sensibly with the assessment of plausibilities in the model.
: Consistency - If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). -preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf
</ol>
<ol start="6">
<li> The utility of existing taxonomies of uncertainty in civil engineering and, by extension, "risk" has been unsatisfactory for this very reason. This is particularly true in cases where the source of the uncertainty is conflicting versus incomplete information. See Regan, et. al.,"A taxonomy and treatment of uncertainty for ecology and conservation biology" (2002)
for a survey of scientific taxonomies for uncertainty.</li>
<li> Regan et al. argue that " ... genuine examples of this kind of uncertainty are hard to find. Even classic cases of random experiments like coin tosses and the throwing of dice are deterministic; it is just that we do not have enough information about the dynamic processes and initial conditions to make any sensible estimates about the outcomes. Such processes are, for all intents and purposes, inherently random, but they are not genuinely inherently random. For similar reasons, complex systems such as ecosystems and weather patterns are unlikely to be inherently random. Similarly, chaotic systems are entirely deterministic. They are unpredictable because the deterministic processes generating them and the relevant initial conditions are hard
to fully specify (see Stewart 1989, Sugihara et al. 1990)." (Regan (2002) </li>
<li> The CEBoK uses the phrase "uncertainty and risk" twice in reference to scheduling (Section 6.5,2) and cost estimating (Section 7.2,2) as well as reversing the two (Risk and Uncertainty) also in two places in discussing project life cycle (both in Section 2.4.1) </li>
</ol>

==References==
<references />

Uncertainty and Risk in Civil Engineering Practice

2024-11-24T16:22:56Z

WikiSysop: /* Certainty and Uncertainty */

'''Life is short and the art long; the occasion instant, experiment perilous, decision difficult.''' (Hippocrates as quoted in Fox,)
:''See also [[Project Definition]] , [[Project Life Cycle and Phase Models]] , [[Project Delivery Methods]]''
:''See also [[Technical Outcomes for Civil Engineering:Risk and Uncertainty]]''
==Basic Considerations==
As human beings,'' ... being alive means seeking opportunities and taking risks.'' As knowledge professionals
living in the 21st century, this means coping with an increasingly complex number of uncertainties for humans
living in this environment. We seek to understand better how these uncertainties can be characterized and
managed. The essence of this article and the experience for engineers in general and civil engineers in particular is
that manageable uncertainty is, by definition, termed risk, and its kindred cousin is termed hazard. This causes us to experience the
:" ''... human dread of and fascination for risk and the increasingly important role of risk analysis within societies ...'' " <ref name="McDaniels"> McDaniels, Timothy, and Mitchell Small. Risk analysis and society: an interdisciplinary characterization of the field. Cambridge University Press, 2004, page 1 </ref> (Ibid.)
and, by extension, civil engineering. McDaniels et al. argue that risk management has been
fundamental to our social and governance development for the past 10,000 years. The scale and shape of the
uncertainties faced in this period shaped the societies that have developed today. The central thrust of this effort
over the centuries has been to reshape and re-frame our understanding and conception of uncertainty from one of
complete unknowing and simple acceptance as our fate in life to one of management (cf "''Against the Gods''"
concept in Bernstein's book <ref name = Bernstein">Bernstein, Peter L., and Jesse Boggs. Against the gods. Simon & Schuster 1997.</ref>.
: ''All the knowledge professions and disciplines have struggled with managing uncertainty, for it is impossible to manage unbounded uncertainty.'' <ref group="note">See McDaniels et al. 2004 for an extensive discussion and bibliography on the historical development of Risk Analysis and some key milestones in risk analysis in the 20th century.</ref>
As outlined below, professions such as civil engineering have successfully acted in the face of uncertainty. It lies in the profession's ability to reduce a wide variety of uncertainties into increasingly smaller and crucially bounded subsets that can be managed. These are called 'risks. More recently, this process has evolved, and individual disciplines such as Civil Engineering (CE) have developed their knowledge and models to perform risk analysis. Risk is also taught as a distinct discipline and specialty practice within civil engineering. Still, it has some unique features that set it apart from other more classical practices within CE. As such, it is one of the first of some very specialized CE practices that use knowledge about civil engineering knowledge, or meta-knowledge. Other examples of civil engineering metaknowledge are project controls and quality controls.
==Semantic, Epistemic and Logical frameworks==
The semantic, epistemic, and logical frameworks for uncertainty and risk have several dimensions and layers of logical frameworks. The semantics problems include interchangeable usages for risk and uncertainty and hazard, uncertain and imperfect, and a lack of definitional material or context. The semantical scheme for this article will be to proceed from the distinction between certainty and uncertainty through marginal refinements and reductions of uncertainty up to the point of causal uncertainties. Beyond this point of semantics will be the logic frameworks or the subject of taxonomies of professional knowledge. First, it is about simple, testable phenomena, and then, it moves on to complex taxonomy schemes for artificially constructed phenomena or engineering projects.
<ref group="note">TBD</ref>
==Semantics framework==
===Certainty and Uncertainty===
''''' There is no such thing as absolute certainty, but there is assurance sufficient for the purposes of human life.''''' ([https://en.wikipedia.org/wiki/John_Stuart_Mill John Stuart Mill]) 
'''''If you tried to doubt everything, you would not get as far as doubting anything. The game of doubting itself presupposes certainty.''''' ([https://en.wikipedia.org/wiki/Ludwig_Wittgenstein Wittgenstein] # 115 from On [https://en.wikipedia.org/wiki/On_Certainty Certainty]) 
It is important to note that the references to epistemic or epidemiological knowledge in this article are assumed to relate to three forms of knowledge, namely:
* Knowledge that ( [https://en.wikipedia.org/wiki/Descriptive_knowledge descriptive or declarative or propositional knowledge])
* Knowledge how ( or "[https://en.wikipedia.org/wiki/Procedural_knowledge know-how]"), and
* [https://en.wikipedia.org/wiki/Knowledge_by_acquaintance Knowledge by acquaintance].
'''Certainty''' has been defined as "an epistemic property" of knowledge in all its forms and the state of our beliefs about that knowledge.
<ref>Certainty, Stanford Encyclopedia of Philosophy, accessed on September 12, 2015, at http://plato.stanford.edu/entries/certainty/#ConCer </ref>
Certainty about any belief about knowledge implies that it is not subject to doubt or skepticism. This immunity to criticism can be dogmatically based, emphasizing the importance of a propositional-based sense of truth over experiential, sensory perceptions. <ref>Definition of [https://en.wikipedia.org/wiki/Dogmatic_theology Dogmatic theology or belief], accessed at Wikipedia</ref>
: '''The demarcation line between dogmatic and non-dogmatic beliefs lies in the presence and recognition of specific criteria and information that would make the believer change their beliefs.'''
An empirical framework of [https://en.wikipedia.org/wiki/Testability testability] and [https://en.wikipedia.org/wiki/Falsifiability falsification] is required to recognize this and limit dogmatism. For example, one could argue that people would change their minds if God asked them to. Similarly, one could construct a concept of epistemic as opposed to dogmatic belief certainty as the ability to know anything that one chooses to know and can be known or [https://en.wikipedia.org/wiki/Omniscience inherent omniscience].

A second kind of certainty is epistemic, when conviction reflects the highest possible support for a belief. In this sense, knowledge is separate from beliefs, although someone may have beliefs about a property, such as certainty of knowledge. Logically, it has been shown that there will be unprovable statements within the system for any such knowledge system. Secondly, the knowledge system cannot demonstrate its own consistency. ([https://en.wikipedia.org/wiki/Gödel%27s_incompleteness_theorems Gödel's incompleteness theorems])
: '''Certainty, in real life, is useless or often damaging (the idea is that "total security from error" is impossible in practice, and a complete "lack of doubt" is undesirable)''' ([[https://en.wikipedia.org/wiki/Certainty Physicist Carlo Rovelli]])
'''Uncertainty''', on the other hand, arises immediately in the slightest amount of doubt or criticism.
<references group="note" /> The Merriam-Webster dictionary defines uncertainty as “the quality or state of being uncertain,” which is something of a circular definition. Likewise, one could define uncertainty as the state of not being certain. Synonyms are distrust, doubt, misgiving, mistrust, reservation, skepticism, and suspicion. Another writer added indefinite, indeterminate, not certain to occur, problematical, not reliable, untrustworthy, not known beyond doubt, dubious, doubtful, not clearly identified or defined, not constant, variable, and fitful to the list. (Han, Paul KJ, William MP Klein, and Neeraj K. Arora. "Varieties of Uncertainty in Health Care A Conceptual Taxonomy." Medical Decision Making 31.6 (2011): 828-838.)Han, et. al. noted that any definition of uncertainty clearly encompasses "...numerous types, sources, and manifestations of uncertainty, and ...(any) ...useful working definition of uncertainty needs to specify the concept underlying these varied meanings of the term."</ref>
Implicit in this definition of uncertainty as a “state of” is ...
"...a conceptualization of uncertainty as a subjective, cognitive experience of people—a state of mind rather than a feature of the objective world. The defining feature of this state, furthermore, appears to be lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one’s lack of knowledge, without which one could not feel uncertain; uncertainty is a form of “meta-cognition” ...(or alternatively, its main component, meta-knowledge)... —a knowing about knowing." (Han, 2011, op. cit.,Emphasis added)[Note 3]
Similarly, a general definition of uncertainty could be given as "...any departure from the unachievable goal of complete [https://en.wikipedia.org/wiki/Determinism determinism]." <ref name="Reference5">[5]</ref> Reasoning under uncertainty is very different than performing the same under certainty. In reasoning under certainty, one has complete knowledge and deduces without doubt and equally important, without limitation, thereby concluding free from error. Reasoning under uncertainty, one works in a state of incomplete, inconsistent, and limited knowledge; doubts cloud any statements or assertions and thereby taint any deductions/inferences resulting in the potential for error. <ref name="Note4">[Note 4]</ref>
:'''Knowledge professions reason in a state of incomplete, inconsistent and limited knowledge with doubts that cloud any statements or assertions and taint any deductions/inferences resulting in the potential for error.'''
:''' It is also important to note the semantical and logical correlation between 'reasoning under uncertainty' or 'acting in the face of uncertainty' with the 'potential for' or 'presence of' error. The presence of uncertainty is invariably linked to the potential presence of error.''' The [https://en.wikipedia.org/wiki/Contingency_(philosophy) contingent] nature of uncertainty logically implies the contingent nature of error.
:'''Knowledge Professions use error as a proxy for uncertainty such that within discipline knowledge frameworks, uncertainty can be managed.''' The rationale for this belief is that error can be reliably described, quantified, explained, and ultimately reduced in ways that uncertainty cannot.

'''Uncertainty''' implies a range of variation that is impossible in certainty. Concepts and definitions of uncertainty are unbounded and vast but start from the point of complete and total ignorance. In describing uncertainty, statements can range from complete ignorance to relatively high degrees of belief that there is less potential for error in our reasoning or action. The latter is based upon confidence in justified knowledge, favorable past experience, and reliable parameters/models. One can't make that statement about ranges in certainty. If certainty, by definition, precludes doubt of any type or nature, then how could that be graduated to any degree? 
Uncertainty, on the other hand, can be reduced through disciplined efforts to acquire knowledge. In short, we can reduce or even manage uncertainty (to a degree); how can certainty be improved? Are there higher degrees of perfection? The answer is no. We reason from an uncertain starting point. Yet this effort presumes, as Mill and Wittgenstein postulated, that we can develop or acquire an increasing degree of belief or conviction that there is less potential for error in our reasoning or action. This position assumes that some way exists to identify what a lesser degree of uncertainty would look like.
:'''Knowledge professions acquire an increasing belief or conviction that there is less potential for error in their reasoning or action based on justified knowledge, favorable past experience, and reliable parameters/models.'''
Uncertainty is, in some form, amenable to description and explanation through observation and analysis. It is explainable and predictable to such a degree that it interests professionals such as scientists and engineers. Professional knowledge of this type has explanatory or predictive power, albeit limited in scope and application to relevant subjects such as physical objects or phenomena and functionality.
:'''The very act of reducing the scope of uncertainty to a limited set of phenomena implies choice and, ultimately, the decision to act in the face of such uncertainty, but acting or judging in this manner adds more value than doing the same under relative ignorance.'''
'''Uncertainty''', as described above, implies a hybrid nature. Examples of this are economic or decision-theoretic applications. In economics, [https://en.wikipedia.org/wiki/Perfect_information perfect information] allows one within the limiting framework of perfect competition in economic games to make decisions with 'perfect knowledge.' The crucial difference here is the distinction between '[https://en.wikipedia.org/wiki/Perfection perfection],' broadly, a state of completeness and lawlessness, and 'perfect, which can be thought of as an analytical limit that is approachable but can never be attained. Arbitrarily delimiting the realm of knowledge into defined, finite boundaries allows the simulated production of perfect choice, assumed crucially, to be free from error. The hybrid nature of this form of 'simulated' certainty produced within boundaries defined by assumptions and, therefore, clouded by doubt is itself a form of uncertainty. The importance of this is the semantical and logical association that exists between certainty and perfection versus the hybrid concept of a perfect anything, whether knowledge, infraction, competition, etc., or in the case of engineering, elastic, permeable, conductive, etc., being contained within an admittedly imperfect matrix of uncertainty.
:'''Simply put, a finite, heavily bounded piece of uncertain knowledge can be improved through the simulated use of [https://www.merriam-webster.com/dictionary/assumed assumedly] perfect information, but the perfection of knowledge cannot be attained or simulated.'''
Engineering and Economics, for example, simulate finite elements of perfect knowledge or information, heavily bounded with assumptions and a range of applications. By doing so, these professions reduce the bounds and variability of uncertainty and thereby manage it. Using these hybrid models to acquire knowledge takes place in an economy (regulated or institutional) and environment (transparent and structured), which puts pressure on the various professions to recognize and use qualitative and quantitative knowledge better to reduce uncertainties in their activities. The challenges in this reflect the nature of the profession's or discipline's knowledge (e.g., scientific, engineering, or medical), the policies and structure of the relevant professional knowledge bodies, process objectives and constraints, and "the elusive demands of politics." [6] One common theme runs through all of these efforts to address the presence of uncertainty in professional activities. 
:"The available scientific information upon which a decision must be made is almost always a mixture of engineering knowledge and uncertainty. Regardless of the information or methodology, there is the potential for error. This is true regardless of the relevant science, whether archaeology or aeronautics, economics or engineering, pharmacology or toxicology or epidemiology. Achieving the best social decisions requires not only understanding and using what we know but also appreciating and weighing the extent of our uncertainty. In making the best use of ... information in ... decision-making, it is still true that the beginning of wisdom is knowing what it is we do not know." (Vern, Op. cit., Reformatted and engineering substituted for scientific) 
'''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty to develop explanations, calculate, and make predictions. This professional knowledge converges on but never reaches certainty, producing a "legitimacy" from the fruitfulness of its use.''' <ref>Randomness Is Unpredictability, Antony Eagle, The British Journal for the Philosophy of Science, Vol. 56, No. 4 (Dec., 2005), pp. 749-790 </ref> 
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=== Bias and Uncertainty ===
Up to this point, the concept of error, as outlined in uncertainty, could easily be simulated by a truly random variable. There should be no detectable differences around the unseen or specified statistical mean. However, several instances in experience point to a tendency towards one extreme or a pattern of error, a collective that is in itself an error of errors, namely [https://en.wikipedia.org/wiki/Bias bias].

Arguably, this could be part of the choice uncertainty discussion below, but it makes more sense to be looked at on its own. Acting in the face of uncertainty is an exercise of personal knowledge or experience. Whether using professional knowledge or individual experience, the potential is there to make a series of choices that, in some instances, make errors more likely than they would be if considered in the context of a statistical analysis.

This tendency for an individual to make the same error in a predictable or repeatable manner is termed bias. Personal experience, beliefs, knowledge, data, and models all have inherent built-in factors, errors, or defects that create predictable error patterns when applied in decision-making. Walker (1998) argues implicitly that such bias can also be considered systemic error. This type of uncertainty is difficult to identify except on theoretical grounds when the magnitude and direction of the bias are known. Most of these biases, whether systemic or knowledge-based, individual or practice-based, require disciplined efforts to "validate" such methods, models, and judgments at all layers while bounding the uncertainty.
: This is the challenge to professional knowledge that must be overcome, namely, developing and validating models and methods for managing bounded uncertainty that minimize this tendency towards repeatable groups of error or bias. Therefore, from this point forward, the discussion will refer to 'uncertainty and its proxies, error, and bias.'
===Propagating Error and Uncertainty===
Partitioning uncertainty and its proxies, error, and bias into logically distinct subsets involves or is associated with multiple-step execution or choice frameworks. Combining those measurements or choices into single parameters or choices involves, in some cases, assembling the data/information in a unique or limited range of sequences. This implies that associated errors in observing, calculating, or choosing will have unequal impacts or consequences depending on where in the chain the error occurs. Also implied in this is the possibility that subsequent errors will be causally linked to some degree. (In statistical practice, this depends upon whether the errors are independent of each other or correlated, specifically, co-variant.)
:'''Professions develop knowledge of how uncertainty and its proxies, error, and bias propagate through an ordered and normative series of observations or choices. This allows the profession to prioritize error and bias reduction to achieve an optimized reduction of uncertainty.'''
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===Cognition, Metacognition and Uncertainty===
The concept of choice and the resultant error in uncertainty can be further delimited depending on the role of [https://en.wikipedia.org/wiki/Cognition cognition], [https://en.wikipedia.org/wiki/Metacognition meta-cognition], and independent external elements. The rationale for this argument is that such choice activity in the face of uncertainty is a conscious, cognitive act.

An argument could be made that such cognition is a prerequisite for exercising choice and introducing error, as presented and discussed above. The counterfactual to this argument is the example of uninformed choice. Choices introduce as much as errors or more as informed choices could be made. (???) Choice, in this sense, is indifferent to knowledge. Purposeful choice in an environment of objectives requires knowledge, method, and experience, but uninformed, speculative choice does not.
:"''' Cognition''' is the set of all mental abilities and processes related to knowledge, attention, memory and working memory, judgment and evaluation, reasoning and "computation," problem-solving and decision making, comprehension and production of language, etc. Human cognition is conscious and unconscious, concrete or abstract, as well as intuitive (like knowledge of a language) and conceptual (like a model of a language). Cognitive processes use existing knowledge and generate new knowledge." (Wikipedia)
:"'''Metacognition''' is "cognition about cognition", or "knowing about knowing" and can take many forms. It includes knowledge about when and how to use particular strategies for learning or problem-solving. There are generally two aspects of [https://en.wikipedia.org/wiki/Metacognition metacognition]: knowledge about cognition and regulation of cognition."
Some types of metacognition knowledge are:
:'''Person knowledge''' (declarative knowledge), which is understanding one's own capabilities.
:'''Task knowledge''' (procedural knowledge), which is how one perceives the difficulty of a task, which is the content, length, and type of assignment.
:'''Strategic knowledge''' (conditional knowledge) is one's capability to use strategies to learn information. (Accessed at Wikipedia)
Like metacognitive knowledge, metacognitive regulation or "cognitive control" contains three essential skills.
:'''Planning:''' refers to the appropriate selection of strategies and the correct allocation of resources that affect task performance.
:'''Monitoring:''' refers to one's awareness of comprehension and task performance.
:'''Evaluating:''' refers to appraising the final product of a task and the efficiency at which the task was performed. This can include re-evaluating strategies that were used. (Accessed at Wikipedia)
Nothing has been said up to this point that limits this structure to an individual, or a collective, or two entities making cognitive choices that are incompatible or inconsistent to varying degrees. Interpretations of this scheme include an individual professional exercising judgment in a decision that, in turn, relies upon the collective judgment and decision of the discipline as an underlying basis versus the decision of another individual to oppose/protest or otherwise contest that decision. Thus, the framework of cognitive/meta-cognitive applies to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, economics of regulatory practice, and game theory.
:By solving useful problems in the face of inherent uncertainty, discipline-specific frameworks of cognitive/meta-cognitive activity apply to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, the economics of regulatory practice, and game theory.
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===Probability and Uncertainty===
Probability is a [https://en.wikipedia.org/wiki/Coherence_theory_of_truth coherent] approach to uncertainty in the physical and mathematics or "classical" domains such as engineering. [8] and is the "...is certainly the best-known and most widely used formalism for quantifying uncertainty.[9] [https://en.wikipedia.org/wiki/Cox%27s_theorem Cox’s theorem] ("Any measure of belief is [https://en.wikipedia.org/wiki/Isomorphism isomorphic] (but not necessarily equal) to a probability measure") is a well-known argument for the validity of that argument. [Note 5]

Another writer, [https://en.wikipedia.org/wiki/Frank_Knight Knight] (1921,1956), presented the following taxonomy of probabilities: [10]
: '''Classical 'a priori' probability:''' As an idealized model, numerical probabilities are computed based on generalized principles of assigning equal likelihoods and mutually exhaustive possible outcomes (Runde, 1998) to all set members. Such probabilities are assigned to "...outcomes based on a judgment of indifference between those outcomes, that is, based on the absence of any evidence of real influences in play that may render any one outcome more or less probable than any other." (Runde, op. cit.) This builds upon Knights's concept of an 'absolutely homogeneous classification of instances that are completely identical except for [https://en.wikipedia.org/wiki/Indeterminate_(variable) indeterminate] factors. This judgment of probability or logical probability is on the same logical plane as the propositions of mathematics and ultimately [https://en.wikipedia.org/wiki/Inductivism inductions] from experience.'(Knight, 1956, pg.225)
: '''Bayesian 'a priori' probability:''' In contrast to interpreting probability as the "frequency" or "propensity" of some phenomenon, [https://en.wikipedia.org/wiki/Bayesian_probability Bayesian probability] is a quantity that we assign to represent a state of knowledge or a state of belief. In this view, probability is assigned to a hypothesis, often using the basis of past or prior experience. In contrast, under the frequentist view, a hypothesis is typically tested without being assigned a probability based on prior experience. The Bayesian interpretation of probability can be seen as an extension of propositional logic that enables reasoning with hypotheses, i.e., propositions whose truth or falsity is uncertain. Bayesian probability belongs to the category of evidential probabilities; to evaluate the probability of a hypothesis, the Bayesian probabilist specifies some prior probability, which is then updated in the light of new, relevant data (evidence). The Bayesian interpretation provides a standard set of procedures and formulae to perform this calculation. [11]
: '''Statistical or 'a posterior probability:''' Empirical evaluation of the frequency of association between predicates, not analyzable into varying combinations of equally probable or logical, 'a priori' alternatives. Knight argued that any "high degree of confidence" that the judgment that experience will remain valid for future predictions is "...still based on an 'a priori" judgment of indeterminateness." (Knight, Op. Cit.) Knight's argument to support this is what will be discussed further below that first, "...the impossibility of eliminating all factors not really indeterminate; and, second, the impossibility of enumerating the equally probable alternatives involved and determining their mode of combination so as to evaluate the probability by a priori calculation." (Knight, Op. Cit.) Knight noted that the main difference between this form and that of logical or inductive probability was the presence of empirical information, which allowed the analyst to identify propensity and direction in the observed phenomena. In this case, 'a priori' probability values may be derived from first principles and "statistical probabilities are determined a posteriori by the empirical method of counting instances." (Runde [1998] quoting Knight, op. cit.)
:'''Estimates:''' The distinction here is that there is no valid basis ('a priori' or 'a posteriori') for any classifying phenomena. For Knight, this form of probability presented the greatest logical difficulties of all ... but its distinction from the other types must be emphasized, and some of its complicated relations indicated..." (pp. 224-5, emphasis in the original)

Arguably, Knight's concept of probability can be viewed as "..a continuum of probability situations, depending on the degree of homogeneity of the 'instances' in question" [12] ; i.e. going from a logically distinct but equally likely set of elements to a unique set with one member with statistical frequency in the middle. Knight's main motivation for distinguishing between a priori probability and statistical probability underscores his opinion that "(i) the 'mathematical or a priori type of probability is practically never met with in business, while the second is extremely common'; and (ii) 'the statistical treatment never gives closely accurate quantitative results' (Runde quoting Knight pp. 215-16). Runde argues that Knight couldn't conceive of (empirically tabulated) occurrences that are used in daily commerce classes of instances that we have to make do within the course of everyday economic life could be divided into subclasses of instances that are sufficiently homogeneous to permit the determination of what he calls 'real' probability (p. 217).
:'''In modeling degrees of uncertainty, any measure of belief is isomorphic but not necessarily equal to a probability or statistics measure'''.
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===Explanation, Prediction and Uncertainty===
It should be clear that the general notion of uncertainty synonymous with complete ignorance must be bounded as part of a scheme to produce a useful concept of uncertainty in professional practice such as civil engineering.
:The first principle that must be introduced is that such uncertainty must be bounded by reliable and valuable knowledge gained from past experience combined with analytical and computational capabilities to address an immediate and real problem of interest. This could be viewed as the economic interest argument. Only uncertainties associated with physical phenomena and economic scarcity will be addressed.
:The second principle is that the knowledge model is capable of producing statements or assertions in the form of hypotheses that themselves are testable. [https://en.wikipedia.org/wiki/Testability Testability], in this sense, is defined as the property applying to an empirical hypothesis and involves two components:
::The logical property that is variously described as contingency, defeasibility, or falsifiability, which means that counterexamples to the hypothesis are logically possible. Contrast this to a [https://en.wikipedia.org/wiki/Tautology_(logic) logical tautology], which is always true that is true in every possible interpretation. The practical feasibility of observing a reproducible series of such counterexamples if they do exist. In short, a hypothesis is testable if there is some real, non-zero expectation of deciding whether it is true or false of verifiable, reproducible experience. Upon this property of its constituent hypotheses rests the ability to decide whether a theory can be supported or falsified by actual experience data. (Source Wikipedia.)
'''Knowledge models must be capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria described above in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error and bias.'''
===Variability and Aleatory Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge referred to as epistemic uncertainty. Acting in the face of epistemic uncertainty introduces choice uncertainty (discussed below) and results in the production of explanations and predictions. It is implied that there will be testable hypotheses in the form of outcomes of interest to the knowledge professions. Inherent in nature is a variation of results, which may appear to uncertain but isn't. Observations of phenomena will not be the same, and part of the experimental process is to narrow those results down to within an acceptable level of tolerance. Statistical measures seek to identify underlying and unobserved measures of central tendency. These parameters are not directly observed but are derived to a degree of confidence.
In some cases, variation around a mean or within a statistical range may be perfectly acceptable to the knowledge profession. Examples of this are in the physical sciences and medicine. In Engineering and, to a lesser extent, economics, the process or phenomenon may be required to be more controlled to a narrower range than what a natural or "unmanaged" amount of variation would allow. Several courses of action present themselves.
:The first would be to ignore the aleatory phenomenon and choice, which is rejected outright.
:The second would be to assume that, in effect, such variation is itself a form of minimally managed uncertainty and treat it the same as epistemic uncertainty. This is not a desirable outcome as reducing Aleatory error and bias requires
:A third approach would be to acknowledge that process variation is, in some forms, manageable. This means making choices and acting in a way that presumes that the natural occurrence of error can be reduced, or more importantly, bias can be reduced. There is ample precedent for the third approach in civil engineering design and project management. In this sense, the argument is made that the aleatory response to managed efforts results in an elastic variation range. Presumably, an effective management effort either "shifts" the central tendency of the process results or reduces the variation range of occurrence or some other population parameter. Choosing to act and accept that narrower range rather than the broader natural variation may result in an outcome that, while well within the bounds of the expected variation, is outside the arbitrary control limits established by the choice and action. While this phenomenon mimics the broader uncertainty and its proxy error, it is not an error. The information on the variation was known, and the ability to produce narrower results was an error, albeit a pseudo-error, when compared to the broader classes of uncertainty discussed above, such as total ignorance.
::Looking at uncertainty using this third approach means that the error in choosing a new target parameter for this process may contain epistemic errors (our knowledge of the phenomenon may be erroneous, or the model flawed), aleatory uncertainties as discussed, or, more importantly, choice error or bias in thinking that we can move the process results to meet the requirements.
:'''In civil engineering practice, this may mean applying professional judgment in developing partitioning schemes or allocating uncertainty between the three dimensions (Epistemic, Aleatory, and Behavioral) of uncertainty.'''
Lastly, raising the topic like this brings the questions of effectiveness and efficiency to the fore. Advancing knowledge models that reduce error and bias at first are largely choice models resulting in justifications and favorable experiences. At some point in the process, the knowledge profession advances explanations and predictions that require reliable mathematical or logical concepts that are reduced to models and parameters. The question of efficiency becomes important; the broader uncertainty may have been reduced, but the process is economically inefficient. The profession is urged to become more frugal or, for example, reduce the variability of the outcomes. Knowledge professions often operate under conditions that require them to acquire knowledge and reduce uncertainty effectively and efficiently. Doing so requires understanding both epistemic uncertainty, as discussed above, and statistical process variation or aleatory variation.
:'''Knowledge professions manage and reduce uncertainty in the form of error and bias in an economically effective and efficient manner. This effort requires understanding both epistemic and aleatory uncertainty.'''
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===Choice and Behavioral Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge, referred to as epistemic uncertainty, as well as the inherent variations in results, or what was termed aleatory uncertainty. Acting in the face of that epistemic and aleatory uncertainty introduces yet another uncertainty into the process, namely choice uncertainty. This uncertainty reflects uncertainties associated with first-person and third-person choice. This is particularly relevant for engineering with project stakeholders.
===Summary of Semantics Framework for Uncertainty===
Professional disciplines have gradually re-framed their understanding of uncertainty and, thru experience and analysis, have arrived at the following meta-knowledge concepts of uncertainty:
* '''Uncertainty and its proxy, error, have three components: [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic epistemic uncertainty], [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic aleatory uncertainty], and behavioral uncertainty.'''
* '''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty using non-dogmatic beliefs, which recognize the existence of specific criteria and information that would make the believer change their beliefs using coherent approaches such as probability.'''
* '''Knowledge professions develop knowledge of how uncertainty and its proxy, error, propagate through an ordered and normative series of observations or choices that meet testability criteria in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error. An error can then be described, explained, and reduced in ways that uncertainty cannot. This allows the professions to prioritize error reduction to achieve an optimized reduction of uncertainty and produce a "legitimacy" from the fruitfulness of its use.'''
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===Uncertainty versus Risk/Hazard===
Up to this point, nothing has been said about risk or hazard. Now, risk can be defined. Simply put risk or hazard are finite, logically distinct subsets of the broader uncertainty. Risk and hazard are currently defined interchangeably. First and foremost, risk is a specific subset of general uncertainty. Any arbitrary subset of general uncertainty can be defined as a risk if it meets the following criteria:
* Uncertainty is associated with the potential for material error when acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena.
* Uncertainty and its proxy, error, can be partitioned into logically distinct subsets associated with multiple-step execution or choice frameworks in physical environments linked to a unique or limited range of sequences.
**Subcriteria 1:Resulting errors will have varying impacts or consequences depending on where the error occurs and the degree of causal linkage or influence in the chain.
**Subcriteria 2:Potential errors can be sequenced and prioritized to achieve an optimized reduction of error or bias and, by proxy, uncertainty over time.
* Uncertainty and its proxies, error bias, and environments are isomorphic to coherent approaches such as probability.
* Uncertainty and its proxies, error, and bias are isomorphic to coherent approaches such as knowledge models capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria in 'reasoning under uncertainty.'
This is a definition of general risk. There is no differentiation for engineering or economics, medicine or insurance. There is no difference between a 'good' risk consequence and a 'negative' one. Redefining general risk into simpler terms gives the following:
:'''General Risk is a subset of general uncertainty that is associated with the potential for material errors and biases when sequentially acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena; isomorphic to probability measures and methods and capable of testability.'''
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==Logical Framework for Specific Risk Taxonomies==
As noted above, at the most fundamental level, uncertainty is ".....the subjective perception of ignorance." [13] [Note 6] At this point in the discussion, the focus has moved from discussing uncertainty to its framed subset, risk.
:'''Further, the discussion moves from general risk to a professional or discipline-specific risk [https://en.wikipedia.org/wiki/Taxonomy taxonomy]. Beyond this point, risk will be used in place of uncertainty.'''
Han et al. note that, in general, "(t) taxonomies are valuable not only in their comprehensiveness but in their coherent reduction of uncertainty to conceptually discrete elements."
Uncertainty (and, by implication, risk or hazard) taxonomies offer an approach or tool to more precisely identify and define ontological uncertainty so that it can be quantified, analyzed, and communicated. Viewed this way, uncertainty is not a single, monolithic phenomenon but "...multi-dimensional with theoretically distinct domains and constructs that are potentially measurable and related to different outcomes, mechanisms of action, and management strategies." [14] Taxonomic schemes for classifying the different kinds of scientific and, by extension, engineering uncertainty require identifying the various kinds of potential error associated with descriptive scientific or engineering information and knowledge. An example of this is the following quote from a legal authority:
:"I would especially stress the need for an agency to disclose the uncertainty that surrounds its determinations. '''And by uncertainty, I mean the agency's ignorance as well as its quantitative estimates of error."''' (Emphasis added) [15]
'''Given the necessity of acting in the face of enormous uncertainties [16], discipline taxonomies must offer a clearly coherent and probabilistic approach to uncertainty that is sufficiently general in nature, logically distinct and exhaustive in scope and "...provide decision-makers with a foundation for understanding the nature of discipline information..."''' [17]
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=== Scientific Risk Taxonomies ===
The [https://en.wikipedia.org/wiki/Scientific_method scientific method] is a body of techniques for investigating phenomena in its environment, acquiring new knowledge, or correcting and integrating previous knowledge. Inherent in that process are uncertainties of many forms. Any one taxonomy of [https://en.wikipedia.org/wiki/Uncertainty scientific uncertainty] has largely been focused on statistical models used to assess and quantify sampling, errors, and parameters, although several different taxonomies are conceivable: note-24 [18]
====Environment-specific, Phenomena oriented, model-centric taxonomy of uncertainties====
** [https://en.wikipedia.org/wiki/Uncertainty_quantification#Sources_of_uncertainty '''Parameter uncertainty'''] the source of which is model parameters that are inputs to the computer model (mathematical model) but whose exact values are unknown and cannot be controlled, or whose values cannot be exactly inferred by statistical methods. Subsets of this type of uncertainty include but are not limited to:
***'''Experimental uncertainty''' is also known as observation error, which comes from the variability of experimental measurements. An example is repeating an experiment measurement several times using exactly the same settings for all inputs/variables and recording the variability. It is a subset of the larger uncertainty component, parameter uncertainty.
*** '''Parametric variability''' comes from the variability of the input variables of the phenomena model and is also a subset of the larger uncertainty component, parameter uncertainty.
** '''Structural uncertainty''', or model inadequacy, model [https://en.wikipedia.org/wiki/bias bias] or "[https://en.wikipedia.org/wiki/Systemic_bias systemic bias]", or model discrepancy, which comes from the lack of knowledge of the underlying true state of the phenomenon or its environment. It depends on how accurately a mathematical model describes the true state of the phenomenon or what is termed "model fitness". Due to the inherent nature of uncertainty in any knowledge body (scientific or engineering), models are only an approximation to reality. Therefore, any conclusions drawn from the model can be very misleading when the underlying basis is not plausible or lacks validity. '''note-25 [19]]'''
*** What is missing in this context is any concept of output variability due to the model itself. Part of this is due to the lack of recognition of "process" in scientific investigation. The concept of the scientific method started with a single investigator, such as Galileo or Michael Faraday, working in their laboratories. It has grown into planet and solar system scale experiments such as data gathered on planetary flybys such as Mars, Pluto, and Ceres to the hunt for the Higgs Boson. Ultimately, science has become more like engineering in the scale and complexity of its investigations and theory aggregates, such as the "Unified theory" of particle physics. Subsets of this type of uncertainty include but are not limited to:
*** '''Algorithmic uncertainty''', or numerical uncertainty, comes from numerical errors and numerical approximations per implementation of the computer model. Most models are too complicated to solve exactly. For example, the [https://en.wikipedia.org/wiki/Finite_element_method finite element method] or [https://en.wikipedia.org/wiki/Finite_difference_method finite difference method] [may be used to approximate a solution [https://en.wikipedia.org/wiki/Partial_differential_equation partial differential equation], which introduces numerical errors. Other examples are numerical integration and infinite sum truncation, which are necessary approximations in numerical implementation.
*** '''Interpolation uncertainty''' comes from a lack of available data collected from computer model simulations or experimental measurements. For other input settings that don't have simulation data or experimental measurements, one must interpolate or extrapolate to predict the corresponding responses.
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====Commentary====
A more comprehensive taxonomy of uncertainties that acknowledges knowledge, data, and linguistic uncertainties is:
*'''Epistemic uncertainty''' -[https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Epistemic uncertainty] or systematic uncertainty, in contrast, reflects limitations in the current “state of knowledge” underlying models themselves, originates from competing theories or models, is not readily quantifiable, and is manifest by subjective confusion or indecision. It includes uncertainty due to measurement limitations, insufficient data, extrapolations and interpolations, and variability over time and space. Epistemic uncertainty is uncertainty about "...some determinate fact ... because of a lack of complete information. '''cite_note-26 [20]]''' Epistemic uncertainty can be classified into six main types:'''cite_note-27 [21]]'''
*''' Measurement error''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Observational_error Measurement error] is uncertainty that manifests itself as (apparently) random variation in the measurement of a quantity. Repeated measurements will vary and demonstrate classic statistical behavior.
* '''Systematic error''' - [https://en.wikipedia.org/wiki/Observational_error Systematic error] occurs due to bias in the measuring equipment, model, analysis, observation, or sampling procedure. It is formally defined as the difference between the true value of the quantity of interest and the value to which the mean of the results converges as sample or data sizes increase. Unlike measurement error, it is not (apparently) random, and therefore, results subject to systematic error alone do not vary about a true value. Systematic error can result from deliberate judgment to exclude (or include) data, parameters, or models that ought not to be excluded (or included).
** "The only way to deal with systematic error is to recognize a bias in the process, model, or procedure and remove it. Systematic error, however, is notoriously difficult to recognize except on theoretical grounds. Corrections may only be applied when the magnitude and direction of the bias are known. Such corrections underlie the application of double-sampling methods in environmental science." (Regan, et. al., op. cit.)
*'''Natural variation''' - [https://en.wikipedia.org/wiki/Natural_process_variation Natural variation] or underlying process variation occurs in systems that "...change (concerning time, space, or other variables) in ways that are difficult to predict...across the full range of temporal and spatial values (or other related variables)." '''note-28 [22]'''
** This form of uncertainty is reduceable utilizing tools such as [https://en.wikipedia.org/wiki/Statistical_process_control statistical process control] and replication of experiments or observations.
*'''Inherent randomness''' - [https://en.wikipedia.org/wiki/Randomness randomness] or [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty aleatory uncertainty] exists because the system is"...in principle, irreducible to a deterministic one (the most well-known case is described by [https://en.wikipedia.org/wiki/Uncertainty_principle Heisenberg’s uncertainty principle] in quantum mechanics)." '''note-29 [23], note-30 [Note 7]'''
** Even if the argument is accepted that it is unlikely that any given physical system at some level is inherently random, it is important for any taxonomy to distinguish between systems that appear random due to incomplete or inconsistent information and those that are intrinsically random. (Again, see Regan (2002)).
*'''Model uncertainty''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling Model uncertainty] occurs in the process of generating a model as a conceptual representation of a particular phenomenon to create a [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling#Overview simplified reflection of reality]. This selectivity of factors and variables creates uncertainty in at least three ways. (Regan (2002))
** '''Variables and processes''' that are regarded as relevant often represent a trade-off between system knowledge and assumed states and model objectives;
** '''Models''' depict observed processes using logical or mathematical constructs based on underlying theories about system states or dynamics using continuous equations to describe discrete processes.
** '''Curve fitting''' (including interpolation and extrapolation) with mathematical expressions using model variables where inputs are discrete data points.
** Regan (2002) argues that '''model uncertainty''' is "...notoriously difficult to quantify and impossible to eliminate ...(and)...(t)he only reliable way of determining how appropriate a model is for prediction is to perform validation studies."
** Subjective judgment occurs due to the interpretation of scarce or error-prone data.
*** The only way to address this type of uncertainty is to "...assign a degree of belief about an event in the form of a subjective probability." (Regan (2002))
*Linguistic uncertainty - Linguistic uncertainty, or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. '''note-31 [24]], note-32 [25]]'''
** '''Context dependence''' is uncertainty concerning the context in which a statement is to be understood;
** '''Ambiguity''' occurs when words have multiple meanings, and further reduction to a single meaning is not logically possible in a given context.
** '''Underspecificity''' occurs in the presence of "unwanted generality" or multiple interpretations, and reduction to a smaller set or even a single interpretation is not logically possible.
*Stochastic or statistical uncertainty - Stochastic or statistical uncertainty, which is sometimes known as [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Aleatoric uncertainty] as well as the subject of [https://en.wikipedia.org/wiki/Stochastic_control stochastic control] pertains to the parameters of a risk model, originates from sampling or measurement error, and can be quantified and mathematically expressed (e.g., using confidence intervals).
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== Uncertainty Taxonomies in a Legal Environment ==
From a legal perspective, a definition of taxonomy is dictionary-based, such as "the systematic distinguishing, ordering, and naming of type groups within a subject field." given by Walker (op. cit.). Such legal concepts of scientific or engineering taxonomies are not required to be epistemically complete. Still, they must cover "the most significant aspects of ...(current, good discipline practices) ... and of the descriptive information commonly encountered by decision-makers" in a logically distinct manner. (Walker, 1991, p. 571) These taxonomies must be sufficient to catalog the types of uncertainty associated with descriptive assertion, including the critical subset of cause and effect assertions. This legal/decision-maker framework has presented descriptive uncertainty as having six components (Walker, 1991). Although not part of the Walker taxonomy, underlying the scheme is linguistic uncertainty, or "vagueness," as a source of uncertainty, including uncertainties due to context dependence, ambiguity, and under-specificity. '''note-33 [26]]''' Arguably, these uncertainties underlie the most basic element in the taxonomy.
* '''Linguistic '''- or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. (Regan, 2002)
* '''Conceptual'''- the definition by choice and design of descriptive concepts or variables to be used as [https://en.wikipedia.org/wiki/Predicate_(grammar) predicates] where the subject of an assertion identifies what is being discussed and the predicate provides information about the topic or characterizations such as what the subject is, what the subject is doing, or what the subject is like (Wikipedia).
* "Conceptual uncertainty, or the potential for conceptual error, arises whenever predication occurs. Whenever a concept is used to describe something, using certain concepts instead of others begins to structure how we understand the object, event, or instance under discussion. Predication or conceptualization generates useful information about things, but it also can inhibit our ability to think about those things with concepts other than those selected. The concepts used may not be the most fruitful or the best designed- either for scientific purposes or for making wise, fair, effective, and efficient decisions." (Edited for reading, Walker, 1991)
* Similarly, choice and design of descriptive variables enhance and restrict data set membership, creating potentially incomplete, incompatible, or inconsistent data sets, "...the classification categories employed, and the relationships among those categories (nominal, ordinal or scalar)." '''note-34 [27]]'''
* Concept uncertainty '''note-35 [28]''' like systematic error discussed above is notoriously difficult to recognize, except on theoretical grounds, and the magnitude and direction of the bias are known.
Both terms could be analogized to that of [https://en.wikipedia.org/wiki/Accuracy_and_precision#Terminology_of_ISO_5725 accuracy] in [https://www.bipm.org/en/publications/guides/ ISO 5725] which looks at the ability of the concept or system's ability to produce results that are proximate to the "true" value or state of the phenomenon. Conceptual uncertainty and systemic bias are related to another measure of uncertainty: validity. Walker has defined validity as the ability of a concept to quantitatively describe and explain what the phenomenon objective "truly" is. Walker also ties the concepts together when he states, "Validity concerns the "accuracy" of the measurement data, not its precision." (Walker, 1998) 
Additionally, conceptual uncertainty and associated validity issues could be propagated throughout this taxonomy. Every component of action and decision in the face of uncertainty raises validity issues. Walker recognized this when the author defined epistemic choice for concepts used throughout the other uncertainty components or layers.
* '''Measurement''' uncertainty or Misclassification error- the application of the underlying concepts or variables to specific, individual cases and the uncertainty of the reliability (Walker, 1998) of the resultant data. (It is important to remember that assessment observation and measurement are used interchangeably in this analysis.) Thus, an assessment method or procedure is considered "reliable" or "precise" in the scientific terms of ISO 5715 if it repeatedly produces consistent results.
* Measurement uncertainty is logically distinct from conceptual uncertainty, which can only occur after any conceptual uncertainty has been established. Still, misclassification can occur even when the conceptual uncertainty has been minimized. Conversely, underlying errors or errors made in measurement or classification propagate thru the reasoning chain and reduce the quality or value of the final result.
* While uncertainties associated with quantitative measurements or assessments can be statistically evaluated and described, uncertainties related to qualitative assessments are more problematic.
**Working through the taxonomic chain from base concept to causal explanation requires amassing and integrating qualitative information and quantitative data into input parameters for mathematical models. Developing procedures for producing consistent, repeatable sets of information from assessments that minimize conceptual error or systemic bias requires professional efforts to "validate" such methods, models, and judgments at all layers in the process. Validation as a process is something that civil engineering as a practice needs to embrace more frequently.
* '''[https://en.wikipedia.org/wiki/Sampling Sampling]'''- in the classical statistics sense, is the selection of a limited subset of a larger population to use as a basis for making estimates about the population in the form of attributes, proprieties, or parameters. The resulting information would be determinative in making forecasts about future population sampling or possessing a desired amount of predictive power. The uncertainty about the ability of this information to correctly predict future samples is termed sampling error.
* '''Modeling'''- Modeling uncertainty arises whenever a claim is made that out of a class of candidate relationships and variable pairings, one variable pairing inclusive of constants is chosen that possesses a particular or persistent mathematical relationship to another variable X. Modeling errors arise in selecting the wrong variable pair and constants or by incorrectly specifying its constants. (Walker, 1991, pg. 599)
** '''Causal'''- In causal modeling and analysis, the relationship between causal variables is not strictly a mathematical function. Still, it makes testable predictions and explains "...how a system of variables works, why a system works the way it does, or why it makes sense to think of certain variables as a system" at all." (Walker, 1991, pg. 609)
** '''Epistemic'''- the choice of interpretations for fundamental, logical concepts used throughout the other components or layers.
The components of this uncertainty taxonomy can combine in different ways to "...produce different aggregate uncertainties." '''note-36 [29]'''
:''' Only the scientific and engineering professions possess the capacity to develop and validate procedures and models that combine the different kinds of qualitative '''note-37 [30]''' and quantitative information and their associated potential for error into an aggregate measure or "scalar variables" of uncertainty.'''note-38 [31]]
Scalar variables are quantitative variables whose categories are related by some measure of the relevant property's incremental frequency, degree, or amount. (Walker, 1991) Examples are Modulus of Elasticity, Compression stress in elastic materials, etc.

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=== Economics Uncertainty Taxonomies ===
Like scientific concepts of uncertainty, economics is environment-specific (in this case, read sector of economy or markets), phenomenon-oriented (in this case, read human activity and manufactured phenomena such as firms and markets guided by human judgment and decision), and model-centric.
:''' Economics is not interested in all forms of uncertainty, only specific, meaningful subsets possessing casual connections with material impacts, often labeled as 'risks'.'''
One of its earlier writers ([https://en.wikipedia.org/wiki/Frank_Knight#Life_and_career Knight], 1921, 1948, 1957) attempted to define "uncertainty" as the presence of "defects of managerial knowledge" (or [https://en.wikipedia.org/wiki/Knightian_uncertainty knightian uncertainty] and thus risk centric) as risk that is immeasurable, not possible to calculate.(Wikipedia) Risk in this sense was defined as "the ordinary risks of business activity which can ... be reduced... by applying the insurance principle." Knight does acknowledge that risk is a specific and unambiguous subset of uncertainty. (Risk, Uncertainty and Profit, 1957, pg. 19) For Knight, measuring uncertainty meant measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences, which Knight referred to as "the imperfection of knowledge". (Risk, Uncertainty and Profit, 1957, pg. 197) One of Knight's many contributions to this analysis was recognizing information and knowledge's role in economic activity.
:"''(W)e are concerned only to emphasize the fact that '''knowledge is in a sense variable in degree and that the practical problem may relate to the degree of knowledge rather than to its presence or absence in toto.''' We live only by knowing something about the future, while the problems of life, or conduct at least, arise from knowing so little. This is as true of business as of other spheres of activity. The situation's essence is action according to opinion, of greater or less foundation and value, neither entire ignorance nor complete and perfect information, but partial knowledge. If we are to understand the workings of the economic system, we must examine the meaning and significance of uncertainty; and to this end, some inquiry into the nature and function of knowledge itself is necessary.''" (Knight, pg. 199) Emphasis added
Knight also reinforced the difficulties in classifying uncertainties when he analyzed life insurance. In this case, accidental death was an uncertain phenomenon compared to sickness and accident, where an "...objective description and classification of cases was impossible..." (Knight, pg. 248) Another factor that made uncertainties ineligible as risks and, therefore, uninsurable because they were unclassifiable was the exercise of judgment in making decisions by the businessman. (Knight, pg. 251)
This is a simple classification of economic uncertainty and, as an idealization, is philosophically controversial. '''note-39 [32]'''
* Uncertainty and information about the economic environment are distinct from uncertainty about others’ behavior or choices.
* Risk as a specific subset of uncertainty implies that "...all possible acts are known, all possible outcomes arising from each act are known, and it is possible to assign probabilities to each act." '''note-40 [33]'''
Like the economic [https://en.wikipedia.org/wiki/Theory_of_the_firm theory of the firm], an economic theory of uncertainty seeks to explain and predict the nature of a specific subset of uncertainty (in this case, risk relating to physical phenomena and human activity), including its behavior, structure, and relationship to the universal uncertainty of physical phenomena. In this sense, uncertainty, like firms and markets, can establish price equilibriums under the right circumstances different from those that might have occurred in either of the other two. Such a theory of uncertainty offers a partitioning scheme that decomposes uncertainty into distinct and meaningful subsets, of which risk is a significant one. It also explains how the presence and variability of knowledge cause or " drive" economic consequences. Unlike physical phenomena, economic activity creates artificial objects such as products, firms, mediums of exchange, markets, economies, and knowledge about those activities. The economics of uncertainty exists as an alternative to "certainty" models that assume away imperfect knowledge and unknown choice/judgment preferences when it is more efficient for economic decision-making. This also allows the introduction of the logical concept of "hazard" versus "risk". Frank used hazard extensively in Risk, Uncertainty, and Profit, but not to the extent that one could say it was interchangeable. An example of this is in the term "[https://en.wikipedia.org/wiki/Moral_hazard Moral hazard]," where a firm could engage in "riskier" and potentially "uninsurable" behavior if the information were transparent, and, therefore, insurance coverage of losses is limited. note-41 [34]], note-42 [35]]

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=== Uncertainty and Risk in Policy and Regulatory Environment ===
:''"Hume's 'just reasoner', when faced with a difficult public policy decision in current times, would probably commission a risk analysis. But how could they incorporate 'a degree of doubt, caution, and modesty?"'' '''note-43 [36]'''
The problem of "decision-making in the face of uncertainty" is how to regulate based on incomplete information that has the potential to be materially inaccurate. A contextual assumption is that we need to evaluate decision rules for dealing with uncertainty. As a social enterprise, risk regulation, whatever its substantive objectives, should be as '''effective, efficient, and equitable''' as practically possible. These "three E's" form a set of "process objectives" or "meta-goals." From the uncertainty standpoint, causal information can be usefully divided into two major categories: information about groups and information about individuals.

Each category, group, and individual has its distinctive types of inherent uncertainty. These are logically distinct, generally independent, and cumulative, contributing to '''(?????)'''

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=== Engineering Uncertainty Taxonomies ===
Like scientific and economic concepts of uncertainty, engineering is environment-specific, phenomena-oriented, model-centric, external choice, and driven by decision-making in the face of uncertainty. Like economics and unlike science, engineering is not interested in all forms of uncertainty, only those with determinable and material consequences. Like economics, engineering conceives risk as a specific and unambiguous subset of uncertainty determined by measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences. Also, engineering recognizes the role information and, by extension, knowledge plays in constructing the built environment.
'''note-44 [37]]''' Like economics, engineering differs from economics and scientific uncertainty in its interest in behavioral uncertainty.
*"Behavioral or interaction uncertainty is how individuals or organizations act or interact. Behavioral uncertainty arises from four sources: design uncertainty, requirement uncertainty, volitional uncertainty, and human errors." '''note-45 [38]]'''
** A design uncertainty is a choice among alternatives over which an individual or group of individuals exercises direct control but has not yet decided upon.
** Requirement uncertainty includes parameters of interest to and determined by the stakeholder, independent of the engineer or designer.
** Volitional uncertainty is uncertainty about what the subject him/herself will decide. Other people’s future actions and conduct are not entirely predictable, particularly when dealing with other organizations.
** Human errors occur during the development of a system or project due to blunders or mistakes by an individual or individuals.

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==Practice Frameworks for Uncertainty and Risk in Civil Engineering Practice==
===PMIBoK===
PMI does not define uncertainty, but the PMI Body of Knowledge uses the term in 45 places (uncertainty) and as describing properties in 8 places (uncertainties). [See Note 8] PMI does define "risk" and uses the term over 1,300 times in the same document. In its glossary, PMI also defines terms such as "threat" and "opportunity" but not events.[391 The BoK contextually defines uncertainty when it states that project risk has its origins in the uncertainty present in all projects. (Op. Cit., pg 309) 
'''Uncertainty''' is contextually defined and taken as having the property of affecting project execution and the ability to meet stakeholder expectations. Uncertainty is more than the sum of all individual risks (known and unknown). The nature of this uncertainty is not defined, only its capacity to affect something else or its properties. 
'''Risk''' is defined as " ... an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives." (Ibid.) Beyond this formal definition, PMI adds context when, in its risk management section (Sec. 11 ), the BoK states that risk is a caused event or condition with multiple causes and impacts. The BoK even offers specific techniques for mapping cause-and-effect relationships and influence diagrams. (Sec. 11.2.2.5) Not as evident but equally important is the recognition that not only is risk caused but the impact is "triggered". The risk trigger is an event or situation that signals that it is about to occur (Glossary, pg. 566). Risk conditions are factors that affect or otherwise contribute to risk, such as project stakeholders. Risk implicitly retains some residual element of probability in it, or a value of less than 1.0 with risk that approaches a level of near certainty is termed "issues" or "realized risk" .(Op. Cit., pg 309). PMI also links risk to underlying variations in project outcomes. (Ibid.) Risk is based upon data and information which can be assessed as to its quality (Sec. 11.3.2.3). This analysis looks to determine the value of the information and examines the degree to which the risk information is understood as well as its " ... accuracy, quality, reliability and integrity ... " (Ibid.) Part of that is understanding the relationship between occurrence and impact as outlined in PMl's Figure 11-10 Risk Impact Matrix and developing a model for prioritizing risk products and setting the threshold for action.
====Commentary:====
For defining and identifying risk, PMI notes in Sec. 11.2.2.4 that " ... Every project and its plan is conceived and developed based on a set of hypotheses, scenarios, or assumptions." Part of the risk analysis process is identifying risks to the project, such as inaccuracy, instability, inconsistency, or incompleteness of assumptions. Similarly, the quality of management plans, as well as their consistency with others and the project objectives and assumptions are 11 •• .indicators of risk in the project." (Sec. 11.2.2.1) 
Similarly, the role of engineering economics in risk is embedded or implied in the PMI definition of risk when the BoK discusses risk identification as a process of assessing which risks out of the total risk exposure for the project "may affect" the project and distilling their characteristics. Embedded in this statement is the concept that not all risks can cause economic impacts (positive and negative) to the project. Also embedded is the requirement to understand risk characteristics to identify project risk (Cost and schedule). Lastly, risk identification is an iterative process of incrementing project risk documentation, much like project scope, cost, and schedule documentation. 
Lastly, PMI acknowledges the complex constitution of risk aggregates or the "sum" of all risks in its material, but is it a sum? Risk information has descriptive and explanatory content. These are knowledge and meta-knowledge components. Understanding project assumptions is project-specific knowledge, but identifying risks from assumption inaccuracy, instability, inconsistency, or incompleteness requires professional or program meta-knowledge. 
'''Uncertainty exists in all projects, but risk, a subset of uncertainty with material economic impacts, is a key meta-knowledge element for civil engineering practice.'''
===Software Development Process- Risk Focused===
The Unified Process requires the project team to focus on addressing the most critical risks early in the project life
cycle. The deliverables of each iteration, especially in the Elaboration phase, must be selected to ensure that the greatest risks are addressed first. <ref> Booch, Grady, Ivar Jacobson, and James Rumbaugh. "The unified software development process."
Reading: Addison Wesley (1999).</ref>
===American Society of Civil Engineers (ASCE)===
ASCE does not define uncertainty, but the ASCE CE Body of Knowledge (CEBoK) uses the term in 22 places (uncertainty) and describes properties in 13 places (uncertainties). 
The CEBoK does not use the phrase "uncertainty and risk" as PMI and AACE do in their publication. ASCE reverses the two (Risk and uncertainty) in twelve places regarding technical outcomes (Outcome 12). A fundamental difference between PMI and ISO is that risk is considered a subset of uncertainty. ASCE does not define "risk" but uses the term 25 times in the same document. Almost all are in the context of variation of design parameters and not in the classical context of cost, schedule uncertainty, and risk.
====Commentary:====
...
=== American Association of Cost Engineers (AACE) ===
AACE defined uncertainty, risk, and other related terms in its Risk Management Dictionary. <ref>Risk Management Committee uRisk Management Dictionary, Cost Engineering Vol. 37/No. 10, OCTOBER 1995. </ref>
AACE's overall strategy was to define risk and other terms that stem from base uncertainty. AACE first defined uncertainty as .... "(t)he total range of events that may happen and produce risks (including both threats and opportunities) affecting a project (see opportunities, events, conditions, risk, and threats" where the following sub-definitions apply:
** Biases--A lack of objectivity based on the individual's position or perspective. Systematic and predictable relationships between a person's opinion or statement and his/her underlying knowledge or circumstances. Note: there may be "system biases" as well as "individual biases."
** Condition (Uncertain Condition)-Any specific identifiable circumstance (such as the rate of inflation or the quality of labor available) that might affect the outcome of the project
** Event (Uncertain Condition)-is a specific identifiable action (such as a large government project being started in the same labor area as your project) or an act of nature that might happen and that (if it does happen) could affect the outcome of the project.
** Opportunities are Uncertain events that could improve the results or improve the probability that the desired outcome will happen
** Threats are Uncertain events that are potentially negative or reduce the probability that the desired outcome will happen.
*** AACE defines Risk as an "ambiguous term" (sic) that is synonymous with uncertainty or a negative subset such as "threats", or an overall negative impact of all possible uncertainties.
====Commentary:====
...
==Limitations of the definition==
...
==Working definition==
...
==Beneficial outcomes==
...
==See also==
Wiki articles on the project.

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==Notes==
<references group="note" />

# See McDaniels et al. 2004 for an extensive discussion and bibliography on the historical development of Risk Analysis and some key milestones in the 20th century.
# Open item
# The Merriam-Webster dictionary defines uncertainty:
::"the quality or state of being uncertain," which is a circular definition. Likewise, one could define uncertainty as the state of not being certain. Synonyms are distrust, doubt, misgiving, mistrust, reservation, skepticism, and suspicion. Another writer added indefinite, indeterminate, not certain to occur, problematical, unreliable, untrustworthy, unknown beyond doubt, dubious, doubtful, not clearly identified or defined, not constant, variable, and fitful to the list.
::Han, Paul KJ, William MP Klein, and Neeraj K. Arora. "Varieties of Uncertainty in Health Care: A Conceptual Taxonomy." Medical Decision Making 31.6, (2011 ): 828-838.
::: Han, ct. al. noted that any definition of uncertainty encompasses " ... numerous types, sources, and manifestations of uncertainty, and ... (any) ... useful working definition of uncertainty needs to specify the concept underlying these varied meanings of the term." (Ibid.)
<ol start="4">
<li> Implicit in this definition of uncertainty as a "state of' is ... </li>
: " ... a conceptualization of uncertainty as a subjective, cognitive experience of people--a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one's lack of knowledge, without which one could not feel uncertain; '''uncertainty is a form of "meta-cognition" ... ( or alternatively, its main component, meta-knowledge) ... -a knowing about knowing.'''" (Han, 2011, op. cit., Emphasis added)
</ol>
<ol start="5">
<li> Cox wanted his system to satisfy the following conditions:</li>
: Divisibility and comparability- The plausibility of a statement is a real number and depends on the information we have related to the statement.
: Common sense - Plausibilities should vary sensibly with the assessment of plausibilities in the model.
: Consistency - If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). -preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf
</ol>
<ol start="6">
<li> The utility of existing taxonomies of uncertainty in civil engineering and, by extension, "risk" has been unsatisfactory for this very reason. This is particularly true in cases where the source of the uncertainty is conflicting versus incomplete information. See Regan, et. al.,"A taxonomy and treatment of uncertainty for ecology and conservation biology" (2002)
for a survey of scientific taxonomies for uncertainty.</li>
<li> Regan et al. argue that " ... genuine examples of this kind of uncertainty are hard to find. Even classic cases of random experiments like coin tosses and the throwing of dice are deterministic; it is just that we do not have enough information about the dynamic processes and initial conditions to make any sensible estimates about the outcomes. Such processes are, for all intents and purposes, inherently random, but they are not genuinely inherently random. For similar reasons, complex systems such as ecosystems and weather patterns are unlikely to be inherently random. Similarly, chaotic systems are entirely deterministic. They are unpredictable because the deterministic processes generating them and the relevant initial conditions are hard
to fully specify (see Stewart 1989, Sugihara et al. 1990)." (Regan (2002) </li>
<li> The CEBoK uses the phrase "uncertainty and risk" twice in reference to scheduling (Section 6.5,2) and cost estimating (Section 7.2,2) as well as reversing the two (Risk and Uncertainty) also in two places in discussing project life cycle (both in Section 2.4.1) </li>
</ol>

==References==
<references />

Uncertainty and Risk in Civil Engineering Practice

2024-11-24T16:18:58Z

WikiSysop:

'''Life is short and the art long; the occasion instant, experiment perilous, decision difficult.''' (Hippocrates as quoted in Fox,)
:''See also [[Project Definition]] , [[Project Life Cycle and Phase Models]] , [[Project Delivery Methods]]''
:''See also [[Technical Outcomes for Civil Engineering:Risk and Uncertainty]]''
==Basic Considerations==
As human beings,'' ... being alive means seeking opportunities and taking risks.'' As knowledge professionals
living in the 21st century, this means coping with an increasingly complex number of uncertainties for humans
living in this environment. We seek to understand better how these uncertainties can be characterized and
managed. The essence of this article and the experience for engineers in general and civil engineers in particular is
that manageable uncertainty is, by definition, termed risk, and its kindred cousin is termed hazard. This causes us to experience the
:" ''... human dread of and fascination for risk and the increasingly important role of risk analysis within societies ...'' " <ref name="McDaniels"> McDaniels, Timothy, and Mitchell Small. Risk analysis and society: an interdisciplinary characterization of the field. Cambridge University Press, 2004, page 1 </ref> (Ibid.)
and, by extension, civil engineering. McDaniels et al. argue that risk management has been
fundamental to our social and governance development for the past 10,000 years. The scale and shape of the
uncertainties faced in this period shaped the societies that have developed today. The central thrust of this effort
over the centuries has been to reshape and re-frame our understanding and conception of uncertainty from one of
complete unknowing and simple acceptance as our fate in life to one of management (cf "''Against the Gods''"
concept in Bernstein's book <ref name = Bernstein">Bernstein, Peter L., and Jesse Boggs. Against the gods. Simon & Schuster 1997.</ref>.
: ''All the knowledge professions and disciplines have struggled with managing uncertainty, for it is impossible to manage unbounded uncertainty.'' <ref group="note">See McDaniels et al. 2004 for an extensive discussion and bibliography on the historical development of Risk Analysis and some key milestones in risk analysis in the 20th century.</ref>
As outlined below, professions such as civil engineering have successfully acted in the face of uncertainty. It lies in the profession's ability to reduce a wide variety of uncertainties into increasingly smaller and crucially bounded subsets that can be managed. These are called 'risks. More recently, this process has evolved, and individual disciplines such as Civil Engineering (CE) have developed their knowledge and models to perform risk analysis. Risk is also taught as a distinct discipline and specialty practice within civil engineering. Still, it has some unique features that set it apart from other more classical practices within CE. As such, it is one of the first of some very specialized CE practices that use knowledge about civil engineering knowledge, or meta-knowledge. Other examples of civil engineering metaknowledge are project controls and quality controls.
==Semantic, Epistemic and Logical frameworks==
The semantic, epistemic, and logical frameworks for uncertainty and risk have several dimensions and layers of logical frameworks. The semantics problems include interchangeable usages for risk and uncertainty and hazard, uncertain and imperfect, and a lack of definitional material or context. The semantical scheme for this article will be to proceed from the distinction between certainty and uncertainty through marginal refinements and reductions of uncertainty up to the point of causal uncertainties. Beyond this point of semantics will be the logic frameworks or the subject of taxonomies of professional knowledge. First, it is about simple, testable phenomena, and then, it moves on to complex taxonomy schemes for artificially constructed phenomena or engineering projects.
<ref group="note">TBD</ref>
==Semantics framework==
===Certainty and Uncertainty===
''''' There is no such thing as absolute certainty, but there is assurance sufficient for the purposes of human life.''''' ([https://en.wikipedia.org/wiki/John_Stuart_Mill John Stuart Mill]) 
'''''If you tried to doubt everything, you would not get as far as doubting anything. The game of doubting itself presupposes certainty.''''' ([https://en.wikipedia.org/wiki/Ludwig_Wittgenstein Wittgenstein] # 115 from On [https://en.wikipedia.org/wiki/On_Certainty Certainty]) 
It is important to note that the references to epistemic or epidemiological knowledge in this article are assumed to relate to three forms of knowledge, namely:
* Knowledge that ( [https://en.wikipedia.org/wiki/Descriptive_knowledge descriptive or declarative or propositional knowledge])
* Knowledge how ( or "[https://en.wikipedia.org/wiki/Procedural_knowledge know-how]"), and
* [https://en.wikipedia.org/wiki/Knowledge_by_acquaintance Knowledge by acquaintance].
'''Certainty''' has been defined as "an epistemic property" of knowledge in all its forms and the state of our beliefs about that knowledge.
<ref>Certainty, Stanford Encyclopedia of Philosophy, accessed on September 12, 2015, at http://plato.stanford.edu/entries/certainty/#ConCer </ref>
Certainty about any belief about knowledge implies that it is not subject to doubt or skepticism. This immunity to criticism can be dogmatically based, emphasizing the importance of a propositional-based sense of truth over experiential, sensory perceptions. <ref>Definition of [https://en.wikipedia.org/wiki/Dogmatic_theology Dogmatic theology or belief], accessed at Wikipedia</ref>
: '''The demarcation line between dogmatic and non-dogmatic beliefs lies in the presence and recognition of specific criteria and information that would make the believer change their beliefs.'''
An empirical framework of [https://en.wikipedia.org/wiki/Testability testability] and [https://en.wikipedia.org/wiki/Falsifiability falsification] is required to recognize this and limit dogmatism. For example, one could argue that people would change their minds if God asked them to. Similarly, one could construct a concept of epistemic as opposed to dogmatic belief certainty as the ability to know anything that one chooses to know and can be known or [https://en.wikipedia.org/wiki/Omniscience inherent omniscience].

A second kind of certainty is epistemic, when conviction reflects the highest possible support for a belief. In this sense, knowledge is separate from beliefs, although someone may have beliefs about a property, such as certainty of knowledge. Logically, it has been shown that there will be unprovable statements within the system for any such knowledge system. Secondly, the knowledge system cannot demonstrate its own consistency. ([https://en.wikipedia.org/wiki/Gödel%27s_incompleteness_theorems Gödel's incompleteness theorems])
: '''Certainty, in real life, is useless or often damaging (the idea is that "total security from error" is impossible in practice, and a complete "lack of doubt" is undesirable)''' ([[https://en.wikipedia.org/wiki/Certainty Physicist Carlo Rovelli]])
'''Uncertainty''', on the other hand, arises immediately in the slightest amount of doubt or criticism.
<references group="note" /> The Merriam-Webster dictionary defines uncertainty as “the quality or state of being uncertain,” which is something of a circular definition. Likewise, one could define uncertainty as the state of not being certain.

Synonyms are distrust, doubt, misgiving, mistrust, reservation, skepticism, and suspicion. Another writer added indefinite, indeterminate, not certain to occur, problematical, not reliable, untrustworthy, not known beyond doubt, dubious, doubtful, not clearly identified or defined, not constant, variable, and fitful to the list.

Han, Paul KJ, William MP Klein, and Neeraj K. Arora. "Varieties of Uncertainty in Health Care A Conceptual Taxonomy." Medical Decision Making 31.6 (2011): 828-838.
:Han, et. al. noted that any definition of uncertainty clearly encompasses "...numerous types, sources, and manifestations of uncertainty, and ...(any) ...useful working definition of uncertainty needs to specify the concept underlying these varied meanings of the term." (Ibid.)</ref>
↑ Implicit in this definition of uncertainty as a “state of” is ...
"...a conceptualization of uncertainty as a subjective, cognitive experience of people—a state of mind rather than a feature of the objective world. The defining feature of this state, furthermore, appears to be lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one’s lack of knowledge, without which one could not feel uncertain; uncertainty is a form of “meta-cognition” ...(or alternatively, its main component, meta-knowledge)... —a knowing about knowing." (Han, 2011, op. cit.,Emphasis added)</ref>[Note 3]</ref>
Similarly, a general definition of uncertainty could be given as "...any departure from the unachievable goal of complete [https://en.wikipedia.org/wiki/Determinism determinism]." <ref name="Reference5">[5]</ref> Reasoning under uncertainty is very different than performing the same under certainty. In reasoning under certainty, one has complete knowledge and deduces without doubt and equally important, without limitation, thereby concluding free from error. Reasoning under uncertainty, one works in a state of incomplete, inconsistent, and limited knowledge; doubts cloud any statements or assertions and thereby taint any deductions/inferences resulting in the potential for error. <ref name="Note4">[Note 4]</ref>
:'''Knowledge professions reason in a state of incomplete, inconsistent and limited knowledge with doubts that cloud any statements or assertions and taint any deductions/inferences resulting in the potential for error.'''
:''' It is also important to note the semantical and logical correlation between 'reasoning under uncertainty' or 'acting in the face of uncertainty' with the 'potential for' or 'presence of' error. The presence of uncertainty is invariably linked to the potential presence of error.''' The [https://en.wikipedia.org/wiki/Contingency_(philosophy) contingent] nature of uncertainty logically implies the contingent nature of error.
:'''Knowledge Professions use error as a proxy for uncertainty such that within discipline knowledge frameworks, uncertainty can be managed.''' The rationale for this belief is that error can be reliably described, quantified, explained, and ultimately reduced in ways that uncertainty cannot.

'''Uncertainty''' implies a range of variation that is impossible in certainty. Concepts and definitions of uncertainty are unbounded and vast but start from the point of complete and total ignorance. In describing uncertainty, statements can range from complete ignorance to relatively high degrees of belief that there is less potential for error in our reasoning or action. The latter is based upon confidence in justified knowledge, favorable past experience, and reliable parameters/models. One can't make that statement about ranges in certainty. If certainty, by definition, precludes doubt of any type or nature, then how could that be graduated to any degree? 
Uncertainty, on the other hand, can be reduced through disciplined efforts to acquire knowledge. In short, we can reduce or even manage uncertainty (to a degree); how can certainty be improved? Are there higher degrees of perfection? The answer is no. We reason from an uncertain starting point. Yet this effort presumes, as Mill and Wittgenstein postulated, that we can develop or acquire an increasing degree of belief or conviction that there is less potential for error in our reasoning or action. This position assumes that some way exists to identify what a lesser degree of uncertainty would look like.
:'''Knowledge professions acquire an increasing belief or conviction that there is less potential for error in their reasoning or action based on justified knowledge, favorable past experience, and reliable parameters/models.'''
Uncertainty is, in some form, amenable to description and explanation through observation and analysis. It is explainable and predictable to such a degree that it interests professionals such as scientists and engineers. Professional knowledge of this type has explanatory or predictive power, albeit limited in scope and application to relevant subjects such as physical objects or phenomena and functionality.
:'''The very act of reducing the scope of uncertainty to a limited set of phenomena implies choice and, ultimately, the decision to act in the face of such uncertainty, but acting or judging in this manner adds more value than doing the same under relative ignorance.'''
'''Uncertainty''', as described above, implies a hybrid nature. Examples of this are economic or decision-theoretic applications. In economics, [https://en.wikipedia.org/wiki/Perfect_information perfect information] allows one within the limiting framework of perfect competition in economic games to make decisions with 'perfect knowledge.' The crucial difference here is the distinction between '[https://en.wikipedia.org/wiki/Perfection perfection],' broadly, a state of completeness and lawlessness, and 'perfect, which can be thought of as an analytical limit that is approachable but can never be attained. Arbitrarily delimiting the realm of knowledge into defined, finite boundaries allows the simulated production of perfect choice, assumed crucially, to be free from error. The hybrid nature of this form of 'simulated' certainty produced within boundaries defined by assumptions and, therefore, clouded by doubt is itself a form of uncertainty. The importance of this is the semantical and logical association that exists between certainty and perfection versus the hybrid concept of a perfect anything, whether knowledge, infraction, competition, etc., or in the case of engineering, elastic, permeable, conductive, etc., being contained within an admittedly imperfect matrix of uncertainty.
:'''Simply put, a finite, heavily bounded piece of uncertain knowledge can be improved through the simulated use of [https://www.merriam-webster.com/dictionary/assumed assumedly] perfect information, but the perfection of knowledge cannot be attained or simulated.'''
Engineering and Economics, for example, simulate finite elements of perfect knowledge or information, heavily bounded with assumptions and a range of applications. By doing so, these professions reduce the bounds and variability of uncertainty and thereby manage it. Using these hybrid models to acquire knowledge takes place in an economy (regulated or institutional) and environment (transparent and structured), which puts pressure on the various professions to recognize and use qualitative and quantitative knowledge better to reduce uncertainties in their activities. The challenges in this reflect the nature of the profession's or discipline's knowledge (e.g., scientific, engineering, or medical), the policies and structure of the relevant professional knowledge bodies, process objectives and constraints, and "the elusive demands of politics." [6] One common theme runs through all of these efforts to address the presence of uncertainty in professional activities. 
:"The available scientific information upon which a decision must be made is almost always a mixture of engineering knowledge and uncertainty. Regardless of the information or methodology, there is the potential for error. This is true regardless of the relevant science, whether archaeology or aeronautics, economics or engineering, pharmacology or toxicology or epidemiology. Achieving the best social decisions requires not only understanding and using what we know but also appreciating and weighing the extent of our uncertainty. In making the best use of ... information in ... decision-making, it is still true that the beginning of wisdom is knowing what it is we do not know." (Vern, Op. cit., Reformatted and engineering substituted for scientific) 
'''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty to develop explanations, calculate, and make predictions. This professional knowledge converges on but never reaches certainty, producing a "legitimacy" from the fruitfulness of its use.''' <ref>Randomness Is Unpredictability, Antony Eagle, The British Journal for the Philosophy of Science, Vol. 56, No. 4 (Dec., 2005), pp. 749-790 </ref> 
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=== Bias and Uncertainty ===
Up to this point, the concept of error, as outlined in uncertainty, could easily be simulated by a truly random variable. There should be no detectable differences around the unseen or specified statistical mean. However, several instances in experience point to a tendency towards one extreme or a pattern of error, a collective that is in itself an error of errors, namely [https://en.wikipedia.org/wiki/Bias bias].

Arguably, this could be part of the choice uncertainty discussion below, but it makes more sense to be looked at on its own. Acting in the face of uncertainty is an exercise of personal knowledge or experience. Whether using professional knowledge or individual experience, the potential is there to make a series of choices that, in some instances, make errors more likely than they would be if considered in the context of a statistical analysis.

This tendency for an individual to make the same error in a predictable or repeatable manner is termed bias. Personal experience, beliefs, knowledge, data, and models all have inherent built-in factors, errors, or defects that create predictable error patterns when applied in decision-making. Walker (1998) argues implicitly that such bias can also be considered systemic error. This type of uncertainty is difficult to identify except on theoretical grounds when the magnitude and direction of the bias are known. Most of these biases, whether systemic or knowledge-based, individual or practice-based, require disciplined efforts to "validate" such methods, models, and judgments at all layers while bounding the uncertainty.
: This is the challenge to professional knowledge that must be overcome, namely, developing and validating models and methods for managing bounded uncertainty that minimize this tendency towards repeatable groups of error or bias. Therefore, from this point forward, the discussion will refer to 'uncertainty and its proxies, error, and bias.'
===Propagating Error and Uncertainty===
Partitioning uncertainty and its proxies, error, and bias into logically distinct subsets involves or is associated with multiple-step execution or choice frameworks. Combining those measurements or choices into single parameters or choices involves, in some cases, assembling the data/information in a unique or limited range of sequences. This implies that associated errors in observing, calculating, or choosing will have unequal impacts or consequences depending on where in the chain the error occurs. Also implied in this is the possibility that subsequent errors will be causally linked to some degree. (In statistical practice, this depends upon whether the errors are independent of each other or correlated, specifically, co-variant.)
:'''Professions develop knowledge of how uncertainty and its proxies, error, and bias propagate through an ordered and normative series of observations or choices. This allows the profession to prioritize error and bias reduction to achieve an optimized reduction of uncertainty.'''
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===Cognition, Metacognition and Uncertainty===
The concept of choice and the resultant error in uncertainty can be further delimited depending on the role of [https://en.wikipedia.org/wiki/Cognition cognition], [https://en.wikipedia.org/wiki/Metacognition meta-cognition], and independent external elements. The rationale for this argument is that such choice activity in the face of uncertainty is a conscious, cognitive act.

An argument could be made that such cognition is a prerequisite for exercising choice and introducing error, as presented and discussed above. The counterfactual to this argument is the example of uninformed choice. Choices introduce as much as errors or more as informed choices could be made. (???) Choice, in this sense, is indifferent to knowledge. Purposeful choice in an environment of objectives requires knowledge, method, and experience, but uninformed, speculative choice does not.
:"''' Cognition''' is the set of all mental abilities and processes related to knowledge, attention, memory and working memory, judgment and evaluation, reasoning and "computation," problem-solving and decision making, comprehension and production of language, etc. Human cognition is conscious and unconscious, concrete or abstract, as well as intuitive (like knowledge of a language) and conceptual (like a model of a language). Cognitive processes use existing knowledge and generate new knowledge." (Wikipedia)
:"'''Metacognition''' is "cognition about cognition", or "knowing about knowing" and can take many forms. It includes knowledge about when and how to use particular strategies for learning or problem-solving. There are generally two aspects of [https://en.wikipedia.org/wiki/Metacognition metacognition]: knowledge about cognition and regulation of cognition."
Some types of metacognition knowledge are:
:'''Person knowledge''' (declarative knowledge), which is understanding one's own capabilities.
:'''Task knowledge''' (procedural knowledge), which is how one perceives the difficulty of a task, which is the content, length, and type of assignment.
:'''Strategic knowledge''' (conditional knowledge) is one's capability to use strategies to learn information. (Accessed at Wikipedia)
Like metacognitive knowledge, metacognitive regulation or "cognitive control" contains three essential skills.
:'''Planning:''' refers to the appropriate selection of strategies and the correct allocation of resources that affect task performance.
:'''Monitoring:''' refers to one's awareness of comprehension and task performance.
:'''Evaluating:''' refers to appraising the final product of a task and the efficiency at which the task was performed. This can include re-evaluating strategies that were used. (Accessed at Wikipedia)
Nothing has been said up to this point that limits this structure to an individual, or a collective, or two entities making cognitive choices that are incompatible or inconsistent to varying degrees. Interpretations of this scheme include an individual professional exercising judgment in a decision that, in turn, relies upon the collective judgment and decision of the discipline as an underlying basis versus the decision of another individual to oppose/protest or otherwise contest that decision. Thus, the framework of cognitive/meta-cognitive applies to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, economics of regulatory practice, and game theory.
:By solving useful problems in the face of inherent uncertainty, discipline-specific frameworks of cognitive/meta-cognitive activity apply to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, the economics of regulatory practice, and game theory.
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===Probability and Uncertainty===
Probability is a [https://en.wikipedia.org/wiki/Coherence_theory_of_truth coherent] approach to uncertainty in the physical and mathematics or "classical" domains such as engineering. [8] and is the "...is certainly the best-known and most widely used formalism for quantifying uncertainty.[9] [https://en.wikipedia.org/wiki/Cox%27s_theorem Cox’s theorem] ("Any measure of belief is [https://en.wikipedia.org/wiki/Isomorphism isomorphic] (but not necessarily equal) to a probability measure") is a well-known argument for the validity of that argument. [Note 5]

Another writer, [https://en.wikipedia.org/wiki/Frank_Knight Knight] (1921,1956), presented the following taxonomy of probabilities: [10]
: '''Classical 'a priori' probability:''' As an idealized model, numerical probabilities are computed based on generalized principles of assigning equal likelihoods and mutually exhaustive possible outcomes (Runde, 1998) to all set members. Such probabilities are assigned to "...outcomes based on a judgment of indifference between those outcomes, that is, based on the absence of any evidence of real influences in play that may render any one outcome more or less probable than any other." (Runde, op. cit.) This builds upon Knights's concept of an 'absolutely homogeneous classification of instances that are completely identical except for [https://en.wikipedia.org/wiki/Indeterminate_(variable) indeterminate] factors. This judgment of probability or logical probability is on the same logical plane as the propositions of mathematics and ultimately [https://en.wikipedia.org/wiki/Inductivism inductions] from experience.'(Knight, 1956, pg.225)
: '''Bayesian 'a priori' probability:''' In contrast to interpreting probability as the "frequency" or "propensity" of some phenomenon, [https://en.wikipedia.org/wiki/Bayesian_probability Bayesian probability] is a quantity that we assign to represent a state of knowledge or a state of belief. In this view, probability is assigned to a hypothesis, often using the basis of past or prior experience. In contrast, under the frequentist view, a hypothesis is typically tested without being assigned a probability based on prior experience. The Bayesian interpretation of probability can be seen as an extension of propositional logic that enables reasoning with hypotheses, i.e., propositions whose truth or falsity is uncertain. Bayesian probability belongs to the category of evidential probabilities; to evaluate the probability of a hypothesis, the Bayesian probabilist specifies some prior probability, which is then updated in the light of new, relevant data (evidence). The Bayesian interpretation provides a standard set of procedures and formulae to perform this calculation. [11]
: '''Statistical or 'a posterior probability:''' Empirical evaluation of the frequency of association between predicates, not analyzable into varying combinations of equally probable or logical, 'a priori' alternatives. Knight argued that any "high degree of confidence" that the judgment that experience will remain valid for future predictions is "...still based on an 'a priori" judgment of indeterminateness." (Knight, Op. Cit.) Knight's argument to support this is what will be discussed further below that first, "...the impossibility of eliminating all factors not really indeterminate; and, second, the impossibility of enumerating the equally probable alternatives involved and determining their mode of combination so as to evaluate the probability by a priori calculation." (Knight, Op. Cit.) Knight noted that the main difference between this form and that of logical or inductive probability was the presence of empirical information, which allowed the analyst to identify propensity and direction in the observed phenomena. In this case, 'a priori' probability values may be derived from first principles and "statistical probabilities are determined a posteriori by the empirical method of counting instances." (Runde [1998] quoting Knight, op. cit.)
:'''Estimates:''' The distinction here is that there is no valid basis ('a priori' or 'a posteriori') for any classifying phenomena. For Knight, this form of probability presented the greatest logical difficulties of all ... but its distinction from the other types must be emphasized, and some of its complicated relations indicated..." (pp. 224-5, emphasis in the original)

Arguably, Knight's concept of probability can be viewed as "..a continuum of probability situations, depending on the degree of homogeneity of the 'instances' in question" [12] ; i.e. going from a logically distinct but equally likely set of elements to a unique set with one member with statistical frequency in the middle. Knight's main motivation for distinguishing between a priori probability and statistical probability underscores his opinion that "(i) the 'mathematical or a priori type of probability is practically never met with in business, while the second is extremely common'; and (ii) 'the statistical treatment never gives closely accurate quantitative results' (Runde quoting Knight pp. 215-16). Runde argues that Knight couldn't conceive of (empirically tabulated) occurrences that are used in daily commerce classes of instances that we have to make do within the course of everyday economic life could be divided into subclasses of instances that are sufficiently homogeneous to permit the determination of what he calls 'real' probability (p. 217).
:'''In modeling degrees of uncertainty, any measure of belief is isomorphic but not necessarily equal to a probability or statistics measure'''.
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===Explanation, Prediction and Uncertainty===
It should be clear that the general notion of uncertainty synonymous with complete ignorance must be bounded as part of a scheme to produce a useful concept of uncertainty in professional practice such as civil engineering.
:The first principle that must be introduced is that such uncertainty must be bounded by reliable and valuable knowledge gained from past experience combined with analytical and computational capabilities to address an immediate and real problem of interest. This could be viewed as the economic interest argument. Only uncertainties associated with physical phenomena and economic scarcity will be addressed.
:The second principle is that the knowledge model is capable of producing statements or assertions in the form of hypotheses that themselves are testable. [https://en.wikipedia.org/wiki/Testability Testability], in this sense, is defined as the property applying to an empirical hypothesis and involves two components:
::The logical property that is variously described as contingency, defeasibility, or falsifiability, which means that counterexamples to the hypothesis are logically possible. Contrast this to a [https://en.wikipedia.org/wiki/Tautology_(logic) logical tautology], which is always true that is true in every possible interpretation. The practical feasibility of observing a reproducible series of such counterexamples if they do exist. In short, a hypothesis is testable if there is some real, non-zero expectation of deciding whether it is true or false of verifiable, reproducible experience. Upon this property of its constituent hypotheses rests the ability to decide whether a theory can be supported or falsified by actual experience data. (Source Wikipedia.)
'''Knowledge models must be capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria described above in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error and bias.'''
===Variability and Aleatory Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge referred to as epistemic uncertainty. Acting in the face of epistemic uncertainty introduces choice uncertainty (discussed below) and results in the production of explanations and predictions. It is implied that there will be testable hypotheses in the form of outcomes of interest to the knowledge professions. Inherent in nature is a variation of results, which may appear to uncertain but isn't. Observations of phenomena will not be the same, and part of the experimental process is to narrow those results down to within an acceptable level of tolerance. Statistical measures seek to identify underlying and unobserved measures of central tendency. These parameters are not directly observed but are derived to a degree of confidence.
In some cases, variation around a mean or within a statistical range may be perfectly acceptable to the knowledge profession. Examples of this are in the physical sciences and medicine. In Engineering and, to a lesser extent, economics, the process or phenomenon may be required to be more controlled to a narrower range than what a natural or "unmanaged" amount of variation would allow. Several courses of action present themselves.
:The first would be to ignore the aleatory phenomenon and choice, which is rejected outright.
:The second would be to assume that, in effect, such variation is itself a form of minimally managed uncertainty and treat it the same as epistemic uncertainty. This is not a desirable outcome as reducing Aleatory error and bias requires
:A third approach would be to acknowledge that process variation is, in some forms, manageable. This means making choices and acting in a way that presumes that the natural occurrence of error can be reduced, or more importantly, bias can be reduced. There is ample precedent for the third approach in civil engineering design and project management. In this sense, the argument is made that the aleatory response to managed efforts results in an elastic variation range. Presumably, an effective management effort either "shifts" the central tendency of the process results or reduces the variation range of occurrence or some other population parameter. Choosing to act and accept that narrower range rather than the broader natural variation may result in an outcome that, while well within the bounds of the expected variation, is outside the arbitrary control limits established by the choice and action. While this phenomenon mimics the broader uncertainty and its proxy error, it is not an error. The information on the variation was known, and the ability to produce narrower results was an error, albeit a pseudo-error, when compared to the broader classes of uncertainty discussed above, such as total ignorance.
::Looking at uncertainty using this third approach means that the error in choosing a new target parameter for this process may contain epistemic errors (our knowledge of the phenomenon may be erroneous, or the model flawed), aleatory uncertainties as discussed, or, more importantly, choice error or bias in thinking that we can move the process results to meet the requirements.
:'''In civil engineering practice, this may mean applying professional judgment in developing partitioning schemes or allocating uncertainty between the three dimensions (Epistemic, Aleatory, and Behavioral) of uncertainty.'''
Lastly, raising the topic like this brings the questions of effectiveness and efficiency to the fore. Advancing knowledge models that reduce error and bias at first are largely choice models resulting in justifications and favorable experiences. At some point in the process, the knowledge profession advances explanations and predictions that require reliable mathematical or logical concepts that are reduced to models and parameters. The question of efficiency becomes important; the broader uncertainty may have been reduced, but the process is economically inefficient. The profession is urged to become more frugal or, for example, reduce the variability of the outcomes. Knowledge professions often operate under conditions that require them to acquire knowledge and reduce uncertainty effectively and efficiently. Doing so requires understanding both epistemic uncertainty, as discussed above, and statistical process variation or aleatory variation.
:'''Knowledge professions manage and reduce uncertainty in the form of error and bias in an economically effective and efficient manner. This effort requires understanding both epistemic and aleatory uncertainty.'''
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===Choice and Behavioral Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge, referred to as epistemic uncertainty, as well as the inherent variations in results, or what was termed aleatory uncertainty. Acting in the face of that epistemic and aleatory uncertainty introduces yet another uncertainty into the process, namely choice uncertainty. This uncertainty reflects uncertainties associated with first-person and third-person choice. This is particularly relevant for engineering with project stakeholders.
===Summary of Semantics Framework for Uncertainty===
Professional disciplines have gradually re-framed their understanding of uncertainty and, thru experience and analysis, have arrived at the following meta-knowledge concepts of uncertainty:
* '''Uncertainty and its proxy, error, have three components: [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic epistemic uncertainty], [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic aleatory uncertainty], and behavioral uncertainty.'''
* '''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty using non-dogmatic beliefs, which recognize the existence of specific criteria and information that would make the believer change their beliefs using coherent approaches such as probability.'''
* '''Knowledge professions develop knowledge of how uncertainty and its proxy, error, propagate through an ordered and normative series of observations or choices that meet testability criteria in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error. An error can then be described, explained, and reduced in ways that uncertainty cannot. This allows the professions to prioritize error reduction to achieve an optimized reduction of uncertainty and produce a "legitimacy" from the fruitfulness of its use.'''
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===Uncertainty versus Risk/Hazard===
Up to this point, nothing has been said about risk or hazard. Now, risk can be defined. Simply put risk or hazard are finite, logically distinct subsets of the broader uncertainty. Risk and hazard are currently defined interchangeably. First and foremost, risk is a specific subset of general uncertainty. Any arbitrary subset of general uncertainty can be defined as a risk if it meets the following criteria:
* Uncertainty is associated with the potential for material error when acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena.
* Uncertainty and its proxy, error, can be partitioned into logically distinct subsets associated with multiple-step execution or choice frameworks in physical environments linked to a unique or limited range of sequences.
**Subcriteria 1:Resulting errors will have varying impacts or consequences depending on where the error occurs and the degree of causal linkage or influence in the chain.
**Subcriteria 2:Potential errors can be sequenced and prioritized to achieve an optimized reduction of error or bias and, by proxy, uncertainty over time.
* Uncertainty and its proxies, error bias, and environments are isomorphic to coherent approaches such as probability.
* Uncertainty and its proxies, error, and bias are isomorphic to coherent approaches such as knowledge models capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria in 'reasoning under uncertainty.'
This is a definition of general risk. There is no differentiation for engineering or economics, medicine or insurance. There is no difference between a 'good' risk consequence and a 'negative' one. Redefining general risk into simpler terms gives the following:
:'''General Risk is a subset of general uncertainty that is associated with the potential for material errors and biases when sequentially acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena; isomorphic to probability measures and methods and capable of testability.'''
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==Logical Framework for Specific Risk Taxonomies==
As noted above, at the most fundamental level, uncertainty is ".....the subjective perception of ignorance." [13] [Note 6] At this point in the discussion, the focus has moved from discussing uncertainty to its framed subset, risk.
:'''Further, the discussion moves from general risk to a professional or discipline-specific risk [https://en.wikipedia.org/wiki/Taxonomy taxonomy]. Beyond this point, risk will be used in place of uncertainty.'''
Han et al. note that, in general, "(t) taxonomies are valuable not only in their comprehensiveness but in their coherent reduction of uncertainty to conceptually discrete elements."
Uncertainty (and, by implication, risk or hazard) taxonomies offer an approach or tool to more precisely identify and define ontological uncertainty so that it can be quantified, analyzed, and communicated. Viewed this way, uncertainty is not a single, monolithic phenomenon but "...multi-dimensional with theoretically distinct domains and constructs that are potentially measurable and related to different outcomes, mechanisms of action, and management strategies." [14] Taxonomic schemes for classifying the different kinds of scientific and, by extension, engineering uncertainty require identifying the various kinds of potential error associated with descriptive scientific or engineering information and knowledge. An example of this is the following quote from a legal authority:
:"I would especially stress the need for an agency to disclose the uncertainty that surrounds its determinations. '''And by uncertainty, I mean the agency's ignorance as well as its quantitative estimates of error."''' (Emphasis added) [15]
'''Given the necessity of acting in the face of enormous uncertainties [16], discipline taxonomies must offer a clearly coherent and probabilistic approach to uncertainty that is sufficiently general in nature, logically distinct and exhaustive in scope and "...provide decision-makers with a foundation for understanding the nature of discipline information..."''' [17]
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=== Scientific Risk Taxonomies ===
The [https://en.wikipedia.org/wiki/Scientific_method scientific method] is a body of techniques for investigating phenomena in its environment, acquiring new knowledge, or correcting and integrating previous knowledge. Inherent in that process are uncertainties of many forms. Any one taxonomy of [https://en.wikipedia.org/wiki/Uncertainty scientific uncertainty] has largely been focused on statistical models used to assess and quantify sampling, errors, and parameters, although several different taxonomies are conceivable: note-24 [18]
====Environment-specific, Phenomena oriented, model-centric taxonomy of uncertainties====
** [https://en.wikipedia.org/wiki/Uncertainty_quantification#Sources_of_uncertainty '''Parameter uncertainty'''] the source of which is model parameters that are inputs to the computer model (mathematical model) but whose exact values are unknown and cannot be controlled, or whose values cannot be exactly inferred by statistical methods. Subsets of this type of uncertainty include but are not limited to:
***'''Experimental uncertainty''' is also known as observation error, which comes from the variability of experimental measurements. An example is repeating an experiment measurement several times using exactly the same settings for all inputs/variables and recording the variability. It is a subset of the larger uncertainty component, parameter uncertainty.
*** '''Parametric variability''' comes from the variability of the input variables of the phenomena model and is also a subset of the larger uncertainty component, parameter uncertainty.
** '''Structural uncertainty''', or model inadequacy, model [https://en.wikipedia.org/wiki/bias bias] or "[https://en.wikipedia.org/wiki/Systemic_bias systemic bias]", or model discrepancy, which comes from the lack of knowledge of the underlying true state of the phenomenon or its environment. It depends on how accurately a mathematical model describes the true state of the phenomenon or what is termed "model fitness". Due to the inherent nature of uncertainty in any knowledge body (scientific or engineering), models are only an approximation to reality. Therefore, any conclusions drawn from the model can be very misleading when the underlying basis is not plausible or lacks validity. '''note-25 [19]]'''
*** What is missing in this context is any concept of output variability due to the model itself. Part of this is due to the lack of recognition of "process" in scientific investigation. The concept of the scientific method started with a single investigator, such as Galileo or Michael Faraday, working in their laboratories. It has grown into planet and solar system scale experiments such as data gathered on planetary flybys such as Mars, Pluto, and Ceres to the hunt for the Higgs Boson. Ultimately, science has become more like engineering in the scale and complexity of its investigations and theory aggregates, such as the "Unified theory" of particle physics. Subsets of this type of uncertainty include but are not limited to:
*** '''Algorithmic uncertainty''', or numerical uncertainty, comes from numerical errors and numerical approximations per implementation of the computer model. Most models are too complicated to solve exactly. For example, the [https://en.wikipedia.org/wiki/Finite_element_method finite element method] or [https://en.wikipedia.org/wiki/Finite_difference_method finite difference method] [may be used to approximate a solution [https://en.wikipedia.org/wiki/Partial_differential_equation partial differential equation], which introduces numerical errors. Other examples are numerical integration and infinite sum truncation, which are necessary approximations in numerical implementation.
*** '''Interpolation uncertainty''' comes from a lack of available data collected from computer model simulations or experimental measurements. For other input settings that don't have simulation data or experimental measurements, one must interpolate or extrapolate to predict the corresponding responses.
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====Commentary====
A more comprehensive taxonomy of uncertainties that acknowledges knowledge, data, and linguistic uncertainties is:
*'''Epistemic uncertainty''' -[https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Epistemic uncertainty] or systematic uncertainty, in contrast, reflects limitations in the current “state of knowledge” underlying models themselves, originates from competing theories or models, is not readily quantifiable, and is manifest by subjective confusion or indecision. It includes uncertainty due to measurement limitations, insufficient data, extrapolations and interpolations, and variability over time and space. Epistemic uncertainty is uncertainty about "...some determinate fact ... because of a lack of complete information. '''cite_note-26 [20]]''' Epistemic uncertainty can be classified into six main types:'''cite_note-27 [21]]'''
*''' Measurement error''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Observational_error Measurement error] is uncertainty that manifests itself as (apparently) random variation in the measurement of a quantity. Repeated measurements will vary and demonstrate classic statistical behavior.
* '''Systematic error''' - [https://en.wikipedia.org/wiki/Observational_error Systematic error] occurs due to bias in the measuring equipment, model, analysis, observation, or sampling procedure. It is formally defined as the difference between the true value of the quantity of interest and the value to which the mean of the results converges as sample or data sizes increase. Unlike measurement error, it is not (apparently) random, and therefore, results subject to systematic error alone do not vary about a true value. Systematic error can result from deliberate judgment to exclude (or include) data, parameters, or models that ought not to be excluded (or included).
** "The only way to deal with systematic error is to recognize a bias in the process, model, or procedure and remove it. Systematic error, however, is notoriously difficult to recognize except on theoretical grounds. Corrections may only be applied when the magnitude and direction of the bias are known. Such corrections underlie the application of double-sampling methods in environmental science." (Regan, et. al., op. cit.)
*'''Natural variation''' - [https://en.wikipedia.org/wiki/Natural_process_variation Natural variation] or underlying process variation occurs in systems that "...change (concerning time, space, or other variables) in ways that are difficult to predict...across the full range of temporal and spatial values (or other related variables)." '''note-28 [22]'''
** This form of uncertainty is reduceable utilizing tools such as [https://en.wikipedia.org/wiki/Statistical_process_control statistical process control] and replication of experiments or observations.
*'''Inherent randomness''' - [https://en.wikipedia.org/wiki/Randomness randomness] or [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty aleatory uncertainty] exists because the system is"...in principle, irreducible to a deterministic one (the most well-known case is described by [https://en.wikipedia.org/wiki/Uncertainty_principle Heisenberg’s uncertainty principle] in quantum mechanics)." '''note-29 [23], note-30 [Note 7]'''
** Even if the argument is accepted that it is unlikely that any given physical system at some level is inherently random, it is important for any taxonomy to distinguish between systems that appear random due to incomplete or inconsistent information and those that are intrinsically random. (Again, see Regan (2002)).
*'''Model uncertainty''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling Model uncertainty] occurs in the process of generating a model as a conceptual representation of a particular phenomenon to create a [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling#Overview simplified reflection of reality]. This selectivity of factors and variables creates uncertainty in at least three ways. (Regan (2002))
** '''Variables and processes''' that are regarded as relevant often represent a trade-off between system knowledge and assumed states and model objectives;
** '''Models''' depict observed processes using logical or mathematical constructs based on underlying theories about system states or dynamics using continuous equations to describe discrete processes.
** '''Curve fitting''' (including interpolation and extrapolation) with mathematical expressions using model variables where inputs are discrete data points.
** Regan (2002) argues that '''model uncertainty''' is "...notoriously difficult to quantify and impossible to eliminate ...(and)...(t)he only reliable way of determining how appropriate a model is for prediction is to perform validation studies."
** Subjective judgment occurs due to the interpretation of scarce or error-prone data.
*** The only way to address this type of uncertainty is to "...assign a degree of belief about an event in the form of a subjective probability." (Regan (2002))
*Linguistic uncertainty - Linguistic uncertainty, or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. '''note-31 [24]], note-32 [25]]'''
** '''Context dependence''' is uncertainty concerning the context in which a statement is to be understood;
** '''Ambiguity''' occurs when words have multiple meanings, and further reduction to a single meaning is not logically possible in a given context.
** '''Underspecificity''' occurs in the presence of "unwanted generality" or multiple interpretations, and reduction to a smaller set or even a single interpretation is not logically possible.
*Stochastic or statistical uncertainty - Stochastic or statistical uncertainty, which is sometimes known as [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Aleatoric uncertainty] as well as the subject of [https://en.wikipedia.org/wiki/Stochastic_control stochastic control] pertains to the parameters of a risk model, originates from sampling or measurement error, and can be quantified and mathematically expressed (e.g., using confidence intervals).
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== Uncertainty Taxonomies in a Legal Environment ==
From a legal perspective, a definition of taxonomy is dictionary-based, such as "the systematic distinguishing, ordering, and naming of type groups within a subject field." given by Walker (op. cit.). Such legal concepts of scientific or engineering taxonomies are not required to be epistemically complete. Still, they must cover "the most significant aspects of ...(current, good discipline practices) ... and of the descriptive information commonly encountered by decision-makers" in a logically distinct manner. (Walker, 1991, p. 571) These taxonomies must be sufficient to catalog the types of uncertainty associated with descriptive assertion, including the critical subset of cause and effect assertions. This legal/decision-maker framework has presented descriptive uncertainty as having six components (Walker, 1991). Although not part of the Walker taxonomy, underlying the scheme is linguistic uncertainty, or "vagueness," as a source of uncertainty, including uncertainties due to context dependence, ambiguity, and under-specificity. '''note-33 [26]]''' Arguably, these uncertainties underlie the most basic element in the taxonomy.
* '''Linguistic '''- or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. (Regan, 2002)
* '''Conceptual'''- the definition by choice and design of descriptive concepts or variables to be used as [https://en.wikipedia.org/wiki/Predicate_(grammar) predicates] where the subject of an assertion identifies what is being discussed and the predicate provides information about the topic or characterizations such as what the subject is, what the subject is doing, or what the subject is like (Wikipedia).
* "Conceptual uncertainty, or the potential for conceptual error, arises whenever predication occurs. Whenever a concept is used to describe something, using certain concepts instead of others begins to structure how we understand the object, event, or instance under discussion. Predication or conceptualization generates useful information about things, but it also can inhibit our ability to think about those things with concepts other than those selected. The concepts used may not be the most fruitful or the best designed- either for scientific purposes or for making wise, fair, effective, and efficient decisions." (Edited for reading, Walker, 1991)
* Similarly, choice and design of descriptive variables enhance and restrict data set membership, creating potentially incomplete, incompatible, or inconsistent data sets, "...the classification categories employed, and the relationships among those categories (nominal, ordinal or scalar)." '''note-34 [27]]'''
* Concept uncertainty '''note-35 [28]''' like systematic error discussed above is notoriously difficult to recognize, except on theoretical grounds, and the magnitude and direction of the bias are known.
Both terms could be analogized to that of [https://en.wikipedia.org/wiki/Accuracy_and_precision#Terminology_of_ISO_5725 accuracy] in [https://www.bipm.org/en/publications/guides/ ISO 5725] which looks at the ability of the concept or system's ability to produce results that are proximate to the "true" value or state of the phenomenon. Conceptual uncertainty and systemic bias are related to another measure of uncertainty: validity. Walker has defined validity as the ability of a concept to quantitatively describe and explain what the phenomenon objective "truly" is. Walker also ties the concepts together when he states, "Validity concerns the "accuracy" of the measurement data, not its precision." (Walker, 1998) 
Additionally, conceptual uncertainty and associated validity issues could be propagated throughout this taxonomy. Every component of action and decision in the face of uncertainty raises validity issues. Walker recognized this when the author defined epistemic choice for concepts used throughout the other uncertainty components or layers.
* '''Measurement''' uncertainty or Misclassification error- the application of the underlying concepts or variables to specific, individual cases and the uncertainty of the reliability (Walker, 1998) of the resultant data. (It is important to remember that assessment observation and measurement are used interchangeably in this analysis.) Thus, an assessment method or procedure is considered "reliable" or "precise" in the scientific terms of ISO 5715 if it repeatedly produces consistent results.
* Measurement uncertainty is logically distinct from conceptual uncertainty, which can only occur after any conceptual uncertainty has been established. Still, misclassification can occur even when the conceptual uncertainty has been minimized. Conversely, underlying errors or errors made in measurement or classification propagate thru the reasoning chain and reduce the quality or value of the final result.
* While uncertainties associated with quantitative measurements or assessments can be statistically evaluated and described, uncertainties related to qualitative assessments are more problematic.
**Working through the taxonomic chain from base concept to causal explanation requires amassing and integrating qualitative information and quantitative data into input parameters for mathematical models. Developing procedures for producing consistent, repeatable sets of information from assessments that minimize conceptual error or systemic bias requires professional efforts to "validate" such methods, models, and judgments at all layers in the process. Validation as a process is something that civil engineering as a practice needs to embrace more frequently.
* '''[https://en.wikipedia.org/wiki/Sampling Sampling]'''- in the classical statistics sense, is the selection of a limited subset of a larger population to use as a basis for making estimates about the population in the form of attributes, proprieties, or parameters. The resulting information would be determinative in making forecasts about future population sampling or possessing a desired amount of predictive power. The uncertainty about the ability of this information to correctly predict future samples is termed sampling error.
* '''Modeling'''- Modeling uncertainty arises whenever a claim is made that out of a class of candidate relationships and variable pairings, one variable pairing inclusive of constants is chosen that possesses a particular or persistent mathematical relationship to another variable X. Modeling errors arise in selecting the wrong variable pair and constants or by incorrectly specifying its constants. (Walker, 1991, pg. 599)
** '''Causal'''- In causal modeling and analysis, the relationship between causal variables is not strictly a mathematical function. Still, it makes testable predictions and explains "...how a system of variables works, why a system works the way it does, or why it makes sense to think of certain variables as a system" at all." (Walker, 1991, pg. 609)
** '''Epistemic'''- the choice of interpretations for fundamental, logical concepts used throughout the other components or layers.
The components of this uncertainty taxonomy can combine in different ways to "...produce different aggregate uncertainties." '''note-36 [29]'''
:''' Only the scientific and engineering professions possess the capacity to develop and validate procedures and models that combine the different kinds of qualitative '''note-37 [30]''' and quantitative information and their associated potential for error into an aggregate measure or "scalar variables" of uncertainty.'''note-38 [31]]
Scalar variables are quantitative variables whose categories are related by some measure of the relevant property's incremental frequency, degree, or amount. (Walker, 1991) Examples are Modulus of Elasticity, Compression stress in elastic materials, etc.

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=== Economics Uncertainty Taxonomies ===
Like scientific concepts of uncertainty, economics is environment-specific (in this case, read sector of economy or markets), phenomenon-oriented (in this case, read human activity and manufactured phenomena such as firms and markets guided by human judgment and decision), and model-centric.
:''' Economics is not interested in all forms of uncertainty, only specific, meaningful subsets possessing casual connections with material impacts, often labeled as 'risks'.'''
One of its earlier writers ([https://en.wikipedia.org/wiki/Frank_Knight#Life_and_career Knight], 1921, 1948, 1957) attempted to define "uncertainty" as the presence of "defects of managerial knowledge" (or [https://en.wikipedia.org/wiki/Knightian_uncertainty knightian uncertainty] and thus risk centric) as risk that is immeasurable, not possible to calculate.(Wikipedia) Risk in this sense was defined as "the ordinary risks of business activity which can ... be reduced... by applying the insurance principle." Knight does acknowledge that risk is a specific and unambiguous subset of uncertainty. (Risk, Uncertainty and Profit, 1957, pg. 19) For Knight, measuring uncertainty meant measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences, which Knight referred to as "the imperfection of knowledge". (Risk, Uncertainty and Profit, 1957, pg. 197) One of Knight's many contributions to this analysis was recognizing information and knowledge's role in economic activity.
:"''(W)e are concerned only to emphasize the fact that '''knowledge is in a sense variable in degree and that the practical problem may relate to the degree of knowledge rather than to its presence or absence in toto.''' We live only by knowing something about the future, while the problems of life, or conduct at least, arise from knowing so little. This is as true of business as of other spheres of activity. The situation's essence is action according to opinion, of greater or less foundation and value, neither entire ignorance nor complete and perfect information, but partial knowledge. If we are to understand the workings of the economic system, we must examine the meaning and significance of uncertainty; and to this end, some inquiry into the nature and function of knowledge itself is necessary.''" (Knight, pg. 199) Emphasis added
Knight also reinforced the difficulties in classifying uncertainties when he analyzed life insurance. In this case, accidental death was an uncertain phenomenon compared to sickness and accident, where an "...objective description and classification of cases was impossible..." (Knight, pg. 248) Another factor that made uncertainties ineligible as risks and, therefore, uninsurable because they were unclassifiable was the exercise of judgment in making decisions by the businessman. (Knight, pg. 251)
This is a simple classification of economic uncertainty and, as an idealization, is philosophically controversial. '''note-39 [32]'''
* Uncertainty and information about the economic environment are distinct from uncertainty about others’ behavior or choices.
* Risk as a specific subset of uncertainty implies that "...all possible acts are known, all possible outcomes arising from each act are known, and it is possible to assign probabilities to each act." '''note-40 [33]'''
Like the economic [https://en.wikipedia.org/wiki/Theory_of_the_firm theory of the firm], an economic theory of uncertainty seeks to explain and predict the nature of a specific subset of uncertainty (in this case, risk relating to physical phenomena and human activity), including its behavior, structure, and relationship to the universal uncertainty of physical phenomena. In this sense, uncertainty, like firms and markets, can establish price equilibriums under the right circumstances different from those that might have occurred in either of the other two. Such a theory of uncertainty offers a partitioning scheme that decomposes uncertainty into distinct and meaningful subsets, of which risk is a significant one. It also explains how the presence and variability of knowledge cause or " drive" economic consequences. Unlike physical phenomena, economic activity creates artificial objects such as products, firms, mediums of exchange, markets, economies, and knowledge about those activities. The economics of uncertainty exists as an alternative to "certainty" models that assume away imperfect knowledge and unknown choice/judgment preferences when it is more efficient for economic decision-making. This also allows the introduction of the logical concept of "hazard" versus "risk". Frank used hazard extensively in Risk, Uncertainty, and Profit, but not to the extent that one could say it was interchangeable. An example of this is in the term "[https://en.wikipedia.org/wiki/Moral_hazard Moral hazard]," where a firm could engage in "riskier" and potentially "uninsurable" behavior if the information were transparent, and, therefore, insurance coverage of losses is limited. note-41 [34]], note-42 [35]]

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=== Uncertainty and Risk in Policy and Regulatory Environment ===
:''"Hume's 'just reasoner', when faced with a difficult public policy decision in current times, would probably commission a risk analysis. But how could they incorporate 'a degree of doubt, caution, and modesty?"'' '''note-43 [36]'''
The problem of "decision-making in the face of uncertainty" is how to regulate based on incomplete information that has the potential to be materially inaccurate. A contextual assumption is that we need to evaluate decision rules for dealing with uncertainty. As a social enterprise, risk regulation, whatever its substantive objectives, should be as '''effective, efficient, and equitable''' as practically possible. These "three E's" form a set of "process objectives" or "meta-goals." From the uncertainty standpoint, causal information can be usefully divided into two major categories: information about groups and information about individuals.

Each category, group, and individual has its distinctive types of inherent uncertainty. These are logically distinct, generally independent, and cumulative, contributing to '''(?????)'''

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=== Engineering Uncertainty Taxonomies ===
Like scientific and economic concepts of uncertainty, engineering is environment-specific, phenomena-oriented, model-centric, external choice, and driven by decision-making in the face of uncertainty. Like economics and unlike science, engineering is not interested in all forms of uncertainty, only those with determinable and material consequences. Like economics, engineering conceives risk as a specific and unambiguous subset of uncertainty determined by measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences. Also, engineering recognizes the role information and, by extension, knowledge plays in constructing the built environment.
'''note-44 [37]]''' Like economics, engineering differs from economics and scientific uncertainty in its interest in behavioral uncertainty.
*"Behavioral or interaction uncertainty is how individuals or organizations act or interact. Behavioral uncertainty arises from four sources: design uncertainty, requirement uncertainty, volitional uncertainty, and human errors." '''note-45 [38]]'''
** A design uncertainty is a choice among alternatives over which an individual or group of individuals exercises direct control but has not yet decided upon.
** Requirement uncertainty includes parameters of interest to and determined by the stakeholder, independent of the engineer or designer.
** Volitional uncertainty is uncertainty about what the subject him/herself will decide. Other people’s future actions and conduct are not entirely predictable, particularly when dealing with other organizations.
** Human errors occur during the development of a system or project due to blunders or mistakes by an individual or individuals.

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==Practice Frameworks for Uncertainty and Risk in Civil Engineering Practice==
===PMIBoK===
PMI does not define uncertainty, but the PMI Body of Knowledge uses the term in 45 places (uncertainty) and as describing properties in 8 places (uncertainties). [See Note 8] PMI does define "risk" and uses the term over 1,300 times in the same document. In its glossary, PMI also defines terms such as "threat" and "opportunity" but not events.[391 The BoK contextually defines uncertainty when it states that project risk has its origins in the uncertainty present in all projects. (Op. Cit., pg 309) 
'''Uncertainty''' is contextually defined and taken as having the property of affecting project execution and the ability to meet stakeholder expectations. Uncertainty is more than the sum of all individual risks (known and unknown). The nature of this uncertainty is not defined, only its capacity to affect something else or its properties. 
'''Risk''' is defined as " ... an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives." (Ibid.) Beyond this formal definition, PMI adds context when, in its risk management section (Sec. 11 ), the BoK states that risk is a caused event or condition with multiple causes and impacts. The BoK even offers specific techniques for mapping cause-and-effect relationships and influence diagrams. (Sec. 11.2.2.5) Not as evident but equally important is the recognition that not only is risk caused but the impact is "triggered". The risk trigger is an event or situation that signals that it is about to occur (Glossary, pg. 566). Risk conditions are factors that affect or otherwise contribute to risk, such as project stakeholders. Risk implicitly retains some residual element of probability in it, or a value of less than 1.0 with risk that approaches a level of near certainty is termed "issues" or "realized risk" .(Op. Cit., pg 309). PMI also links risk to underlying variations in project outcomes. (Ibid.) Risk is based upon data and information which can be assessed as to its quality (Sec. 11.3.2.3). This analysis looks to determine the value of the information and examines the degree to which the risk information is understood as well as its " ... accuracy, quality, reliability and integrity ... " (Ibid.) Part of that is understanding the relationship between occurrence and impact as outlined in PMl's Figure 11-10 Risk Impact Matrix and developing a model for prioritizing risk products and setting the threshold for action.
====Commentary:====
For defining and identifying risk, PMI notes in Sec. 11.2.2.4 that " ... Every project and its plan is conceived and developed based on a set of hypotheses, scenarios, or assumptions." Part of the risk analysis process is identifying risks to the project, such as inaccuracy, instability, inconsistency, or incompleteness of assumptions. Similarly, the quality of management plans, as well as their consistency with others and the project objectives and assumptions are 11 •• .indicators of risk in the project." (Sec. 11.2.2.1) 
Similarly, the role of engineering economics in risk is embedded or implied in the PMI definition of risk when the BoK discusses risk identification as a process of assessing which risks out of the total risk exposure for the project "may affect" the project and distilling their characteristics. Embedded in this statement is the concept that not all risks can cause economic impacts (positive and negative) to the project. Also embedded is the requirement to understand risk characteristics to identify project risk (Cost and schedule). Lastly, risk identification is an iterative process of incrementing project risk documentation, much like project scope, cost, and schedule documentation. 
Lastly, PMI acknowledges the complex constitution of risk aggregates or the "sum" of all risks in its material, but is it a sum? Risk information has descriptive and explanatory content. These are knowledge and meta-knowledge components. Understanding project assumptions is project-specific knowledge, but identifying risks from assumption inaccuracy, instability, inconsistency, or incompleteness requires professional or program meta-knowledge. 
'''Uncertainty exists in all projects, but risk, a subset of uncertainty with material economic impacts, is a key meta-knowledge element for civil engineering practice.'''
===Software Development Process- Risk Focused===
The Unified Process requires the project team to focus on addressing the most critical risks early in the project life
cycle. The deliverables of each iteration, especially in the Elaboration phase, must be selected to ensure that the greatest risks are addressed first. <ref> Booch, Grady, Ivar Jacobson, and James Rumbaugh. "The unified software development process."
Reading: Addison Wesley (1999).</ref>
===American Society of Civil Engineers (ASCE)===
ASCE does not define uncertainty, but the ASCE CE Body of Knowledge (CEBoK) uses the term in 22 places (uncertainty) and describes properties in 13 places (uncertainties). 
The CEBoK does not use the phrase "uncertainty and risk" as PMI and AACE do in their publication. ASCE reverses the two (Risk and uncertainty) in twelve places regarding technical outcomes (Outcome 12). A fundamental difference between PMI and ISO is that risk is considered a subset of uncertainty. ASCE does not define "risk" but uses the term 25 times in the same document. Almost all are in the context of variation of design parameters and not in the classical context of cost, schedule uncertainty, and risk.
====Commentary:====
...
=== American Association of Cost Engineers (AACE) ===
AACE defined uncertainty, risk, and other related terms in its Risk Management Dictionary. <ref>Risk Management Committee uRisk Management Dictionary, Cost Engineering Vol. 37/No. 10, OCTOBER 1995. </ref>
AACE's overall strategy was to define risk and other terms that stem from base uncertainty. AACE first defined uncertainty as .... "(t)he total range of events that may happen and produce risks (including both threats and opportunities) affecting a project (see opportunities, events, conditions, risk, and threats" where the following sub-definitions apply:
** Biases--A lack of objectivity based on the individual's position or perspective. Systematic and predictable relationships between a person's opinion or statement and his/her underlying knowledge or circumstances. Note: there may be "system biases" as well as "individual biases."
** Condition (Uncertain Condition)-Any specific identifiable circumstance (such as the rate of inflation or the quality of labor available) that might affect the outcome of the project
** Event (Uncertain Condition)-is a specific identifiable action (such as a large government project being started in the same labor area as your project) or an act of nature that might happen and that (if it does happen) could affect the outcome of the project.
** Opportunities are Uncertain events that could improve the results or improve the probability that the desired outcome will happen
** Threats are Uncertain events that are potentially negative or reduce the probability that the desired outcome will happen.
*** AACE defines Risk as an "ambiguous term" (sic) that is synonymous with uncertainty or a negative subset such as "threats", or an overall negative impact of all possible uncertainties.
====Commentary:====
...
==Limitations of the definition==
...
==Working definition==
...
==Beneficial outcomes==
...
==See also==
Wiki articles on the project.

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==Notes==
<references group="note" />

# See McDaniels et al. 2004 for an extensive discussion and bibliography on the historical development of Risk Analysis and some key milestones in the 20th century.
# Open item
# The Merriam-Webster dictionary defines uncertainty:
::"the quality or state of being uncertain," which is a circular definition. Likewise, one could define uncertainty as the state of not being certain. Synonyms are distrust, doubt, misgiving, mistrust, reservation, skepticism, and suspicion. Another writer added indefinite, indeterminate, not certain to occur, problematical, unreliable, untrustworthy, unknown beyond doubt, dubious, doubtful, not clearly identified or defined, not constant, variable, and fitful to the list.
::Han, Paul KJ, William MP Klein, and Neeraj K. Arora. "Varieties of Uncertainty in Health Care: A Conceptual Taxonomy." Medical Decision Making 31.6, (2011 ): 828-838.
::: Han, ct. al. noted that any definition of uncertainty encompasses " ... numerous types, sources, and manifestations of uncertainty, and ... (any) ... useful working definition of uncertainty needs to specify the concept underlying these varied meanings of the term." (Ibid.)
<ol start="4">
<li> Implicit in this definition of uncertainty as a "state of' is ... </li>
: " ... a conceptualization of uncertainty as a subjective, cognitive experience of people--a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one's lack of knowledge, without which one could not feel uncertain; '''uncertainty is a form of "meta-cognition" ... ( or alternatively, its main component, meta-knowledge) ... -a knowing about knowing.'''" (Han, 2011, op. cit., Emphasis added)
</ol>
<ol start="5">
<li> Cox wanted his system to satisfy the following conditions:</li>
: Divisibility and comparability- The plausibility of a statement is a real number and depends on the information we have related to the statement.
: Common sense - Plausibilities should vary sensibly with the assessment of plausibilities in the model.
: Consistency - If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). -preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf
</ol>
<ol start="6">
<li> The utility of existing taxonomies of uncertainty in civil engineering and, by extension, "risk" has been unsatisfactory for this very reason. This is particularly true in cases where the source of the uncertainty is conflicting versus incomplete information. See Regan, et. al.,"A taxonomy and treatment of uncertainty for ecology and conservation biology" (2002)
for a survey of scientific taxonomies for uncertainty.</li>
<li> Regan et al. argue that " ... genuine examples of this kind of uncertainty are hard to find. Even classic cases of random experiments like coin tosses and the throwing of dice are deterministic; it is just that we do not have enough information about the dynamic processes and initial conditions to make any sensible estimates about the outcomes. Such processes are, for all intents and purposes, inherently random, but they are not genuinely inherently random. For similar reasons, complex systems such as ecosystems and weather patterns are unlikely to be inherently random. Similarly, chaotic systems are entirely deterministic. They are unpredictable because the deterministic processes generating them and the relevant initial conditions are hard
to fully specify (see Stewart 1989, Sugihara et al. 1990)." (Regan (2002) </li>
<li> The CEBoK uses the phrase "uncertainty and risk" twice in reference to scheduling (Section 6.5,2) and cost estimating (Section 7.2,2) as well as reversing the two (Risk and Uncertainty) also in two places in discussing project life cycle (both in Section 2.4.1) </li>
</ol>

==References==
<references />

Uncertainty and Risk in Civil Engineering Practice

2024-11-24T16:16:01Z

WikiSysop:

'''Life is short and the art long; the occasion instant, experiment perilous, decision difficult.''' (Hippocrates as quoted in Fox,)
:''See also [[Project Definition]] , [[Project Life Cycle and Phase Models]] , [[Project Delivery Methods]]''
:''See also [[Technical Outcomes for Civil Engineering:Risk and Uncertainty]]''
==Basic Considerations==
As human beings,'' ... being alive means seeking opportunities and taking risks.'' As knowledge professionals
living in the 21st century, this means coping with an increasingly complex number of uncertainties for humans
living in this environment. We seek to understand better how these uncertainties can be characterized and
managed. The essence of this article and the experience for engineers in general and civil engineers in particular is
that manageable uncertainty is, by definition, termed risk, and its kindred cousin is termed hazard. This causes us to experience the
:" ''... human dread of and fascination for risk and the increasingly important role of risk analysis within societies ...'' " <ref name="McDaniels"> McDaniels, Timothy, and Mitchell Small. Risk analysis and society: an interdisciplinary characterization of the field. Cambridge University Press, 2004, page 1 </ref> (Ibid.)
and, by extension, civil engineering. McDaniels et al. argue that risk management has been
fundamental to our social and governance development for the past 10,000 years. The scale and shape of the
uncertainties faced in this period shaped the societies that have developed today. The central thrust of this effort
over the centuries has been to reshape and re-frame our understanding and conception of uncertainty from one of
complete unknowing and simple acceptance as our fate in life to one of management (cf "''Against the Gods''"
concept in Bernstein's book <ref name = Bernstein">Bernstein, Peter L., and Jesse Boggs. Against the gods. Simon & Schuster 1997.</ref>.
: ''All the knowledge professions and disciplines have struggled with managing uncertainty, for it is impossible to manage unbounded uncertainty.'' <ref group="note">See McDaniels et al. 2004 for an extensive discussion and bibliography on the historical development of Risk Analysis and some key milestones in risk analysis in the 20th century.</ref>
As outlined below, professions such as civil engineering have successfully acted in the face of uncertainty. It lies in the profession's ability to reduce a wide variety of uncertainties into increasingly smaller and crucially bounded subsets that can be managed. These are called 'risks. More recently, this process has evolved, and individual disciplines such as Civil Engineering (CE) have developed their knowledge and models to perform risk analysis. Risk is also taught as a distinct discipline and specialty practice within civil engineering. Still, it has some unique features that set it apart from other more classical practices within CE. As such, it is one of the first of some very specialized CE practices that use knowledge about civil engineering knowledge, or meta-knowledge. Other examples of civil engineering metaknowledge are project controls and quality controls.
==Semantic, Epistemic and Logical frameworks==
The semantic, epistemic, and logical frameworks for uncertainty and risk have several dimensions and layers of logical frameworks. The semantics problems include interchangeable usages for risk and uncertainty and hazard, uncertain and imperfect, and a lack of definitional material or context. The semantical scheme for this article will be to proceed from the distinction between certainty and uncertainty through marginal refinements and reductions of uncertainty up to the point of causal uncertainties. Beyond this point of semantics will be the logic frameworks or the subject of taxonomies of professional knowledge. First, it is about simple, testable phenomena, and then, it moves on to complex taxonomy schemes for artificially constructed phenomena or engineering projects.
<ref group="note">TBD</ref>
==Semantics framework==
===Certainty and Uncertainty===
''''' There is no such thing as absolute certainty, but there is assurance sufficient for the purposes of human life.''''' ([https://en.wikipedia.org/wiki/John_Stuart_Mill John Stuart Mill]) 
'''''If you tried to doubt everything, you would not get as far as doubting anything. The game of doubting itself presupposes certainty.''''' ([https://en.wikipedia.org/wiki/Ludwig_Wittgenstein Wittgenstein] # 115 from On [https://en.wikipedia.org/wiki/On_Certainty Certainty]) 
It is important to note that the references to epistemic or epidemiological knowledge in this article are assumed to relate to three forms of knowledge, namely:
* Knowledge that ( [https://en.wikipedia.org/wiki/Descriptive_knowledge descriptive or declarative or propositional knowledge])
* Knowledge how ( or "[https://en.wikipedia.org/wiki/Procedural_knowledge know-how]"), and
* [https://en.wikipedia.org/wiki/Knowledge_by_acquaintance Knowledge by acquaintance].
'''Certainty''' has been defined as "an epistemic property" of knowledge in all its forms and the state of our beliefs about that knowledge.
<ref>Certainty, Stanford Encyclopedia of Philosophy, accessed on September 12, 2015, at http://plato.stanford.edu/entries/certainty/#ConCer </ref>
Certainty about any belief about knowledge implies that it is not subject to doubt or skepticism. This immunity to criticism can be dogmatically based, emphasizing the importance of a propositional-based sense of truth over experiential, sensory perceptions. <ref>Definition of [https://en.wikipedia.org/wiki/Dogmatic_theology Dogmatic theology or belief], accessed at Wikipedia</ref>
: '''The demarcation line between dogmatic and non-dogmatic beliefs lies in the presence and recognition of specific criteria and information that would make the believer change their beliefs.'''
An empirical framework of [https://en.wikipedia.org/wiki/Testability testability] and [https://en.wikipedia.org/wiki/Falsifiability falsification] is required to recognize this and limit dogmatism. For example, one could argue that people would change their minds if God asked them to. Similarly, one could construct a concept of epistemic as opposed to dogmatic belief certainty as the ability to know anything that one chooses to know and can be known or [https://en.wikipedia.org/wiki/Omniscience inherent omniscience].

A second kind of certainty is epistemic, when conviction reflects the highest possible support for a belief. In this sense, knowledge is separate from beliefs, although someone may have beliefs about a property, such as certainty of knowledge. Logically, it has been shown that there will be unprovable statements within the system for any such system of knowledge. Secondly, the knowledge system cannot demonstrate its own consistency. ([https://en.wikipedia.org/wiki/Gödel%27s_incompleteness_theorems Gödel's incompleteness theorems])
: '''Certainty, in real life, is useless or often damaging (the idea is that "total security from error" is impossible in practice, and a complete "lack of doubt" is undesirable)''' ([[https://en.wikipedia.org/wiki/Certainty Physicist Carlo Rovelli]])
'''Uncertainty''', on the other hand, arises immediately in the slightest amount of doubt or criticism.
<ref name="note">The Merriam-Webster dictionary defines uncertainty as “the quality or state of being uncertain” which is something of a circular definition. Likewise, one could define uncertainty as the state of not being certain.

Synonyms are distrust, doubt, misgiving, mistrust, reservation, skepticism, and suspicion. Another writer added indefinite, indeterminate, not certain to occur, problematical, not reliable, untrustworthy, not known beyond doubt, dubious, doubtful, not clearly identified or defined, not constant, variable, and fitful to the list.

Han, Paul KJ, William MP Klein, and Neeraj K. Arora. "Varieties of Uncertainty in Health Care A Conceptual Taxonomy." Medical Decision Making 31.6 (2011): 828-838.
:Han, et. al. noted that any definition of uncertainty clearly encompasses "...numerous types, sources, and manifestations of uncertainty, and ...(any) ...useful working definition of uncertainty needs to specify the concept underlying these varied meanings of the term." (Ibid.)</ref>
↑ Implicit in this definition of uncertainty as a “state of” is ...
"...a conceptualization of uncertainty as a subjective, cognitive experience of people—a state of mind rather than a feature of the objective world. The defining feature of this state, furthermore, appears to be lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one’s lack of knowledge, without which one could not feel uncertain; uncertainty is a form of “meta-cognition” ...(or alternatively, its main component, meta-knowledge)... —a knowing about knowing." (Han, 2011, op. cit.,Emphasis added)</ref>[Note 3]</ref>
Similarly, a general definition of uncertainty could be given as "...any departure from the unachievable goal of complete [https://en.wikipedia.org/wiki/Determinism determinism]." <ref name="Reference5">[5]</ref> Reasoning under uncertainty is very different than performing the same under certainty. In reasoning under certainty, one has complete knowledge and deduces without doubt and equally important, without limitation, thereby concluding free from error. Reasoning under uncertainty, one works in a state of incomplete, inconsistent, and limited knowledge; doubts cloud any statements or assertions and thereby taint any deductions/inferences resulting in the potential for error. <ref name="Note4">[Note 4]</ref>
:'''Knowledge professions reason in a state of incomplete, inconsistent and limited knowledge with doubts that cloud any statements or assertions and taint any deductions/inferences resulting in the potential for error.'''
:''' It is also important to note the semantical and logical correlation between 'reasoning under uncertainty' or 'acting in the face of uncertainty' with the 'potential for' or 'presence of' error. The presence of uncertainty is invariably linked to the potential presence of error.''' The [https://en.wikipedia.org/wiki/Contingency_(philosophy) contingent] nature of uncertainty logically implies the contingent nature of error.
:'''Knowledge Professions use error as a proxy for uncertainty such that within discipline knowledge frameworks, uncertainty can be managed.''' The rationale for this belief is that error can be reliably described, quantified, explained, and ultimately reduced in ways that uncertainty cannot.

'''Uncertainty''' implies a range of variation that is impossible in certainty. Concepts and definitions of uncertainty are unbounded and vast but start from the point of complete and total ignorance. In describing uncertainty, statements can range from complete ignorance to relatively high degrees of belief that there is less potential for error in our reasoning or action. The latter is based upon confidence in justified knowledge, favorable past experience, and reliable parameters/models. One can't make that statement about ranges in certainty. If certainty, by definition, precludes doubt of any type or nature, then how could that be graduated to any degree? 
Uncertainty, on the other hand, can be reduced through disciplined efforts to acquire knowledge. In short, we can reduce or even manage uncertainty (to a degree); how can certainty be improved? Are there higher degrees of perfection? The answer is no. We reason from an uncertain starting point. Yet this effort presumes, as Mill and Wittgenstein postulated, that we can develop or acquire an increasing degree of belief or conviction that there is less potential for error in our reasoning or action. This position assumes that some way exists to identify what a lesser degree of uncertainty would look like.
:'''Knowledge professions acquire an increasing belief or conviction that there is less potential for error in their reasoning or action based on justified knowledge, favorable past experience, and reliable parameters/models.'''
Uncertainty is, in some form, amenable to description and explanation through observation and analysis. It is explainable and predictable to such a degree that it interests professionals such as scientists and engineers. Professional knowledge of this type has explanatory or predictive power, albeit limited in scope and application to relevant subjects such as physical objects or phenomena and functionality.
:'''The very act of reducing the scope of uncertainty to a limited set of phenomena implies choice and, ultimately, the decision to act in the face of such uncertainty, but acting or judging in this manner adds more value than doing the same under relative ignorance.'''
'''Uncertainty''', as described above, implies a hybrid nature. Examples of this are economic or decision-theoretic applications. In economics, [https://en.wikipedia.org/wiki/Perfect_information perfect information] allows one within the limiting framework of perfect competition in economic games to make decisions with 'perfect knowledge.' The crucial difference here is the distinction between '[https://en.wikipedia.org/wiki/Perfection perfection],' broadly, a state of completeness and lawlessness, and 'perfect, which can be thought of as an analytical limit that is approachable but can never be attained. Arbitrarily delimiting the realm of knowledge into defined, finite boundaries allows the simulated production of perfect choice, assumed crucially, to be free from error. The hybrid nature of this form of 'simulated' certainty produced within boundaries defined by assumptions and, therefore, clouded by doubt is itself a form of uncertainty. The importance of this is the semantical and logical association that exists between certainty and perfection versus the hybrid concept of a perfect anything, whether knowledge, infraction, competition, etc., or in the case of engineering, elastic, permeable, conductive, etc., being contained within an admittedly imperfect matrix of uncertainty.
:'''Simply put, a finite, heavily bounded piece of uncertain knowledge can be improved through the simulated use of [https://www.merriam-webster.com/dictionary/assumed assumedly] perfect information, but the perfection of knowledge cannot be attained or simulated.'''
Engineering and Economics, for example, simulate finite elements of perfect knowledge or information, heavily bounded with assumptions and a range of applications. By doing so, these professions reduce the bounds and variability of uncertainty and thereby manage it. Using these hybrid models to acquire knowledge takes place in an economy (regulated or institutional) and environment (transparent and structured), which puts pressure on the various professions to recognize and use qualitative and quantitative knowledge better to reduce uncertainties in their activities. The challenges in this reflect the nature of the profession's or discipline's knowledge (e.g., scientific, engineering, or medical), the policies and structure of the relevant professional knowledge bodies, process objectives and constraints, and "the elusive demands of politics." [6] One common theme runs through all of these efforts to address the presence of uncertainty in professional activities. 
:"The available scientific information upon which a decision must be made is almost always a mixture of engineering knowledge and uncertainty. Regardless of the information or methodology, there is the potential for error. This is true regardless of the relevant science, whether archaeology or aeronautics, economics or engineering, pharmacology or toxicology or epidemiology. Achieving the best social decisions requires not only understanding and using what we know but also appreciating and weighing the extent of our uncertainty. In making the best use of ... information in ... decision-making, it is still true that the beginning of wisdom is knowing what it is we do not know." (Vern, Op. cit., Reformatted and engineering substituted for scientific) 
'''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty to develop explanations, calculate, and make predictions. This professional knowledge converges on but never reaches certainty, producing a "legitimacy" from the fruitfulness of its use.''' <ref>Randomness Is Unpredictability, Antony Eagle, The British Journal for the Philosophy of Science, Vol. 56, No. 4 (Dec., 2005), pp. 749-790 </ref> 
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=== Bias and Uncertainty ===
Up to this point, the concept of error, as outlined in uncertainty, could easily be simulated by a truly random variable. There should be no detectable differences around the unseen or specified statistical mean. However, several instances in experience point to a tendency towards one extreme or a pattern of error, a collective that is in itself an error of errors, namely [https://en.wikipedia.org/wiki/Bias bias].

Arguably, this could be part of the choice uncertainty discussion below, but it makes more sense to be looked at on its own. Acting in the face of uncertainty is an exercise of personal knowledge or experience. Whether using professional knowledge or individual experience, the potential is there to make a series of choices that, in some instances, make errors more likely than they would be if considered in the context of a statistical analysis.

This tendency for an individual to make the same error in a predictable or repeatable manner is termed bias. Personal experience, beliefs, knowledge, data, and models all have inherent built-in factors, errors, or defects that create predictable error patterns when applied in decision-making. Walker (1998) argues implicitly that such bias can also be considered systemic error. This type of uncertainty is difficult to identify except on theoretical grounds when the magnitude and direction of the bias are known. Most of these biases, whether systemic or knowledge-based, individual or practice-based, require disciplined efforts to "validate" such methods, models, and judgments at all layers while bounding the uncertainty.
: This is the challenge to professional knowledge that must be overcome, namely, developing and validating models and methods for managing bounded uncertainty that minimize this tendency towards repeatable groups of error or bias. Therefore, from this point forward, the discussion will refer to 'uncertainty and its proxies, error, and bias.'
===Propagating Error and Uncertainty===
Partitioning uncertainty and its proxies, error, and bias into logically distinct subsets involves or is associated with multiple-step execution or choice frameworks. Combining those measurements or choices into single parameters or choices involves, in some cases, assembling the data/information in a unique or limited range of sequences. This implies that associated errors in observing, calculating, or choosing will have unequal impacts or consequences depending on where in the chain the error occurs. Also implied in this is the possibility that subsequent errors will be causally linked to some degree. (In statistical practice, this depends upon whether the errors are independent of each other or correlated, specifically, co-variant.)
:'''Professions develop knowledge of how uncertainty and its proxies, error, and bias propagate through an ordered and normative series of observations or choices. This allows the profession to prioritize error and bias reduction to achieve an optimized reduction of uncertainty.'''
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===Cognition, Metacognition and Uncertainty===
The concept of choice and the resultant error in uncertainty can be further delimited depending on the role of [https://en.wikipedia.org/wiki/Cognition cognition], [https://en.wikipedia.org/wiki/Metacognition meta-cognition], and independent external elements. The rationale for this argument is that such choice activity in the face of uncertainty is a conscious, cognitive act.

An argument could be made that such cognition is a prerequisite for exercising choice and introducing error, as presented and discussed above. The counterfactual to this argument is the example of uninformed choice. Choices introduce as much as errors or more as informed choices could be made. (???) Choice, in this sense, is indifferent to knowledge. Purposeful choice in an environment of objectives requires knowledge, method, and experience, but uninformed, speculative choice does not.
:"''' Cognition''' is the set of all mental abilities and processes related to knowledge, attention, memory and working memory, judgment and evaluation, reasoning and "computation," problem-solving and decision making, comprehension and production of language, etc. Human cognition is conscious and unconscious, concrete or abstract, as well as intuitive (like knowledge of a language) and conceptual (like a model of a language). Cognitive processes use existing knowledge and generate new knowledge." (Wikipedia)
:"'''Metacognition''' is "cognition about cognition", or "knowing about knowing" and can take many forms. It includes knowledge about when and how to use particular strategies for learning or problem-solving. There are generally two aspects of [https://en.wikipedia.org/wiki/Metacognition metacognition]: knowledge about cognition and regulation of cognition."
Some types of metacognition knowledge are:
:'''Person knowledge''' (declarative knowledge), which is understanding one's own capabilities.
:'''Task knowledge''' (procedural knowledge), which is how one perceives the difficulty of a task, which is the content, length, and type of assignment.
:'''Strategic knowledge''' (conditional knowledge) is one's capability to use strategies to learn information. (Accessed at Wikipedia)
Like metacognitive knowledge, metacognitive regulation or "cognitive control" contains three essential skills.
:'''Planning:''' refers to the appropriate selection of strategies and the correct allocation of resources that affect task performance.
:'''Monitoring:''' refers to one's awareness of comprehension and task performance.
:'''Evaluating:''' refers to appraising the final product of a task and the efficiency at which the task was performed. This can include re-evaluating strategies that were used. (Accessed at Wikipedia)
Nothing has been said up to this point that limits this structure to an individual, or a collective, or two entities making cognitive choices that are incompatible or inconsistent to varying degrees. Interpretations of this scheme include an individual professional exercising judgment in a decision that, in turn, relies upon the collective judgment and decision of the discipline as an underlying basis versus the decision of another individual to oppose/protest or otherwise contest that decision. Thus, the framework of cognitive/meta-cognitive applies to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, economics of regulatory practice, and game theory.
:By solving useful problems in the face of inherent uncertainty, discipline-specific frameworks of cognitive/meta-cognitive activity apply to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, the economics of regulatory practice, and game theory.
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===Probability and Uncertainty===
Probability is a [https://en.wikipedia.org/wiki/Coherence_theory_of_truth coherent] approach to uncertainty in the physical and mathematics or "classical" domains such as engineering. [8] and is the "...is certainly the best-known and most widely used formalism for quantifying uncertainty.[9] [https://en.wikipedia.org/wiki/Cox%27s_theorem Cox’s theorem] ("Any measure of belief is [https://en.wikipedia.org/wiki/Isomorphism isomorphic] (but not necessarily equal) to a probability measure") is a well-known argument for the validity of that argument. [Note 5]

Another writer, [https://en.wikipedia.org/wiki/Frank_Knight Knight] (1921,1956), presented the following taxonomy of probabilities: [10]
: '''Classical 'a priori' probability:''' As an idealized model, numerical probabilities are computed based on generalized principles of assigning equal likelihoods and mutually exhaustive possible outcomes (Runde, 1998) to all set members. Such probabilities are assigned to "...outcomes based on a judgment of indifference between those outcomes, that is, based on the absence of any evidence of real influences in play that may render any one outcome more or less probable than any other." (Runde, op. cit.) This builds upon Knights's concept of an 'absolutely homogeneous classification of instances that are completely identical except for [https://en.wikipedia.org/wiki/Indeterminate_(variable) indeterminate] factors. This judgment of probability or logical probability is on the same logical plane as the propositions of mathematics and ultimately [https://en.wikipedia.org/wiki/Inductivism inductions] from experience.'(Knight, 1956, pg.225)
: '''Bayesian 'a priori' probability:''' In contrast to interpreting probability as the "frequency" or "propensity" of some phenomenon, [https://en.wikipedia.org/wiki/Bayesian_probability Bayesian probability] is a quantity that we assign to represent a state of knowledge or a state of belief. In this view, probability is assigned to a hypothesis, often using the basis of past or prior experience. In contrast, under the frequentist view, a hypothesis is typically tested without being assigned a probability based on prior experience. The Bayesian interpretation of probability can be seen as an extension of propositional logic that enables reasoning with hypotheses, i.e., propositions whose truth or falsity is uncertain. Bayesian probability belongs to the category of evidential probabilities; to evaluate the probability of a hypothesis, the Bayesian probabilist specifies some prior probability, which is then updated in the light of new, relevant data (evidence). The Bayesian interpretation provides a standard set of procedures and formulae to perform this calculation. [11]
: '''Statistical or 'a posterior probability:''' Empirical evaluation of the frequency of association between predicates, not analyzable into varying combinations of equally probable or logical, 'a priori' alternatives. Knight argued that any "high degree of confidence" that the judgment that experience will remain valid for future predictions is "...still based on an 'a priori" judgment of indeterminateness." (Knight, Op. Cit.) Knight's argument to support this is what will be discussed further below that first, "...the impossibility of eliminating all factors not really indeterminate; and, second, the impossibility of enumerating the equally probable alternatives involved and determining their mode of combination so as to evaluate the probability by a priori calculation." (Knight, Op. Cit.) Knight noted that the main difference between this form and that of logical or inductive probability was the presence of empirical information, which allowed the analyst to identify propensity and direction in the observed phenomena. In this case, 'a priori' probability values may be derived from first principles and "statistical probabilities are determined a posteriori by the empirical method of counting instances." (Runde [1998] quoting Knight, op. cit.)
:'''Estimates:''' The distinction here is that there is no valid basis ('a priori' or 'a posteriori') for any classifying phenomena. For Knight, this form of probability presented the greatest logical difficulties of all ... but its distinction from the other types must be emphasized, and some of its complicated relations indicated..." (pp. 224-5, emphasis in the original)

Arguably, Knight's concept of probability can be viewed as "..a continuum of probability situations, depending on the degree of homogeneity of the 'instances' in question" [12] ; i.e. going from a logically distinct but equally likely set of elements to a unique set with one member with statistical frequency in the middle. Knight's main motivation for distinguishing between a priori probability and statistical probability underscores his opinion that "(i) the 'mathematical or a priori type of probability is practically never met with in business, while the second is extremely common'; and (ii) 'the statistical treatment never gives closely accurate quantitative results' (Runde quoting Knight pp. 215-16). Runde argues that Knight couldn't conceive of (empirically tabulated) occurrences that are used in daily commerce classes of instances that we have to make do within the course of everyday economic life could be divided into subclasses of instances that are sufficiently homogeneous to permit the determination of what he calls 'real' probability (p. 217).
:'''In modeling degrees of uncertainty, any measure of belief is isomorphic but not necessarily equal to a probability or statistics measure'''.
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===Explanation, Prediction and Uncertainty===
It should be clear that the general notion of uncertainty synonymous with complete ignorance must be bounded as part of a scheme to produce a useful concept of uncertainty in professional practice such as civil engineering.
:The first principle that must be introduced is that such uncertainty must be bounded by reliable and valuable knowledge gained from past experience combined with analytical and computational capabilities to address an immediate and real problem of interest. This could be viewed as the economic interest argument. Only uncertainties associated with physical phenomena and economic scarcity will be addressed.
:The second principle is that the knowledge model is capable of producing statements or assertions in the form of hypotheses that themselves are testable. [https://en.wikipedia.org/wiki/Testability Testability], in this sense, is defined as the property applying to an empirical hypothesis and involves two components:
::The logical property that is variously described as contingency, defeasibility, or falsifiability, which means that counterexamples to the hypothesis are logically possible. Contrast this to a [https://en.wikipedia.org/wiki/Tautology_(logic) logical tautology], which is always true that is true in every possible interpretation. The practical feasibility of observing a reproducible series of such counterexamples if they do exist. In short, a hypothesis is testable if there is some real, non-zero expectation of deciding whether it is true or false of verifiable, reproducible experience. Upon this property of its constituent hypotheses rests the ability to decide whether a theory can be supported or falsified by actual experience data. (Source Wikipedia.)
'''Knowledge models must be capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria described above in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error and bias.'''
===Variability and Aleatory Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge referred to as epistemic uncertainty. Acting in the face of epistemic uncertainty introduces choice uncertainty (discussed below) and results in the production of explanations and predictions. It is implied that there will be testable hypotheses in the form of outcomes of interest to the knowledge professions. Inherent in nature is a variation of results, which may appear to uncertain but isn't. Observations of phenomena will not be the same, and part of the experimental process is to narrow those results down to within an acceptable level of tolerance. Statistical measures seek to identify underlying and unobserved measures of central tendency. These parameters are not directly observed but are derived to a degree of confidence.
In some cases, variation around a mean or within a statistical range may be perfectly acceptable to the knowledge profession. Examples of this are in the physical sciences and medicine. In Engineering and, to a lesser extent, economics, the process or phenomenon may be required to be more controlled to a narrower range than what a natural or "unmanaged" amount of variation would allow. Several courses of action present themselves.
:The first would be to ignore the aleatory phenomenon and choice, which is rejected outright.
:The second would be to assume that, in effect, such variation is itself a form of minimally managed uncertainty and treat it the same as epistemic uncertainty. This is not a desirable outcome as reducing Aleatory error and bias requires
:A third approach would be to acknowledge that process variation is, in some forms, manageable. This means making choices and acting in a way that presumes that the natural occurrence of error can be reduced, or more importantly, bias can be reduced. There is ample precedent for the third approach in civil engineering design and project management. In this sense, the argument is made that the aleatory response to managed efforts results in an elastic variation range. Presumably, an effective management effort either "shifts" the central tendency of the process results or reduces the variation range of occurrence or some other population parameter. Choosing to act and accept that narrower range rather than the broader natural variation may result in an outcome that, while well within the bounds of the expected variation, is outside the arbitrary control limits established by the choice and action. While this phenomenon mimics the broader uncertainty and its proxy error, it is not an error. The information on the variation was known, and the ability to produce narrower results was an error, albeit a pseudo-error, when compared to the broader classes of uncertainty discussed above, such as total ignorance.
::Looking at uncertainty using this third approach means that the error in choosing a new target parameter for this process may contain epistemic errors (our knowledge of the phenomenon may be erroneous, or the model flawed), aleatory uncertainties as discussed, or, more importantly, choice error or bias in thinking that we can move the process results to meet the requirements.
:'''In civil engineering practice, this may mean applying professional judgment in developing partitioning schemes or allocating uncertainty between the three dimensions (Epistemic, Aleatory, and Behavioral) of uncertainty.'''
Lastly, raising the topic like this brings the questions of effectiveness and efficiency to the fore. Advancing knowledge models that reduce error and bias at first are largely choice models resulting in justifications and favorable experiences. At some point in the process, the knowledge profession advances explanations and predictions that require reliable mathematical or logical concepts that are reduced to models and parameters. The question of efficiency becomes important; the broader uncertainty may have been reduced, but the process is economically inefficient. The profession is urged to become more frugal or, for example, reduce the variability of the outcomes. Knowledge professions often operate under conditions that require them to acquire knowledge and reduce uncertainty effectively and efficiently. Doing so requires understanding both epistemic uncertainty, as discussed above, and statistical process variation or aleatory variation.
:'''Knowledge professions manage and reduce uncertainty in the form of error and bias in an economically effective and efficient manner. This effort requires understanding both epistemic and aleatory uncertainty.'''
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===Choice and Behavioral Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge, referred to as epistemic uncertainty, as well as the inherent variations in results, or what was termed aleatory uncertainty. Acting in the face of that epistemic and aleatory uncertainty introduces yet another uncertainty into the process, namely choice uncertainty. This uncertainty reflects uncertainties associated with first-person and third-person choice. This is particularly relevant for engineering with project stakeholders.
===Summary of Semantics Framework for Uncertainty===
Professional disciplines have gradually re-framed their understanding of uncertainty and, thru experience and analysis, have arrived at the following meta-knowledge concepts of uncertainty:
* '''Uncertainty and its proxy, error, have three components: [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic epistemic uncertainty], [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic aleatory uncertainty], and behavioral uncertainty.'''
* '''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty using non-dogmatic beliefs, which recognize the existence of specific criteria and information that would make the believer change their beliefs using coherent approaches such as probability.'''
* '''Knowledge professions develop knowledge of how uncertainty and its proxy, error, propagate through an ordered and normative series of observations or choices that meet testability criteria in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error. An error can then be described, explained, and reduced in ways that uncertainty cannot. This allows the professions to prioritize error reduction to achieve an optimized reduction of uncertainty and produce a "legitimacy" from the fruitfulness of its use.'''
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===Uncertainty versus Risk/Hazard===
Up to this point, nothing has been said about risk or hazard. Now, risk can be defined. Simply put risk or hazard are finite, logically distinct subsets of the broader uncertainty. Risk and hazard are currently defined interchangeably. First and foremost, risk is a specific subset of general uncertainty. Any arbitrary subset of general uncertainty can be defined as a risk if it meets the following criteria:
* Uncertainty is associated with the potential for material error when acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena.
* Uncertainty and its proxy, error, can be partitioned into logically distinct subsets associated with multiple-step execution or choice frameworks in physical environments linked to a unique or limited range of sequences.
**Subcriteria 1:Resulting errors will have varying impacts or consequences depending on where the error occurs and the degree of causal linkage or influence in the chain.
**Subcriteria 2:Potential errors can be sequenced and prioritized to achieve an optimized reduction of error or bias and, by proxy, uncertainty over time.
* Uncertainty and its proxies, error bias, and environments are isomorphic to coherent approaches such as probability.
* Uncertainty and its proxies, error, and bias are isomorphic to coherent approaches such as knowledge models capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria in 'reasoning under uncertainty.'
This is a definition of general risk. There is no differentiation for engineering or economics, medicine or insurance. There is no difference between a 'good' risk consequence and a 'negative' one. Redefining general risk into simpler terms gives the following:
:'''General Risk is a subset of general uncertainty that is associated with the potential for material errors and biases when sequentially acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena; isomorphic to probability measures and methods and capable of testability.'''
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==Logical Framework for Specific Risk Taxonomies==
As noted above, at the most fundamental level, uncertainty is ".....the subjective perception of ignorance." [13] [Note 6] At this point in the discussion, the focus has moved from discussing uncertainty to its framed subset, risk.
:'''Further, the discussion moves from general risk to a professional or discipline-specific risk [https://en.wikipedia.org/wiki/Taxonomy taxonomy]. Beyond this point, risk will be used in place of uncertainty.'''
Han et al. note that, in general, "(t) taxonomies are valuable not only in their comprehensiveness but in their coherent reduction of uncertainty to conceptually discrete elements."
Uncertainty (and, by implication, risk or hazard) taxonomies offer an approach or tool to more precisely identify and define ontological uncertainty so that it can be quantified, analyzed, and communicated. Viewed this way, uncertainty is not a single, monolithic phenomenon but "...multi-dimensional with theoretically distinct domains and constructs that are potentially measurable and related to different outcomes, mechanisms of action, and management strategies." [14] Taxonomic schemes for classifying the different kinds of scientific and, by extension, engineering uncertainty require identifying the various kinds of potential error associated with descriptive scientific or engineering information and knowledge. An example of this is the following quote from a legal authority:
:"I would especially stress the need for an agency to disclose the uncertainty that surrounds its determinations. '''And by uncertainty, I mean the agency's ignorance as well as its quantitative estimates of error."''' (Emphasis added) [15]
'''Given the necessity of acting in the face of enormous uncertainties [16], discipline taxonomies must offer a clearly coherent and probabilistic approach to uncertainty that is sufficiently general in nature, logically distinct and exhaustive in scope and "...provide decision-makers with a foundation for understanding the nature of discipline information..."''' [17]
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=== Scientific Risk Taxonomies ===
The [https://en.wikipedia.org/wiki/Scientific_method scientific method] is a body of techniques for investigating phenomena in its environment, acquiring new knowledge, or correcting and integrating previous knowledge. Inherent in that process are uncertainties of many forms. Any one taxonomy of [https://en.wikipedia.org/wiki/Uncertainty scientific uncertainty] has largely been focused on statistical models used to assess and quantify sampling, errors, and parameters, although several different taxonomies are conceivable: note-24 [18]
====Environment-specific, Phenomena oriented, model-centric taxonomy of uncertainties====
** [https://en.wikipedia.org/wiki/Uncertainty_quantification#Sources_of_uncertainty '''Parameter uncertainty'''] the source of which is model parameters that are inputs to the computer model (mathematical model) but whose exact values are unknown and cannot be controlled, or whose values cannot be exactly inferred by statistical methods. Subsets of this type of uncertainty include but are not limited to:
***'''Experimental uncertainty''' is also known as observation error, which comes from the variability of experimental measurements. An example is repeating an experiment measurement several times using exactly the same settings for all inputs/variables and recording the variability. It is a subset of the larger uncertainty component, parameter uncertainty.
*** '''Parametric variability''' comes from the variability of the input variables of the phenomena model and is also a subset of the larger uncertainty component, parameter uncertainty.
** '''Structural uncertainty''', or model inadequacy, model [https://en.wikipedia.org/wiki/bias bias] or "[https://en.wikipedia.org/wiki/Systemic_bias systemic bias]", or model discrepancy, which comes from the lack of knowledge of the underlying true state of the phenomenon or its environment. It depends on how accurately a mathematical model describes the true state of the phenomenon or what is termed "model fitness". Due to the inherent nature of uncertainty in any knowledge body (scientific or engineering), models are only an approximation to reality. Therefore, any conclusions drawn from the model can be very misleading when the underlying basis is not plausible or lacks validity. '''note-25 [19]]'''
*** What is missing in this context is any concept of output variability due to the model itself. Part of this is due to the lack of recognition of "process" in scientific investigation. The concept of the scientific method started with a single investigator, such as Galileo or Michael Faraday, working in their laboratories. It has grown into planet and solar system scale experiments such as data gathered on planetary flybys such as Mars, Pluto, and Ceres to the hunt for the Higgs Boson. Ultimately, science has become more like engineering in the scale and complexity of its investigations and theory aggregates, such as the "Unified theory" of particle physics. Subsets of this type of uncertainty include but are not limited to:
*** '''Algorithmic uncertainty''', or numerical uncertainty, comes from numerical errors and numerical approximations per implementation of the computer model. Most models are too complicated to solve exactly. For example, the [https://en.wikipedia.org/wiki/Finite_element_method finite element method] or [https://en.wikipedia.org/wiki/Finite_difference_method finite difference method] [may be used to approximate a solution [https://en.wikipedia.org/wiki/Partial_differential_equation partial differential equation], which introduces numerical errors. Other examples are numerical integration and infinite sum truncation, which are necessary approximations in numerical implementation.
*** '''Interpolation uncertainty''' comes from a lack of available data collected from computer model simulations or experimental measurements. For other input settings that don't have simulation data or experimental measurements, one must interpolate or extrapolate to predict the corresponding responses.
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====Commentary====
A more comprehensive taxonomy of uncertainties that acknowledges knowledge, data, and linguistic uncertainties is:
*'''Epistemic uncertainty''' -[https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Epistemic uncertainty] or systematic uncertainty, in contrast, reflects limitations in the current “state of knowledge” underlying models themselves, originates from competing theories or models, is not readily quantifiable, and is manifest by subjective confusion or indecision. It includes uncertainty due to measurement limitations, insufficient data, extrapolations and interpolations, and variability over time and space. Epistemic uncertainty is uncertainty about "...some determinate fact ... because of a lack of complete information. '''cite_note-26 [20]]''' Epistemic uncertainty can be classified into six main types:'''cite_note-27 [21]]'''
*''' Measurement error''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Observational_error Measurement error] is uncertainty that manifests itself as (apparently) random variation in the measurement of a quantity. Repeated measurements will vary and demonstrate classic statistical behavior.
* '''Systematic error''' - [https://en.wikipedia.org/wiki/Observational_error Systematic error] occurs due to bias in the measuring equipment, model, analysis, observation, or sampling procedure. It is formally defined as the difference between the true value of the quantity of interest and the value to which the mean of the results converges as sample or data sizes increase. Unlike measurement error, it is not (apparently) random, and therefore, results subject to systematic error alone do not vary about a true value. Systematic error can result from deliberate judgment to exclude (or include) data, parameters, or models that ought not to be excluded (or included).
** "The only way to deal with systematic error is to recognize a bias in the process, model, or procedure and remove it. Systematic error, however, is notoriously difficult to recognize except on theoretical grounds. Corrections may only be applied when the magnitude and direction of the bias are known. Such corrections underlie the application of double-sampling methods in environmental science." (Regan, et. al., op. cit.)
*'''Natural variation''' - [https://en.wikipedia.org/wiki/Natural_process_variation Natural variation] or underlying process variation occurs in systems that "...change (concerning time, space, or other variables) in ways that are difficult to predict...across the full range of temporal and spatial values (or other related variables)." '''note-28 [22]'''
** This form of uncertainty is reduceable utilizing tools such as [https://en.wikipedia.org/wiki/Statistical_process_control statistical process control] and replication of experiments or observations.
*'''Inherent randomness''' - [https://en.wikipedia.org/wiki/Randomness randomness] or [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty aleatory uncertainty] exists because the system is"...in principle, irreducible to a deterministic one (the most well-known case is described by [https://en.wikipedia.org/wiki/Uncertainty_principle Heisenberg’s uncertainty principle] in quantum mechanics)." '''note-29 [23], note-30 [Note 7]'''
** Even if the argument is accepted that it is unlikely that any given physical system at some level is inherently random, it is important for any taxonomy to distinguish between systems that appear random due to incomplete or inconsistent information and those that are intrinsically random. (Again, see Regan (2002)).
*'''Model uncertainty''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling Model uncertainty] occurs in the process of generating a model as a conceptual representation of a particular phenomenon to create a [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling#Overview simplified reflection of reality]. This selectivity of factors and variables creates uncertainty in at least three ways. (Regan (2002))
** '''Variables and processes''' that are regarded as relevant often represent a trade-off between system knowledge and assumed states and model objectives;
** '''Models''' depict observed processes using logical or mathematical constructs based on underlying theories about system states or dynamics using continuous equations to describe discrete processes.
** '''Curve fitting''' (including interpolation and extrapolation) with mathematical expressions using model variables where inputs are discrete data points.
** Regan (2002) argues that '''model uncertainty''' is "...notoriously difficult to quantify and impossible to eliminate ...(and)...(t)he only reliable way of determining how appropriate a model is for prediction is to perform validation studies."
** Subjective judgment occurs due to the interpretation of scarce or error-prone data.
*** The only way to address this type of uncertainty is to "...assign a degree of belief about an event in the form of a subjective probability." (Regan (2002))
*Linguistic uncertainty - Linguistic uncertainty, or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. '''note-31 [24]], note-32 [25]]'''
** '''Context dependence''' is uncertainty concerning the context in which a statement is to be understood;
** '''Ambiguity''' occurs when words have multiple meanings, and further reduction to a single meaning is not logically possible in a given context.
** '''Underspecificity''' occurs in the presence of "unwanted generality" or multiple interpretations, and reduction to a smaller set or even a single interpretation is not logically possible.
*Stochastic or statistical uncertainty - Stochastic or statistical uncertainty, which is sometimes known as [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Aleatoric uncertainty] as well as the subject of [https://en.wikipedia.org/wiki/Stochastic_control stochastic control] pertains to the parameters of a risk model, originates from sampling or measurement error, and can be quantified and mathematically expressed (e.g., using confidence intervals).
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== Uncertainty Taxonomies in a Legal Environment ==
From a legal perspective, a definition of taxonomy is dictionary-based, such as "the systematic distinguishing, ordering, and naming of type groups within a subject field." given by Walker (op. cit.). Such legal concepts of scientific or engineering taxonomies are not required to be epistemically complete. Still, they must cover "the most significant aspects of ...(current, good discipline practices) ... and of the descriptive information commonly encountered by decision-makers" in a logically distinct manner. (Walker, 1991, p. 571) These taxonomies must be sufficient to catalog the types of uncertainty associated with descriptive assertion, including the critical subset of cause and effect assertions. This legal/decision-maker framework has presented descriptive uncertainty as having six components (Walker, 1991). Although not part of the Walker taxonomy, underlying the scheme is linguistic uncertainty, or "vagueness," as a source of uncertainty, including uncertainties due to context dependence, ambiguity, and under-specificity. '''note-33 [26]]''' Arguably, these uncertainties underlie the most basic element in the taxonomy.
* '''Linguistic '''- or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. (Regan, 2002)
* '''Conceptual'''- the definition by choice and design of descriptive concepts or variables to be used as [https://en.wikipedia.org/wiki/Predicate_(grammar) predicates] where the subject of an assertion identifies what is being discussed and the predicate provides information about the topic or characterizations such as what the subject is, what the subject is doing, or what the subject is like (Wikipedia).
* "Conceptual uncertainty, or the potential for conceptual error, arises whenever predication occurs. Whenever a concept is used to describe something, using certain concepts instead of others begins to structure how we understand the object, event, or instance under discussion. Predication or conceptualization generates useful information about things, but it also can inhibit our ability to think about those things with concepts other than those selected. The concepts used may not be the most fruitful or the best designed- either for scientific purposes or for making wise, fair, effective, and efficient decisions." (Edited for reading, Walker, 1991)
* Similarly, choice and design of descriptive variables enhance and restrict data set membership, creating potentially incomplete, incompatible, or inconsistent data sets, "...the classification categories employed, and the relationships among those categories (nominal, ordinal or scalar)." '''note-34 [27]]'''
* Concept uncertainty '''note-35 [28]''' like systematic error discussed above is notoriously difficult to recognize, except on theoretical grounds, and the magnitude and direction of the bias are known.
Both terms could be analogized to that of [https://en.wikipedia.org/wiki/Accuracy_and_precision#Terminology_of_ISO_5725 accuracy] in [https://www.bipm.org/en/publications/guides/ ISO 5725] which looks at the ability of the concept or system's ability to produce results that are proximate to the "true" value or state of the phenomenon. Conceptual uncertainty and systemic bias are related to another measure of uncertainty: validity. Walker has defined validity as the ability of a concept to quantitatively describe and explain what the phenomenon objective "truly" is. Walker also ties the concepts together when he states, "Validity concerns the "accuracy" of the measurement data, not its precision." (Walker, 1998) 
Additionally, conceptual uncertainty and associated validity issues could be propagated throughout this taxonomy. Every component of action and decision in the face of uncertainty raises validity issues. Walker recognized this when the author defined epistemic choice for concepts used throughout the other uncertainty components or layers.
* '''Measurement''' uncertainty or Misclassification error- the application of the underlying concepts or variables to specific, individual cases and the uncertainty of the reliability (Walker, 1998) of the resultant data. (It is important to remember that assessment observation and measurement are used interchangeably in this analysis.) Thus, an assessment method or procedure is considered "reliable" or "precise" in the scientific terms of ISO 5715 if it repeatedly produces consistent results.
* Measurement uncertainty is logically distinct from conceptual uncertainty, which can only occur after any conceptual uncertainty has been established. Still, misclassification can occur even when the conceptual uncertainty has been minimized. Conversely, underlying errors or errors made in measurement or classification propagate thru the reasoning chain and reduce the quality or value of the final result.
* While uncertainties associated with quantitative measurements or assessments can be statistically evaluated and described, uncertainties related to qualitative assessments are more problematic.
**Working through the taxonomic chain from base concept to causal explanation requires amassing and integrating qualitative information and quantitative data into input parameters for mathematical models. Developing procedures for producing consistent, repeatable sets of information from assessments that minimize conceptual error or systemic bias requires professional efforts to "validate" such methods, models, and judgments at all layers in the process. Validation as a process is something that civil engineering as a practice needs to embrace more frequently.
* '''[https://en.wikipedia.org/wiki/Sampling Sampling]'''- in the classical statistics sense, is the selection of a limited subset of a larger population to use as a basis for making estimates about the population in the form of attributes, proprieties, or parameters. The resulting information would be determinative in making forecasts about future population sampling or possessing a desired amount of predictive power. The uncertainty about the ability of this information to correctly predict future samples is termed sampling error.
* '''Modeling'''- Modeling uncertainty arises whenever a claim is made that out of a class of candidate relationships and variable pairings, one variable pairing inclusive of constants is chosen that possesses a particular or persistent mathematical relationship to another variable X. Modeling errors arise in selecting the wrong variable pair and constants or by incorrectly specifying its constants. (Walker, 1991, pg. 599)
** '''Causal'''- In causal modeling and analysis, the relationship between causal variables is not strictly a mathematical function. Still, it makes testable predictions and explains "...how a system of variables works, why a system works the way it does, or why it makes sense to think of certain variables as a system" at all." (Walker, 1991, pg. 609)
** '''Epistemic'''- the choice of interpretations for fundamental, logical concepts used throughout the other components or layers.
The components of this uncertainty taxonomy can combine in different ways to "...produce different aggregate uncertainties." '''note-36 [29]'''
:''' Only the scientific and engineering professions possess the capacity to develop and validate procedures and models that combine the different kinds of qualitative '''note-37 [30]''' and quantitative information and their associated potential for error into an aggregate measure or "scalar variables" of uncertainty.'''note-38 [31]]
Scalar variables are quantitative variables whose categories are related by some measure of the relevant property's incremental frequency, degree, or amount. (Walker, 1991) Examples are Modulus of Elasticity, Compression stress in elastic materials, etc.

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=== Economics Uncertainty Taxonomies ===
Like scientific concepts of uncertainty, economics is environment-specific (in this case, read sector of economy or markets), phenomenon-oriented (in this case, read human activity and manufactured phenomena such as firms and markets guided by human judgment and decision), and model-centric.
:''' Economics is not interested in all forms of uncertainty, only specific, meaningful subsets possessing casual connections with material impacts, often labeled as 'risks'.'''
One of its earlier writers ([https://en.wikipedia.org/wiki/Frank_Knight#Life_and_career Knight], 1921, 1948, 1957) attempted to define "uncertainty" as the presence of "defects of managerial knowledge" (or [https://en.wikipedia.org/wiki/Knightian_uncertainty knightian uncertainty] and thus risk centric) as risk that is immeasurable, not possible to calculate.(Wikipedia) Risk in this sense was defined as "the ordinary risks of business activity which can ... be reduced... by applying the insurance principle." Knight does acknowledge that risk is a specific and unambiguous subset of uncertainty. (Risk, Uncertainty and Profit, 1957, pg. 19) For Knight, measuring uncertainty meant measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences, which Knight referred to as "the imperfection of knowledge". (Risk, Uncertainty and Profit, 1957, pg. 197) One of Knight's many contributions to this analysis was recognizing information and knowledge's role in economic activity.
:"''(W)e are concerned only to emphasize the fact that '''knowledge is in a sense variable in degree and that the practical problem may relate to the degree of knowledge rather than to its presence or absence in toto.''' We live only by knowing something about the future, while the problems of life, or conduct at least, arise from knowing so little. This is as true of business as of other spheres of activity. The situation's essence is action according to opinion, of greater or less foundation and value, neither entire ignorance nor complete and perfect information, but partial knowledge. If we are to understand the workings of the economic system, we must examine the meaning and significance of uncertainty; and to this end, some inquiry into the nature and function of knowledge itself is necessary.''" (Knight, pg. 199) Emphasis added
Knight also reinforced the difficulties in classifying uncertainties when he analyzed life insurance. In this case, accidental death was an uncertain phenomenon compared to sickness and accident, where an "...objective description and classification of cases was impossible..." (Knight, pg. 248) Another factor that made uncertainties ineligible as risks and, therefore, uninsurable because they were unclassifiable was the exercise of judgment in making decisions by the businessman. (Knight, pg. 251)
This is a simple classification of economic uncertainty and, as an idealization, is philosophically controversial. '''note-39 [32]'''
* Uncertainty and information about the economic environment are distinct from uncertainty about others’ behavior or choices.
* Risk as a specific subset of uncertainty implies that "...all possible acts are known, all possible outcomes arising from each act are known, and it is possible to assign probabilities to each act." '''note-40 [33]'''
Like the economic [https://en.wikipedia.org/wiki/Theory_of_the_firm theory of the firm], an economic theory of uncertainty seeks to explain and predict the nature of a specific subset of uncertainty (in this case, risk relating to physical phenomena and human activity), including its behavior, structure, and relationship to the universal uncertainty of physical phenomena. In this sense, uncertainty, like firms and markets, can establish price equilibriums under the right circumstances different from those that might have occurred in either of the other two. Such a theory of uncertainty offers a partitioning scheme that decomposes uncertainty into distinct and meaningful subsets, of which risk is a significant one. It also explains how the presence and variability of knowledge cause or " drive" economic consequences. Unlike physical phenomena, economic activity creates artificial objects such as products, firms, mediums of exchange, markets, economies, and knowledge about those activities. The economics of uncertainty exists as an alternative to "certainty" models that assume away imperfect knowledge and unknown choice/judgment preferences when it is more efficient for economic decision-making. This also allows the introduction of the logical concept of "hazard" versus "risk". Frank used hazard extensively in Risk, Uncertainty, and Profit, but not to the extent that one could say it was interchangeable. An example of this is in the term "[https://en.wikipedia.org/wiki/Moral_hazard Moral hazard]," where a firm could engage in "riskier" and potentially "uninsurable" behavior if the information were transparent, and, therefore, insurance coverage of losses is limited. note-41 [34]], note-42 [35]]

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=== Uncertainty and Risk in Policy and Regulatory Environment ===
:''"Hume's 'just reasoner', when faced with a difficult public policy decision in current times, would probably commission a risk analysis. But how could they incorporate 'a degree of doubt, caution, and modesty?"'' '''note-43 [36]'''
The problem of "decision-making in the face of uncertainty" is how to regulate based on incomplete information that has the potential to be materially inaccurate. A contextual assumption is that we need to evaluate decision rules for dealing with uncertainty. As a social enterprise, risk regulation, whatever its substantive objectives, should be as '''effective, efficient, and equitable''' as practically possible. These "three E's" form a set of "process objectives" or "meta-goals." From the uncertainty standpoint, causal information can be usefully divided into two major categories: information about groups and information about individuals.

Each category, group, and individual has its distinctive types of inherent uncertainty. These are logically distinct, generally independent, and cumulative, contributing to '''(?????)'''

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=== Engineering Uncertainty Taxonomies ===
Like scientific and economic concepts of uncertainty, engineering is environment-specific, phenomena-oriented, model-centric, external choice, and driven by decision-making in the face of uncertainty. Like economics and unlike science, engineering is not interested in all forms of uncertainty, only those with determinable and material consequences. Like economics, engineering conceives risk as a specific and unambiguous subset of uncertainty determined by measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences. Also, engineering recognizes the role information and, by extension, knowledge plays in constructing the built environment.
'''note-44 [37]]''' Like economics, engineering differs from economics and scientific uncertainty in its interest in behavioral uncertainty.
*"Behavioral or interaction uncertainty is how individuals or organizations act or interact. Behavioral uncertainty arises from four sources: design uncertainty, requirement uncertainty, volitional uncertainty, and human errors." '''note-45 [38]]'''
** A design uncertainty is a choice among alternatives over which an individual or group of individuals exercises direct control but has not yet decided upon.
** Requirement uncertainty includes parameters of interest to and determined by the stakeholder, independent of the engineer or designer.
** Volitional uncertainty is uncertainty about what the subject him/herself will decide. Other people’s future actions and conduct are not entirely predictable, particularly when dealing with other organizations.
** Human errors occur during the development of a system or project due to blunders or mistakes by an individual or individuals.

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==Practice Frameworks for Uncertainty and Risk in Civil Engineering Practice==
===PMIBoK===
PMI does not define uncertainty, but the PMI Body of Knowledge uses the term in 45 places (uncertainty) and as describing properties in 8 places (uncertainties). [See Note 8] PMI does define "risk" and uses the term over 1,300 times in the same document. In its glossary, PMI also defines terms such as "threat" and "opportunity" but not events.[391 The BoK contextually defines uncertainty when it states that project risk has its origins in the uncertainty present in all projects. (Op. Cit., pg 309) 
'''Uncertainty''' is contextually defined and taken as having the property of affecting project execution and the ability to meet stakeholder expectations. Uncertainty is more than the sum of all individual risks (known and unknown). The nature of this uncertainty is not defined, only its capacity to affect something else or its properties. 
'''Risk''' is defined as " ... an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives." (Ibid.) Beyond this formal definition, PMI adds context when, in its risk management section (Sec. 11 ), the BoK states that risk is a caused event or condition with multiple causes and impacts. The BoK even offers specific techniques for mapping cause-and-effect relationships and influence diagrams. (Sec. 11.2.2.5) Not as evident but equally important is the recognition that not only is risk caused but the impact is "triggered". The risk trigger is an event or situation that signals that it is about to occur (Glossary, pg. 566). Risk conditions are factors that affect or otherwise contribute to risk, such as project stakeholders. Risk implicitly retains some residual element of probability in it, or a value of less than 1.0 with risk that approaches a level of near certainty is termed "issues" or "realized risk" .(Op. Cit., pg 309). PMI also links risk to underlying variations in project outcomes. (Ibid.) Risk is based upon data and information which can be assessed as to its quality (Sec. 11.3.2.3). This analysis looks to determine the value of the information and examines the degree to which the risk information is understood as well as its " ... accuracy, quality, reliability and integrity ... " (Ibid.) Part of that is understanding the relationship between occurrence and impact as outlined in PMl's Figure 11-10 Risk Impact Matrix and developing a model for prioritizing risk products and setting the threshold for action.
====Commentary:====
For defining and identifying risk, PMI notes in Sec. 11.2.2.4 that " ... Every project and its plan is conceived and developed based on a set of hypotheses, scenarios, or assumptions." Part of the risk analysis process is identifying risks to the project, such as inaccuracy, instability, inconsistency, or incompleteness of assumptions. Similarly, the quality of management plans, as well as their consistency with others and the project objectives and assumptions are 11 •• .indicators of risk in the project." (Sec. 11.2.2.1) 
Similarly, the role of engineering economics in risk is embedded or implied in the PMI definition of risk when the BoK discusses risk identification as a process of assessing which risks out of the total risk exposure for the project "may affect" the project and distilling their characteristics. Embedded in this statement is the concept that not all risks can cause economic impacts (positive and negative) to the project. Also embedded is the requirement to understand risk characteristics to identify project risk (Cost and schedule). Lastly, risk identification is an iterative process of incrementing project risk documentation, much like project scope, cost, and schedule documentation. 
Lastly, PMI acknowledges the complex constitution of risk aggregates or the "sum" of all risks in its material, but is it a sum? Risk information has descriptive and explanatory content. These are knowledge and meta-knowledge components. Understanding project assumptions is project-specific knowledge, but identifying risks from assumption inaccuracy, instability, inconsistency, or incompleteness requires professional or program meta-knowledge. 
'''Uncertainty exists in all projects, but risk, a subset of uncertainty with material economic impacts, is a key meta-knowledge element for civil engineering practice.'''
===Software Development Process- Risk Focused===
The Unified Process requires the project team to focus on addressing the most critical risks early in the project life
cycle. The deliverables of each iteration, especially in the Elaboration phase, must be selected to ensure that the greatest risks are addressed first. <ref> Booch, Grady, Ivar Jacobson, and James Rumbaugh. "The unified software development process."
Reading: Addison Wesley (1999).</ref>
===American Society of Civil Engineers (ASCE)===
ASCE does not define uncertainty, but the ASCE CE Body of Knowledge (CEBoK) uses the term in 22 places (uncertainty) and describes properties in 13 places (uncertainties). 
The CEBoK does not use the phrase "uncertainty and risk" as PMI and AACE do in their publication. ASCE reverses the two (Risk and uncertainty) in twelve places regarding technical outcomes (Outcome 12). A fundamental difference between PMI and ISO is that risk is considered a subset of uncertainty. ASCE does not define "risk" but uses the term 25 times in the same document. Almost all are in the context of variation of design parameters and not in the classical context of cost, schedule uncertainty, and risk.
====Commentary:====
...
=== American Association of Cost Engineers (AACE) ===
AACE defined uncertainty, risk, and other related terms in its Risk Management Dictionary. <ref>Risk Management Committee uRisk Management Dictionary, Cost Engineering Vol. 37/No. 10, OCTOBER 1995. </ref>
AACE's overall strategy was to define risk and other terms that stem from base uncertainty. AACE first defined uncertainty as .... "(t)he total range of events that may happen and produce risks (including both threats and opportunities) affecting a project (see opportunities, events, conditions, risk, and threats" where the following sub-definitions apply:
** Biases--A lack of objectivity based on the individual's position or perspective. Systematic and predictable relationships between a person's opinion or statement and his/her underlying knowledge or circumstances. Note: there may be "system biases" as well as "individual biases."
** Condition (Uncertain Condition)-Any specific identifiable circumstance (such as the rate of inflation or the quality of labor available) that might affect the outcome of the project
** Event (Uncertain Condition)-is a specific identifiable action (such as a large government project being started in the same labor area as your project) or an act of nature that might happen and that (if it does happen) could affect the outcome of the project.
** Opportunities are Uncertain events that could improve the results or improve the probability that the desired outcome will happen
** Threats are Uncertain events that are potentially negative or reduce the probability that the desired outcome will happen.
*** AACE defines Risk as an "ambiguous term" (sic) that is synonymous with uncertainty or a negative subset such as "threats", or an overall negative impact of all possible uncertainties.
====Commentary:====
...
==Limitations of the definition==
...
==Working definition==
...
==Beneficial outcomes==
...
==See also==
Wiki articles on the project.

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==Notes==
<references group="note" />

# See McDaniels et al. 2004 for an extensive discussion and bibliography on the historical development of Risk Analysis and some key milestones in the 20th century.
# Open item
# The Merriam-Webster dictionary defines uncertainty:
::"the quality or state of being uncertain," which is a circular definition. Likewise, one could define uncertainty as the state of not being certain. Synonyms are distrust, doubt, misgiving, mistrust, reservation, skepticism, and suspicion. Another writer added indefinite, indeterminate, not certain to occur, problematical, unreliable, untrustworthy, unknown beyond doubt, dubious, doubtful, not clearly identified or defined, not constant, variable, and fitful to the list.
::Han, Paul KJ, William MP Klein, and Neeraj K. Arora. "Varieties of Uncertainty in Health Care: A Conceptual Taxonomy." Medical Decision Making 31.6, (2011 ): 828-838.
::: Han, ct. al. noted that any definition of uncertainty encompasses " ... numerous types, sources, and manifestations of uncertainty, and ... (any) ... useful working definition of uncertainty needs to specify the concept underlying these varied meanings of the term." (Ibid.)
<ol start="4">
<li> Implicit in this definition of uncertainty as a "state of' is ... </li>
: " ... a conceptualization of uncertainty as a subjective, cognitive experience of people--a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one's lack of knowledge, without which one could not feel uncertain; '''uncertainty is a form of "meta-cognition" ... ( or alternatively, its main component, meta-knowledge) ... -a knowing about knowing.'''" (Han, 2011, op. cit., Emphasis added)
</ol>
<ol start="5">
<li> Cox wanted his system to satisfy the following conditions:</li>
: Divisibility and comparability- The plausibility of a statement is a real number and depends on the information we have related to the statement.
: Common sense - Plausibilities should vary sensibly with the assessment of plausibilities in the model.
: Consistency - If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). -preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf
</ol>
<ol start="6">
<li> The utility of existing taxonomies of uncertainty in civil engineering and, by extension, "risk" has been unsatisfactory for this very reason. This is particularly true in cases where the source of the uncertainty is conflicting versus incomplete information. See Regan, et. al.,"A taxonomy and treatment of uncertainty for ecology and conservation biology" (2002)
for a survey of scientific taxonomies for uncertainty.</li>
<li> Regan et al. argue that " ... genuine examples of this kind of uncertainty are hard to find. Even classic cases of random experiments like coin tosses and the throwing of dice are deterministic; it is just that we do not have enough information about the dynamic processes and initial conditions to make any sensible estimates about the outcomes. Such processes are, for all intents and purposes, inherently random, but they are not genuinely inherently random. For similar reasons, complex systems such as ecosystems and weather patterns are unlikely to be inherently random. Similarly, chaotic systems are entirely deterministic. They are unpredictable because the deterministic processes generating them and the relevant initial conditions are hard
to fully specify (see Stewart 1989, Sugihara et al. 1990)." (Regan (2002) </li>
<li> The CEBoK uses the phrase "uncertainty and risk" twice in reference to scheduling (Section 6.5,2) and cost estimating (Section 7.2,2) as well as reversing the two (Risk and Uncertainty) also in two places in discussing project life cycle (both in Section 2.4.1) </li>
</ol>

==References==
<references />

Uncertainty and Risk in Civil Engineering Practice

2024-11-24T16:03:18Z

WikiSysop: /* Semantic, Epistemic and Logical frameworks */

'''Life is short and the art long; the occasion instant, experiment perilous, decision difficult.''' (Hippocrates as quoted in Fox,)
:''See also [[Project Definition]] , [[Project Life Cycle and Phase Models]] , [[Project Delivery Methods]]''
:''See also [[Technical Outcomes for Civil Engineering:Risk and Uncertainty]]''
==Basic Considerations==
As human beings,'' ... being alive means seeking opportunities and taking risks.'' As knowledge professionals
living in the 21st century, this means coping with an increasingly complex number of uncertainties for humans
living in this environment. We seek to understand better how these uncertainties can be characterized and
managed. The essence of this article and the experience for engineers in general and civil engineers in particular is
that manageable uncertainty is, by definition, termed risk, and its kindred cousin is termed hazard. This causes us to experience the
:" ''... human dread of and fascination for risk and the increasingly important role of risk analysis within societies ...'' " <ref name="McDaniels"> McDaniels, Timothy, and Mitchell Small. Risk analysis and society: an interdisciplinary characterization of the field. Cambridge University Press, 2004, page 1 </ref> (Ibid.)
and, by extension, civil engineering. McDaniels et al. argue that risk management has been
fundamental to our social and governance development for the past 10,000 years. The scale and shape of the
uncertainties faced in this period shaped the societies that have developed today. The central thrust of this effort
over the centuries has been to reshape and re-frame our understanding and conception of uncertainty from one of
complete unknowing and simple acceptance as our fate in life to one of management (cf "''Against the Gods''"
concept in Bernstein's book <ref name = Bernstein">Bernstein, Peter L., and Jesse Boggs. Against the gods. Simon & Schuster 1997.</ref>.
: ''All the knowledge professions and disciplines have struggled with managing uncertainty, for it is impossible to manage unbounded uncertainty.'' <ref group="note">See McDaniels et al. 2004 for an extensive discussion and bibliography on the historical development of Risk Analysis and some key milestones in risk analysis in the 20th century.</ref>
As outlined below, professions such as civil engineering have successfully acted in the face of uncertainty. It lies in the profession's ability to reduce a wide variety of uncertainties into increasingly smaller and crucially bounded subsets that can be managed. These are called 'risks. More recently, this process has evolved, and individual disciplines such as Civil Engineering (CE) have developed their knowledge and models to perform risk analysis. Risk is also taught as a distinct discipline and specialty practice within civil engineering. Still, it has some unique features that set it apart from other more classical practices within CE. As such, it is one of the first of some very specialized CE practices that use knowledge about civil engineering knowledge, or meta-knowledge. Other examples of civil engineering metaknowledge are project controls and quality controls.
==Semantic, Epistemic and Logical frameworks==
The semantic, epistemic, and logical frameworks for uncertainty and risk have several dimensions and layers of logical frameworks. The semantics problems include interchangeable usages for risk and uncertainty and hazard, uncertain and imperfect, and a lack of definitional material or context. The semantical scheme for this article will be to proceed from the distinction between certainty and uncertainty through marginal refinements and reductions of uncertainty up to the point of causal uncertainties. Beyond this point of semantics will be the logic frameworks or the subject of taxonomies of professional knowledge. First, it is about simple, testable phenomena, and then, it moves on to complex taxonomy schemes for artificially constructed phenomena or engineering projects.
<ref group="note">TBD</ref>

==Semantics framework==
===Certainty and Uncertainty===
''''' There is no such thing as absolute certainty, but there is assurance sufficient for the purposes of human life.''''' ([https://en.wikipedia.org/wiki/John_Stuart_Mill John Stuart Mill]) 
'''''If you tried to doubt everything, you would not get as far as doubting anything. The game of doubting itself presupposes certainty.''''' ([https://en.wikipedia.org/wiki/Ludwig_Wittgenstein Wittgenstein] # 115 from On [https://en.wikipedia.org/wiki/On_Certainty Certainty]) 
It is important to note that the references to epistemic or epidemiological knowledge in this article are assumed to relate to three forms of knowledge, namely:
* Knowledge that ( [https://en.wikipedia.org/wiki/Descriptive_knowledge descriptive or declarative or propositional knowledge])
* Knowledge how ( or "[https://en.wikipedia.org/wiki/Procedural_knowledge know-how]"), and
* [https://en.wikipedia.org/wiki/Knowledge_by_acquaintance Knowledge by acquaintance].
'''Certainty''' has been defined as "an epistemic property" of knowledge in all its forms and the state of our beliefs about that knowledge.
<ref group="note">Certainty, Stanford Encyclopedia of Philosophy, accessed on September 12, 2015, at http://plato.stanford.edu/entries/certainty/#ConCer </ref>
Certainty about any belief about knowledge implies that it is not subject to doubt or skepticism. This immunity to criticism can be dogmatically based, emphasizing the importance of a propositional-based sense of truth over experiential, sensory perceptions. <ref name="Note4"/>
: '''The demarcation line between dogmatic and non-dogmatic beliefs lies in the presence and recognition of specific criteria and information that would make the believer change their beliefs.'''
An empirical framework of [https://en.wikipedia.org/wiki/Testability testability] and [https://en.wikipedia.org/wiki/Falsifiability falsification] is required to recognize this and limit dogmatism. For example, one could argue that people would change their minds if God asked them to. Similarly, one could construct a concept of epistemic as opposed to dogmatic belief certainty as the ability to know anything that one chooses to know and can be known or [https://en.wikipedia.org/wiki/Omniscience inherent omniscience].

A second kind of certainty is epistemic, when conviction reflects the highest possible support for a belief. In this sense, knowledge is separate from beliefs, although someone may have beliefs about a property, such as certainty of knowledge. Logically, it has been shown that there will be unprovable statements within the system for any such system of knowledge. Secondly, the knowledge system cannot demonstrate its own consistency. ([https://en.wikipedia.org/wiki/Gödel%27s_incompleteness_theorems Gödel's incompleteness theorems])
: '''Certainty, in real life, is useless or often damaging (the idea is that "total security from error" is impossible in practice, and a complete "lack of doubt" is undesirable)''' ([[https://en.wikipedia.org/wiki/Certainty Physicist Carlo Rovelli]])
'''Uncertainty''', on the other hand, arises immediately in the slightest amount of doubt or criticism. <ref name="Note3">[Note 3]</ref> Similarly, a general definition of uncertainty could be given as "...any departure from the unachievable goal of complete [https://en.wikipedia.org/wiki/Determinism determinism]." <ref name="Reference5">[5]</ref> Reasoning under uncertainty is very different than performing the same under certainty. In reasoning under certainty, one has complete knowledge and deduces without doubt and equally important, without limitation, thereby concluding free from error. Reasoning under uncertainty, one works in a state of incomplete, inconsistent, and limited knowledge; doubts cloud any statements or assertions and thereby taint any deductions/inferences resulting in the potential for error. <ref name="Note4">[Note 4]</ref>
:'''Knowledge professions reason in a state of incomplete, inconsistent and limited knowledge with doubts that cloud any statements or assertions and taint any deductions/inferences resulting in the potential for error.'''
:''' It is also important to note the semantical and logical correlation between 'reasoning under uncertainty' or 'acting in the face of uncertainty' with the 'potential for' or 'presence of' error. The presence of uncertainty is invariably linked to the potential presence of error.''' The [https://en.wikipedia.org/wiki/Contingency_(philosophy) contingent] nature of uncertainty logically implies the contingent nature of error.
:'''Knowledge Professions use error as a proxy for uncertainty such that within discipline knowledge frameworks, uncertainty can be managed.''' The rationale for this belief is that error can be reliably described, quantified, explained, and ultimately reduced in ways that uncertainty cannot.

'''Uncertainty''' implies a range of variation that is impossible in certainty. Concepts and definitions of uncertainty are unbounded and vast but start from the point of complete and total ignorance. In describing uncertainty, statements can range from complete ignorance to relatively high degrees of belief that there is less potential for error in our reasoning or action. The latter is based upon confidence in justified knowledge, favorable past experience, and reliable parameters/models. One can't make that statement about ranges in certainty. If certainty, by definition, precludes doubt of any type or nature, then how could that be graduated to any degree? 
Uncertainty, on the other hand, can be reduced through disciplined efforts to acquire knowledge. In short, we can reduce or even manage uncertainty (to a degree); how can certainty be improved? Are there higher degrees of perfection? The answer is no. We reason from an uncertain starting point. Yet this effort presumes, as Mill and Wittgenstein postulated, that we can develop or acquire an increasing degree of belief or conviction that there is less potential for error in our reasoning or action. This position assumes that some way exists to identify what a lesser degree of uncertainty would look like.
:'''Knowledge professions acquire an increasing belief or conviction that there is less potential for error in their reasoning or action based on justified knowledge, favorable past experience, and reliable parameters/models.'''
Uncertainty is, in some form, amenable to description and explanation through observation and analysis. It is explainable and predictable to such a degree that it interests professionals such as scientists and engineers. Professional knowledge of this type has explanatory or predictive power, albeit limited in scope and application to relevant subjects such as physical objects or phenomena and functionality.
:'''The very act of reducing the scope of uncertainty to a limited set of phenomena implies choice and, ultimately, the decision to act in the face of such uncertainty, but acting or judging in this manner adds more value than doing the same under relative ignorance.'''
'''Uncertainty''', as described above, implies a hybrid nature. Examples of this are economic or decision-theoretic applications. In economics, [https://en.wikipedia.org/wiki/Perfect_information perfect information] allows one within the limiting framework of perfect competition in economic games to make decisions with 'perfect knowledge.' The crucial difference here is the distinction between '[https://en.wikipedia.org/wiki/Perfection perfection],' broadly, a state of completeness and lawlessness, and 'perfect, which can be thought of as an analytical limit that is approachable but can never be attained. Arbitrarily delimiting the realm of knowledge into defined, finite boundaries allows the simulated production of perfect choice, assumed crucially, to be free from error. The hybrid nature of this form of 'simulated' certainty produced within boundaries defined by assumptions and, therefore, clouded by doubt is itself a form of uncertainty. The importance of this is the semantical and logical association that exists between certainty and perfection versus the hybrid concept of a perfect anything, whether knowledge, infraction, competition, etc., or in the case of engineering, elastic, permeable, conductive, etc., being contained within an admittedly imperfect matrix of uncertainty.
:'''Simply put, a finite, heavily bounded piece of uncertain knowledge can be improved through the simulated use of [https://www.merriam-webster.com/dictionary/assumed assumedly] perfect information, but the perfection of knowledge cannot be attained or simulated.'''
Engineering and Economics, for example, simulate finite elements of perfect knowledge or information, heavily bounded with assumptions and a range of applications. By doing so, these professions reduce the bounds and variability of uncertainty and thereby manage it. Using these hybrid models to acquire knowledge takes place in an economy (regulated or institutional) and environment (transparent and structured), which puts pressure on the various professions to recognize and use qualitative and quantitative knowledge better to reduce uncertainties in their activities. The challenges in this reflect the nature of the profession's or discipline's knowledge (e.g., scientific, engineering, or medical), the policies and structure of the relevant professional knowledge bodies, process objectives and constraints, and "the elusive demands of politics." [6] One common theme runs through all of these efforts to address the presence of uncertainty in professional activities. 
:"The available scientific information upon which a decision must be made is almost always a mixture of engineering knowledge and uncertainty. Regardless of the information or methodology, there is the potential for error. This is true regardless of the relevant science, whether archaeology or aeronautics, economics or engineering, pharmacology or toxicology or epidemiology. Achieving the best social decisions requires not only understanding and using what we know but also appreciating and weighing the extent of our uncertainty. In making the best use of ... information in ... decision-making, it is still true that the beginning of wisdom is knowing what it is we do not know." (Vern, Op. cit., Reformatted and engineering substituted for scientific) 
'''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty to develop explanations, calculate, and make predictions. This professional knowledge converges on but never reaches certainty, producing a "legitimacy" from the fruitfulness of its use.''' <ref>Randomness Is Unpredictability, Antony Eagle, The British Journal for the Philosophy of Science, Vol. 56, No. 4 (Dec., 2005), pp. 749-790 </ref> 
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=== Bias and Uncertainty ===
Up to this point, the concept of error, as outlined in uncertainty, could easily be simulated by a truly random variable. There should be no detectable differences around the unseen or specified statistical mean. However, several instances in experience point to a tendency towards one extreme or a pattern of error, a collective that is in itself an error of errors, namely [https://en.wikipedia.org/wiki/Bias bias].

Arguably, this could be part of the choice uncertainty discussion below, but it makes more sense to be looked at on its own. Acting in the face of uncertainty is an exercise of personal knowledge or experience. Whether using professional knowledge or individual experience, the potential is there to make a series of choices that, in some instances, make errors more likely than they would be if considered in the context of a statistical analysis.

This tendency for an individual to make the same error in a predictable or repeatable manner is termed bias. Personal experience, beliefs, knowledge, data, and models all have inherent built-in factors, errors, or defects that create predictable error patterns when applied in decision-making. Walker (1998) argues implicitly that such bias can also be considered systemic error. This type of uncertainty is difficult to identify except on theoretical grounds when the magnitude and direction of the bias are known. Most of these biases, whether systemic or knowledge-based, individual or practice-based, require disciplined efforts to "validate" such methods, models, and judgments at all layers while bounding the uncertainty.
: This is the challenge to professional knowledge that must be overcome, namely, developing and validating models and methods for managing bounded uncertainty that minimize this tendency towards repeatable groups of error or bias. Therefore, from this point forward, the discussion will refer to 'uncertainty and its proxies, error, and bias.'
===Propagating Error and Uncertainty===
Partitioning uncertainty and its proxies, error, and bias into logically distinct subsets involves or is associated with multiple-step execution or choice frameworks. Combining those measurements or choices into single parameters or choices involves, in some cases, assembling the data/information in a unique or limited range of sequences. This implies that associated errors in observing, calculating, or choosing will have unequal impacts or consequences depending on where in the chain the error occurs. Also implied in this is the possibility that subsequent errors will be causally linked to some degree. (In statistical practice, this depends upon whether the errors are independent of each other or correlated, specifically, co-variant.)
:'''Professions develop knowledge of how uncertainty and its proxies, error, and bias propagate through an ordered and normative series of observations or choices. This allows the profession to prioritize error and bias reduction to achieve an optimized reduction of uncertainty.'''
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===Cognition, Metacognition and Uncertainty===
The concept of choice and the resultant error in uncertainty can be further delimited depending on the role of [https://en.wikipedia.org/wiki/Cognition cognition], [https://en.wikipedia.org/wiki/Metacognition meta-cognition], and independent external elements. The rationale for this argument is that such choice activity in the face of uncertainty is a conscious, cognitive act.

An argument could be made that such cognition is a prerequisite for exercising choice and introducing error, as presented and discussed above. The counterfactual to this argument is the example of uninformed choice. Choices introduce as much as errors or more as informed choices could be made. (???) Choice, in this sense, is indifferent to knowledge. Purposeful choice in an environment of objectives requires knowledge, method, and experience, but uninformed, speculative choice does not.
:"''' Cognition''' is the set of all mental abilities and processes related to knowledge, attention, memory and working memory, judgment and evaluation, reasoning and "computation," problem-solving and decision making, comprehension and production of language, etc. Human cognition is conscious and unconscious, concrete or abstract, as well as intuitive (like knowledge of a language) and conceptual (like a model of a language). Cognitive processes use existing knowledge and generate new knowledge." (Wikipedia)
:"'''Metacognition''' is "cognition about cognition", or "knowing about knowing" and can take many forms. It includes knowledge about when and how to use particular strategies for learning or problem-solving. There are generally two aspects of [https://en.wikipedia.org/wiki/Metacognition metacognition]: knowledge about cognition and regulation of cognition."
Some types of metacognition knowledge are:
:'''Person knowledge''' (declarative knowledge), which is understanding one's own capabilities.
:'''Task knowledge''' (procedural knowledge), which is how one perceives the difficulty of a task, which is the content, length, and type of assignment.
:'''Strategic knowledge''' (conditional knowledge) is one's capability to use strategies to learn information. (Accessed at Wikipedia)
Like metacognitive knowledge, metacognitive regulation or "cognitive control" contains three essential skills.
:'''Planning:''' refers to the appropriate selection of strategies and the correct allocation of resources that affect task performance.
:'''Monitoring:''' refers to one's awareness of comprehension and task performance.
:'''Evaluating:''' refers to appraising the final product of a task and the efficiency at which the task was performed. This can include re-evaluating strategies that were used. (Accessed at Wikipedia)
Nothing has been said up to this point that limits this structure to an individual, or a collective, or two entities making cognitive choices that are incompatible or inconsistent to varying degrees. Interpretations of this scheme include an individual professional exercising judgment in a decision that, in turn, relies upon the collective judgment and decision of the discipline as an underlying basis versus the decision of another individual to oppose/protest or otherwise contest that decision. Thus, the framework of cognitive/meta-cognitive applies to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, economics of regulatory practice, and game theory.
:By solving useful problems in the face of inherent uncertainty, discipline-specific frameworks of cognitive/meta-cognitive activity apply to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, the economics of regulatory practice, and game theory.
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===Probability and Uncertainty===
Probability is a [https://en.wikipedia.org/wiki/Coherence_theory_of_truth coherent] approach to uncertainty in the physical and mathematics or "classical" domains such as engineering. [8] and is the "...is certainly the best-known and most widely used formalism for quantifying uncertainty.[9] [https://en.wikipedia.org/wiki/Cox%27s_theorem Cox’s theorem] ("Any measure of belief is [https://en.wikipedia.org/wiki/Isomorphism isomorphic] (but not necessarily equal) to a probability measure") is a well-known argument for the validity of that argument. [Note 5]

Another writer, [https://en.wikipedia.org/wiki/Frank_Knight Knight] (1921,1956), presented the following taxonomy of probabilities: [10]
: '''Classical 'a priori' probability:''' As an idealized model, numerical probabilities are computed based on generalized principles of assigning equal likelihoods and mutually exhaustive possible outcomes (Runde, 1998) to all set members. Such probabilities are assigned to "...outcomes based on a judgment of indifference between those outcomes, that is, based on the absence of any evidence of real influences in play that may render any one outcome more or less probable than any other." (Runde, op. cit.) This builds upon Knights's concept of an 'absolutely homogeneous classification of instances that are completely identical except for [https://en.wikipedia.org/wiki/Indeterminate_(variable) indeterminate] factors. This judgment of probability or logical probability is on the same logical plane as the propositions of mathematics and ultimately [https://en.wikipedia.org/wiki/Inductivism inductions] from experience.'(Knight, 1956, pg.225)
: '''Bayesian 'a priori' probability:''' In contrast to interpreting probability as the "frequency" or "propensity" of some phenomenon, [https://en.wikipedia.org/wiki/Bayesian_probability Bayesian probability] is a quantity that we assign to represent a state of knowledge or a state of belief. In this view, probability is assigned to a hypothesis, often using the basis of past or prior experience. In contrast, under the frequentist view, a hypothesis is typically tested without being assigned a probability based on prior experience. The Bayesian interpretation of probability can be seen as an extension of propositional logic that enables reasoning with hypotheses, i.e., propositions whose truth or falsity is uncertain. Bayesian probability belongs to the category of evidential probabilities; to evaluate the probability of a hypothesis, the Bayesian probabilist specifies some prior probability, which is then updated in the light of new, relevant data (evidence). The Bayesian interpretation provides a standard set of procedures and formulae to perform this calculation. [11]
: '''Statistical or 'a posterior probability:''' Empirical evaluation of the frequency of association between predicates, not analyzable into varying combinations of equally probable or logical, 'a priori' alternatives. Knight argued that any "high degree of confidence" that the judgment that experience will remain valid for future predictions is "...still based on an 'a priori" judgment of indeterminateness." (Knight, Op. Cit.) Knight's argument to support this is what will be discussed further below that first, "...the impossibility of eliminating all factors not really indeterminate; and, second, the impossibility of enumerating the equally probable alternatives involved and determining their mode of combination so as to evaluate the probability by a priori calculation." (Knight, Op. Cit.) Knight noted that the main difference between this form and that of logical or inductive probability was the presence of empirical information, which allowed the analyst to identify propensity and direction in the observed phenomena. In this case, 'a priori' probability values may be derived from first principles and "statistical probabilities are determined a posteriori by the empirical method of counting instances." (Runde [1998] quoting Knight, op. cit.)
:'''Estimates:''' The distinction here is that there is no valid basis ('a priori' or 'a posteriori') for any classifying phenomena. For Knight, this form of probability presented the greatest logical difficulties of all ... but its distinction from the other types must be emphasized, and some of its complicated relations indicated..." (pp. 224-5, emphasis in the original)

Arguably, Knight's concept of probability can be viewed as "..a continuum of probability situations, depending on the degree of homogeneity of the 'instances' in question" [12] ; i.e. going from a logically distinct but equally likely set of elements to a unique set with one member with statistical frequency in the middle. Knight's main motivation for distinguishing between a priori probability and statistical probability underscores his opinion that "(i) the 'mathematical or a priori type of probability is practically never met with in business, while the second is extremely common'; and (ii) 'the statistical treatment never gives closely accurate quantitative results' (Runde quoting Knight pp. 215-16). Runde argues that Knight couldn't conceive of (empirically tabulated) occurrences that are used in daily commerce classes of instances that we have to make do within the course of everyday economic life could be divided into subclasses of instances that are sufficiently homogeneous to permit the determination of what he calls 'real' probability (p. 217).
:'''In modeling degrees of uncertainty, any measure of belief is isomorphic but not necessarily equal to a probability or statistics measure'''.
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===Explanation, Prediction and Uncertainty===
It should be clear that the general notion of uncertainty synonymous with complete ignorance must be bounded as part of a scheme to produce a useful concept of uncertainty in professional practice such as civil engineering.
:The first principle that must be introduced is that such uncertainty must be bounded by reliable and valuable knowledge gained from past experience combined with analytical and computational capabilities to address an immediate and real problem of interest. This could be viewed as the economic interest argument. Only uncertainties associated with physical phenomena and economic scarcity will be addressed.
:The second principle is that the knowledge model is capable of producing statements or assertions in the form of hypotheses that themselves are testable. [https://en.wikipedia.org/wiki/Testability Testability], in this sense, is defined as the property applying to an empirical hypothesis and involves two components:
::The logical property that is variously described as contingency, defeasibility, or falsifiability, which means that counterexamples to the hypothesis are logically possible. Contrast this to a [https://en.wikipedia.org/wiki/Tautology_(logic) logical tautology], which is always true that is true in every possible interpretation. The practical feasibility of observing a reproducible series of such counterexamples if they do exist. In short, a hypothesis is testable if there is some real, non-zero expectation of deciding whether it is true or false of verifiable, reproducible experience. Upon this property of its constituent hypotheses rests the ability to decide whether a theory can be supported or falsified by actual experience data. (Source Wikipedia.)
'''Knowledge models must be capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria described above in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error and bias.'''
===Variability and Aleatory Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge referred to as epistemic uncertainty. Acting in the face of epistemic uncertainty introduces choice uncertainty (discussed below) and results in the production of explanations and predictions. It is implied that there will be testable hypotheses in the form of outcomes of interest to the knowledge professions. Inherent in nature is a variation of results, which may appear to uncertain but isn't. Observations of phenomena will not be the same, and part of the experimental process is to narrow those results down to within an acceptable level of tolerance. Statistical measures seek to identify underlying and unobserved measures of central tendency. These parameters are not directly observed but are derived to a degree of confidence.
In some cases, variation around a mean or within a statistical range may be perfectly acceptable to the knowledge profession. Examples of this are in the physical sciences and medicine. In Engineering and, to a lesser extent, economics, the process or phenomenon may be required to be more controlled to a narrower range than what a natural or "unmanaged" amount of variation would allow. Several courses of action present themselves.
:The first would be to ignore the aleatory phenomenon and choice, which is rejected outright.
:The second would be to assume that, in effect, such variation is itself a form of minimally managed uncertainty and treat it the same as epistemic uncertainty. This is not a desirable outcome as reducing Aleatory error and bias requires
:A third approach would be to acknowledge that process variation is, in some forms, manageable. This means making choices and acting in a way that presumes that the natural occurrence of error can be reduced, or more importantly, bias can be reduced. There is ample precedent for the third approach in civil engineering design and project management. In this sense, the argument is made that the aleatory response to managed efforts results in an elastic variation range. Presumably, an effective management effort either "shifts" the central tendency of the process results or reduces the variation range of occurrence or some other population parameter. Choosing to act and accept that narrower range rather than the broader natural variation may result in an outcome that, while well within the bounds of the expected variation, is outside the arbitrary control limits established by the choice and action. While this phenomenon mimics the broader uncertainty and its proxy error, it is not an error. The information on the variation was known, and the ability to produce narrower results was an error, albeit a pseudo-error, when compared to the broader classes of uncertainty discussed above, such as total ignorance.
::Looking at uncertainty using this third approach means that the error in choosing a new target parameter for this process may contain epistemic errors (our knowledge of the phenomenon may be erroneous, or the model flawed), aleatory uncertainties as discussed, or, more importantly, choice error or bias in thinking that we can move the process results to meet the requirements.
:'''In civil engineering practice, this may mean applying professional judgment in developing partitioning schemes or allocating uncertainty between the three dimensions (Epistemic, Aleatory, and Behavioral) of uncertainty.'''
Lastly, raising the topic like this brings the questions of effectiveness and efficiency to the fore. Advancing knowledge models that reduce error and bias at first are largely choice models resulting in justifications and favorable experiences. At some point in the process, the knowledge profession advances explanations and predictions that require reliable mathematical or logical concepts that are reduced to models and parameters. The question of efficiency becomes important; the broader uncertainty may have been reduced, but the process is economically inefficient. The profession is urged to become more frugal or, for example, reduce the variability of the outcomes. Knowledge professions often operate under conditions that require them to acquire knowledge and reduce uncertainty effectively and efficiently. Doing so requires understanding both epistemic uncertainty, as discussed above, and statistical process variation or aleatory variation.
:'''Knowledge professions manage and reduce uncertainty in the form of error and bias in an economically effective and efficient manner. This effort requires understanding both epistemic and aleatory uncertainty.'''
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===Choice and Behavioral Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge, referred to as epistemic uncertainty, as well as the inherent variations in results, or what was termed aleatory uncertainty. Acting in the face of that epistemic and aleatory uncertainty introduces yet another uncertainty into the process, namely choice uncertainty. This uncertainty reflects uncertainties associated with first-person and third-person choice. This is particularly relevant for engineering with project stakeholders.
===Summary of Semantics Framework for Uncertainty===
Professional disciplines have gradually re-framed their understanding of uncertainty and, thru experience and analysis, have arrived at the following meta-knowledge concepts of uncertainty:
* '''Uncertainty and its proxy, error, have three components: [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic epistemic uncertainty], [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic aleatory uncertainty], and behavioral uncertainty.'''
* '''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty using non-dogmatic beliefs, which recognize the existence of specific criteria and information that would make the believer change their beliefs using coherent approaches such as probability.'''
* '''Knowledge professions develop knowledge of how uncertainty and its proxy, error, propagate through an ordered and normative series of observations or choices that meet testability criteria in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error. An error can then be described, explained, and reduced in ways that uncertainty cannot. This allows the professions to prioritize error reduction to achieve an optimized reduction of uncertainty and produce a "legitimacy" from the fruitfulness of its use.'''
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===Uncertainty versus Risk/Hazard===
Up to this point, nothing has been said about risk or hazard. Now, risk can be defined. Simply put risk or hazard are finite, logically distinct subsets of the broader uncertainty. Risk and hazard are currently defined interchangeably. First and foremost, risk is a specific subset of general uncertainty. Any arbitrary subset of general uncertainty can be defined as a risk if it meets the following criteria:
* Uncertainty is associated with the potential for material error when acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena.
* Uncertainty and its proxy, error, can be partitioned into logically distinct subsets associated with multiple-step execution or choice frameworks in physical environments linked to a unique or limited range of sequences.
**Subcriteria 1:Resulting errors will have varying impacts or consequences depending on where the error occurs and the degree of causal linkage or influence in the chain.
**Subcriteria 2:Potential errors can be sequenced and prioritized to achieve an optimized reduction of error or bias and, by proxy, uncertainty over time.
* Uncertainty and its proxies, error bias, and environments are isomorphic to coherent approaches such as probability.
* Uncertainty and its proxies, error, and bias are isomorphic to coherent approaches such as knowledge models capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria in 'reasoning under uncertainty.'
This is a definition of general risk. There is no differentiation for engineering or economics, medicine or insurance. There is no difference between a 'good' risk consequence and a 'negative' one. Redefining general risk into simpler terms gives the following:
:'''General Risk is a subset of general uncertainty that is associated with the potential for material errors and biases when sequentially acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena; isomorphic to probability measures and methods and capable of testability.'''
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==Logical Framework for Specific Risk Taxonomies==
As noted above, at the most fundamental level, uncertainty is ".....the subjective perception of ignorance." [13] [Note 6] At this point in the discussion, the focus has moved from discussing uncertainty to its framed subset, risk.
:'''Further, the discussion moves from general risk to a professional or discipline-specific risk [https://en.wikipedia.org/wiki/Taxonomy taxonomy]. Beyond this point, risk will be used in place of uncertainty.'''
Han et al. note that, in general, "(t) taxonomies are valuable not only in their comprehensiveness but in their coherent reduction of uncertainty to conceptually discrete elements."
Uncertainty (and, by implication, risk or hazard) taxonomies offer an approach or tool to more precisely identify and define ontological uncertainty so that it can be quantified, analyzed, and communicated. Viewed this way, uncertainty is not a single, monolithic phenomenon but "...multi-dimensional with theoretically distinct domains and constructs that are potentially measurable and related to different outcomes, mechanisms of action, and management strategies." [14] Taxonomic schemes for classifying the different kinds of scientific and, by extension, engineering uncertainty require identifying the various kinds of potential error associated with descriptive scientific or engineering information and knowledge. An example of this is the following quote from a legal authority:
:"I would especially stress the need for an agency to disclose the uncertainty that surrounds its determinations. '''And by uncertainty, I mean the agency's ignorance as well as its quantitative estimates of error."''' (Emphasis added) [15]
'''Given the necessity of acting in the face of enormous uncertainties [16], discipline taxonomies must offer a clearly coherent and probabilistic approach to uncertainty that is sufficiently general in nature, logically distinct and exhaustive in scope and "...provide decision-makers with a foundation for understanding the nature of discipline information..."''' [17]
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=== Scientific Risk Taxonomies ===
The [https://en.wikipedia.org/wiki/Scientific_method scientific method] is a body of techniques for investigating phenomena in its environment, acquiring new knowledge, or correcting and integrating previous knowledge. Inherent in that process are uncertainties of many forms. Any one taxonomy of [https://en.wikipedia.org/wiki/Uncertainty scientific uncertainty] has largely been focused on statistical models used to assess and quantify sampling, errors, and parameters, although several different taxonomies are conceivable: note-24 [18]
====Environment-specific, Phenomena oriented, model-centric taxonomy of uncertainties====
** [https://en.wikipedia.org/wiki/Uncertainty_quantification#Sources_of_uncertainty '''Parameter uncertainty'''] the source of which is model parameters that are inputs to the computer model (mathematical model) but whose exact values are unknown and cannot be controlled, or whose values cannot be exactly inferred by statistical methods. Subsets of this type of uncertainty include but are not limited to:
***'''Experimental uncertainty''' is also known as observation error, which comes from the variability of experimental measurements. An example is repeating an experiment measurement several times using exactly the same settings for all inputs/variables and recording the variability. It is a subset of the larger uncertainty component, parameter uncertainty.
*** '''Parametric variability''' comes from the variability of the input variables of the phenomena model and is also a subset of the larger uncertainty component, parameter uncertainty.
** '''Structural uncertainty''', or model inadequacy, model [https://en.wikipedia.org/wiki/bias bias] or "[https://en.wikipedia.org/wiki/Systemic_bias systemic bias]", or model discrepancy, which comes from the lack of knowledge of the underlying true state of the phenomenon or its environment. It depends on how accurately a mathematical model describes the true state of the phenomenon or what is termed "model fitness". Due to the inherent nature of uncertainty in any knowledge body (scientific or engineering), models are only an approximation to reality. Therefore, any conclusions drawn from the model can be very misleading when the underlying basis is not plausible or lacks validity. '''note-25 [19]]'''
*** What is missing in this context is any concept of output variability due to the model itself. Part of this is due to the lack of recognition of "process" in scientific investigation. The concept of the scientific method started with a single investigator, such as Galileo or Michael Faraday, working in their laboratories. It has grown into planet and solar system scale experiments such as data gathered on planetary flybys such as Mars, Pluto, and Ceres to the hunt for the Higgs Boson. Ultimately, science has become more like engineering in the scale and complexity of its investigations and theory aggregates, such as the "Unified theory" of particle physics. Subsets of this type of uncertainty include but are not limited to:
*** '''Algorithmic uncertainty''', or numerical uncertainty, comes from numerical errors and numerical approximations per implementation of the computer model. Most models are too complicated to solve exactly. For example, the [https://en.wikipedia.org/wiki/Finite_element_method finite element method] or [https://en.wikipedia.org/wiki/Finite_difference_method finite difference method] [may be used to approximate a solution [https://en.wikipedia.org/wiki/Partial_differential_equation partial differential equation], which introduces numerical errors. Other examples are numerical integration and infinite sum truncation, which are necessary approximations in numerical implementation.
*** '''Interpolation uncertainty''' comes from a lack of available data collected from computer model simulations or experimental measurements. For other input settings that don't have simulation data or experimental measurements, one must interpolate or extrapolate to predict the corresponding responses.
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====Commentary====
A more comprehensive taxonomy of uncertainties that acknowledges knowledge, data, and linguistic uncertainties is:
*'''Epistemic uncertainty''' -[https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Epistemic uncertainty] or systematic uncertainty, in contrast, reflects limitations in the current “state of knowledge” underlying models themselves, originates from competing theories or models, is not readily quantifiable, and is manifest by subjective confusion or indecision. It includes uncertainty due to measurement limitations, insufficient data, extrapolations and interpolations, and variability over time and space. Epistemic uncertainty is uncertainty about "...some determinate fact ... because of a lack of complete information. '''cite_note-26 [20]]''' Epistemic uncertainty can be classified into six main types:'''cite_note-27 [21]]'''
*''' Measurement error''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Observational_error Measurement error] is uncertainty that manifests itself as (apparently) random variation in the measurement of a quantity. Repeated measurements will vary and demonstrate classic statistical behavior.
* '''Systematic error''' - [https://en.wikipedia.org/wiki/Observational_error Systematic error] occurs due to bias in the measuring equipment, model, analysis, observation, or sampling procedure. It is formally defined as the difference between the true value of the quantity of interest and the value to which the mean of the results converges as sample or data sizes increase. Unlike measurement error, it is not (apparently) random, and therefore, results subject to systematic error alone do not vary about a true value. Systematic error can result from deliberate judgment to exclude (or include) data, parameters, or models that ought not to be excluded (or included).
** "The only way to deal with systematic error is to recognize a bias in the process, model, or procedure and remove it. Systematic error, however, is notoriously difficult to recognize except on theoretical grounds. Corrections may only be applied when the magnitude and direction of the bias are known. Such corrections underlie the application of double-sampling methods in environmental science." (Regan, et. al., op. cit.)
*'''Natural variation''' - [https://en.wikipedia.org/wiki/Natural_process_variation Natural variation] or underlying process variation occurs in systems that "...change (concerning time, space, or other variables) in ways that are difficult to predict...across the full range of temporal and spatial values (or other related variables)." '''note-28 [22]'''
** This form of uncertainty is reduceable utilizing tools such as [https://en.wikipedia.org/wiki/Statistical_process_control statistical process control] and replication of experiments or observations.
*'''Inherent randomness''' - [https://en.wikipedia.org/wiki/Randomness randomness] or [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty aleatory uncertainty] exists because the system is"...in principle, irreducible to a deterministic one (the most well-known case is described by [https://en.wikipedia.org/wiki/Uncertainty_principle Heisenberg’s uncertainty principle] in quantum mechanics)." '''note-29 [23], note-30 [Note 7]'''
** Even if the argument is accepted that it is unlikely that any given physical system at some level is inherently random, it is important for any taxonomy to distinguish between systems that appear random due to incomplete or inconsistent information and those that are intrinsically random. (Again, see Regan (2002)).
*'''Model uncertainty''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling Model uncertainty] occurs in the process of generating a model as a conceptual representation of a particular phenomenon to create a [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling#Overview simplified reflection of reality]. This selectivity of factors and variables creates uncertainty in at least three ways. (Regan (2002))
** '''Variables and processes''' that are regarded as relevant often represent a trade-off between system knowledge and assumed states and model objectives;
** '''Models''' depict observed processes using logical or mathematical constructs based on underlying theories about system states or dynamics using continuous equations to describe discrete processes.
** '''Curve fitting''' (including interpolation and extrapolation) with mathematical expressions using model variables where inputs are discrete data points.
** Regan (2002) argues that '''model uncertainty''' is "...notoriously difficult to quantify and impossible to eliminate ...(and)...(t)he only reliable way of determining how appropriate a model is for prediction is to perform validation studies."
** Subjective judgment occurs due to the interpretation of scarce or error-prone data.
*** The only way to address this type of uncertainty is to "...assign a degree of belief about an event in the form of a subjective probability." (Regan (2002))
*Linguistic uncertainty - Linguistic uncertainty, or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. '''note-31 [24]], note-32 [25]]'''
** '''Context dependence''' is uncertainty concerning the context in which a statement is to be understood;
** '''Ambiguity''' occurs when words have multiple meanings, and further reduction to a single meaning is not logically possible in a given context.
** '''Underspecificity''' occurs in the presence of "unwanted generality" or multiple interpretations, and reduction to a smaller set or even a single interpretation is not logically possible.
*Stochastic or statistical uncertainty - Stochastic or statistical uncertainty, which is sometimes known as [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Aleatoric uncertainty] as well as the subject of [https://en.wikipedia.org/wiki/Stochastic_control stochastic control] pertains to the parameters of a risk model, originates from sampling or measurement error, and can be quantified and mathematically expressed (e.g., using confidence intervals).
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== Uncertainty Taxonomies in a Legal Environment ==
From a legal perspective, a definition of taxonomy is dictionary-based, such as "the systematic distinguishing, ordering, and naming of type groups within a subject field." given by Walker (op. cit.). Such legal concepts of scientific or engineering taxonomies are not required to be epistemically complete. Still, they must cover "the most significant aspects of ...(current, good discipline practices) ... and of the descriptive information commonly encountered by decision-makers" in a logically distinct manner. (Walker, 1991, p. 571) These taxonomies must be sufficient to catalog the types of uncertainty associated with descriptive assertion, including the critical subset of cause and effect assertions. This legal/decision-maker framework has presented descriptive uncertainty as having six components (Walker, 1991). Although not part of the Walker taxonomy, underlying the scheme is linguistic uncertainty, or "vagueness," as a source of uncertainty, including uncertainties due to context dependence, ambiguity, and under-specificity. '''note-33 [26]]''' Arguably, these uncertainties underlie the most basic element in the taxonomy.
* '''Linguistic '''- or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. (Regan, 2002)
* '''Conceptual'''- the definition by choice and design of descriptive concepts or variables to be used as [https://en.wikipedia.org/wiki/Predicate_(grammar) predicates] where the subject of an assertion identifies what is being discussed and the predicate provides information about the topic or characterizations such as what the subject is, what the subject is doing, or what the subject is like (Wikipedia).
* "Conceptual uncertainty, or the potential for conceptual error, arises whenever predication occurs. Whenever a concept is used to describe something, using certain concepts instead of others begins to structure how we understand the object, event, or instance under discussion. Predication or conceptualization generates useful information about things, but it also can inhibit our ability to think about those things with concepts other than those selected. The concepts used may not be the most fruitful or the best designed- either for scientific purposes or for making wise, fair, effective, and efficient decisions." (Edited for reading, Walker, 1991)
* Similarly, choice and design of descriptive variables enhance and restrict data set membership, creating potentially incomplete, incompatible, or inconsistent data sets, "...the classification categories employed, and the relationships among those categories (nominal, ordinal or scalar)." '''note-34 [27]]'''
* Concept uncertainty '''note-35 [28]''' like systematic error discussed above is notoriously difficult to recognize, except on theoretical grounds, and the magnitude and direction of the bias are known.
Both terms could be analogized to that of [https://en.wikipedia.org/wiki/Accuracy_and_precision#Terminology_of_ISO_5725 accuracy] in [https://www.bipm.org/en/publications/guides/ ISO 5725] which looks at the ability of the concept or system's ability to produce results that are proximate to the "true" value or state of the phenomenon. Conceptual uncertainty and systemic bias are related to another measure of uncertainty: validity. Walker has defined validity as the ability of a concept to quantitatively describe and explain what the phenomenon objective "truly" is. Walker also ties the concepts together when he states, "Validity concerns the "accuracy" of the measurement data, not its precision." (Walker, 1998) 
Additionally, conceptual uncertainty and associated validity issues could be propagated throughout this taxonomy. Every component of action and decision in the face of uncertainty raises validity issues. Walker recognized this when the author defined epistemic choice for concepts used throughout the other uncertainty components or layers.
* '''Measurement''' uncertainty or Misclassification error- the application of the underlying concepts or variables to specific, individual cases and the uncertainty of the reliability (Walker, 1998) of the resultant data. (It is important to remember that assessment observation and measurement are used interchangeably in this analysis.) Thus, an assessment method or procedure is considered "reliable" or "precise" in the scientific terms of ISO 5715 if it repeatedly produces consistent results.
* Measurement uncertainty is logically distinct from conceptual uncertainty, which can only occur after any conceptual uncertainty has been established. Still, misclassification can occur even when the conceptual uncertainty has been minimized. Conversely, underlying errors or errors made in measurement or classification propagate thru the reasoning chain and reduce the quality or value of the final result.
* While uncertainties associated with quantitative measurements or assessments can be statistically evaluated and described, uncertainties related to qualitative assessments are more problematic.
**Working through the taxonomic chain from base concept to causal explanation requires amassing and integrating qualitative information and quantitative data into input parameters for mathematical models. Developing procedures for producing consistent, repeatable sets of information from assessments that minimize conceptual error or systemic bias requires professional efforts to "validate" such methods, models, and judgments at all layers in the process. Validation as a process is something that civil engineering as a practice needs to embrace more frequently.
* '''[https://en.wikipedia.org/wiki/Sampling Sampling]'''- in the classical statistics sense, is the selection of a limited subset of a larger population to use as a basis for making estimates about the population in the form of attributes, proprieties, or parameters. The resulting information would be determinative in making forecasts about future population sampling or possessing a desired amount of predictive power. The uncertainty about the ability of this information to correctly predict future samples is termed sampling error.
* '''Modeling'''- Modeling uncertainty arises whenever a claim is made that out of a class of candidate relationships and variable pairings, one variable pairing inclusive of constants is chosen that possesses a particular or persistent mathematical relationship to another variable X. Modeling errors arise in selecting the wrong variable pair and constants or by incorrectly specifying its constants. (Walker, 1991, pg. 599)
** '''Causal'''- In causal modeling and analysis, the relationship between causal variables is not strictly a mathematical function. Still, it makes testable predictions and explains "...how a system of variables works, why a system works the way it does, or why it makes sense to think of certain variables as a system" at all." (Walker, 1991, pg. 609)
** '''Epistemic'''- the choice of interpretations for fundamental, logical concepts used throughout the other components or layers.
The components of this uncertainty taxonomy can combine in different ways to "...produce different aggregate uncertainties." '''note-36 [29]'''
:''' Only the scientific and engineering professions possess the capacity to develop and validate procedures and models that combine the different kinds of qualitative '''note-37 [30]''' and quantitative information and their associated potential for error into an aggregate measure or "scalar variables" of uncertainty.'''note-38 [31]]
Scalar variables are quantitative variables whose categories are related by some measure of the relevant property's incremental frequency, degree, or amount. (Walker, 1991) Examples are Modulus of Elasticity, Compression stress in elastic materials, etc.

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=== Economics Uncertainty Taxonomies ===
Like scientific concepts of uncertainty, economics is environment-specific (in this case, read sector of economy or markets), phenomenon-oriented (in this case, read human activity and manufactured phenomena such as firms and markets guided by human judgment and decision), and model-centric.
:''' Economics is not interested in all forms of uncertainty, only specific, meaningful subsets possessing casual connections with material impacts, often labeled as 'risks'.'''
One of its earlier writers ([https://en.wikipedia.org/wiki/Frank_Knight#Life_and_career Knight], 1921, 1948, 1957) attempted to define "uncertainty" as the presence of "defects of managerial knowledge" (or [https://en.wikipedia.org/wiki/Knightian_uncertainty knightian uncertainty] and thus risk centric) as risk that is immeasurable, not possible to calculate.(Wikipedia) Risk in this sense was defined as "the ordinary risks of business activity which can ... be reduced... by applying the insurance principle." Knight does acknowledge that risk is a specific and unambiguous subset of uncertainty. (Risk, Uncertainty and Profit, 1957, pg. 19) For Knight, measuring uncertainty meant measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences, which Knight referred to as "the imperfection of knowledge". (Risk, Uncertainty and Profit, 1957, pg. 197) One of Knight's many contributions to this analysis was recognizing information and knowledge's role in economic activity.
:"''(W)e are concerned only to emphasize the fact that '''knowledge is in a sense variable in degree and that the practical problem may relate to the degree of knowledge rather than to its presence or absence in toto.''' We live only by knowing something about the future, while the problems of life, or conduct at least, arise from knowing so little. This is as true of business as of other spheres of activity. The situation's essence is action according to opinion, of greater or less foundation and value, neither entire ignorance nor complete and perfect information, but partial knowledge. If we are to understand the workings of the economic system, we must examine the meaning and significance of uncertainty; and to this end, some inquiry into the nature and function of knowledge itself is necessary.''" (Knight, pg. 199) Emphasis added
Knight also reinforced the difficulties in classifying uncertainties when he analyzed life insurance. In this case, accidental death was an uncertain phenomenon compared to sickness and accident, where an "...objective description and classification of cases was impossible..." (Knight, pg. 248) Another factor that made uncertainties ineligible as risks and, therefore, uninsurable because they were unclassifiable was the exercise of judgment in making decisions by the businessman. (Knight, pg. 251)
This is a simple classification of economic uncertainty and, as an idealization, is philosophically controversial. '''note-39 [32]'''
* Uncertainty and information about the economic environment are distinct from uncertainty about others’ behavior or choices.
* Risk as a specific subset of uncertainty implies that "...all possible acts are known, all possible outcomes arising from each act are known, and it is possible to assign probabilities to each act." '''note-40 [33]'''
Like the economic [https://en.wikipedia.org/wiki/Theory_of_the_firm theory of the firm], an economic theory of uncertainty seeks to explain and predict the nature of a specific subset of uncertainty (in this case, risk relating to physical phenomena and human activity), including its behavior, structure, and relationship to the universal uncertainty of physical phenomena. In this sense, uncertainty, like firms and markets, can establish price equilibriums under the right circumstances different from those that might have occurred in either of the other two. Such a theory of uncertainty offers a partitioning scheme that decomposes uncertainty into distinct and meaningful subsets, of which risk is a significant one. It also explains how the presence and variability of knowledge cause or " drive" economic consequences. Unlike physical phenomena, economic activity creates artificial objects such as products, firms, mediums of exchange, markets, economies, and knowledge about those activities. The economics of uncertainty exists as an alternative to "certainty" models that assume away imperfect knowledge and unknown choice/judgment preferences when it is more efficient for economic decision-making. This also allows the introduction of the logical concept of "hazard" versus "risk". Frank used hazard extensively in Risk, Uncertainty, and Profit, but not to the extent that one could say it was interchangeable. An example of this is in the term "[https://en.wikipedia.org/wiki/Moral_hazard Moral hazard]," where a firm could engage in "riskier" and potentially "uninsurable" behavior if the information were transparent, and, therefore, insurance coverage of losses is limited. note-41 [34]], note-42 [35]]

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=== Uncertainty and Risk in Policy and Regulatory Environment ===
:''"Hume's 'just reasoner', when faced with a difficult public policy decision in current times, would probably commission a risk analysis. But how could they incorporate 'a degree of doubt, caution, and modesty?"'' '''note-43 [36]'''
The problem of "decision-making in the face of uncertainty" is how to regulate based on incomplete information that has the potential to be materially inaccurate. A contextual assumption is that we need to evaluate decision rules for dealing with uncertainty. As a social enterprise, risk regulation, whatever its substantive objectives, should be as '''effective, efficient, and equitable''' as practically possible. These "three E's" form a set of "process objectives" or "meta-goals." From the uncertainty standpoint, causal information can be usefully divided into two major categories: information about groups and information about individuals.

Each category, group, and individual has its distinctive types of inherent uncertainty. These are logically distinct, generally independent, and cumulative, contributing to '''(?????)'''

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=== Engineering Uncertainty Taxonomies ===
Like scientific and economic concepts of uncertainty, engineering is environment-specific, phenomena-oriented, model-centric, external choice, and driven by decision-making in the face of uncertainty. Like economics and unlike science, engineering is not interested in all forms of uncertainty, only those with determinable and material consequences. Like economics, engineering conceives risk as a specific and unambiguous subset of uncertainty determined by measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences. Also, engineering recognizes the role information and, by extension, knowledge plays in constructing the built environment.
'''note-44 [37]]''' Like economics, engineering differs from economics and scientific uncertainty in its interest in behavioral uncertainty.
*"Behavioral or interaction uncertainty is how individuals or organizations act or interact. Behavioral uncertainty arises from four sources: design uncertainty, requirement uncertainty, volitional uncertainty, and human errors." '''note-45 [38]]'''
** A design uncertainty is a choice among alternatives over which an individual or group of individuals exercises direct control but has not yet decided upon.
** Requirement uncertainty includes parameters of interest to and determined by the stakeholder, independent of the engineer or designer.
** Volitional uncertainty is uncertainty about what the subject him/herself will decide. Other people’s future actions and conduct are not entirely predictable, particularly when dealing with other organizations.
** Human errors occur during the development of a system or project due to blunders or mistakes by an individual or individuals.

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==Practice Frameworks for Uncertainty and Risk in Civil Engineering Practice==
===PMIBoK===
PMI does not define uncertainty, but the PMI Body of Knowledge uses the term in 45 places (uncertainty) and as describing properties in 8 places (uncertainties). [See Note 8] PMI does define "risk" and uses the term over 1,300 times in the same document. In its glossary, PMI also defines terms such as "threat" and "opportunity" but not events.[391 The BoK contextually defines uncertainty when it states that project risk has its origins in the uncertainty present in all projects. (Op. Cit., pg 309) 
'''Uncertainty''' is contextually defined and taken as having the property of affecting project execution and the ability to meet stakeholder expectations. Uncertainty is more than the sum of all individual risks (known and unknown). The nature of this uncertainty is not defined, only its capacity to affect something else or its properties. 
'''Risk''' is defined as " ... an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives." (Ibid.) Beyond this formal definition, PMI adds context when, in its risk management section (Sec. 11 ), the BoK states that risk is a caused event or condition with multiple causes and impacts. The BoK even offers specific techniques for mapping cause-and-effect relationships and influence diagrams. (Sec. 11.2.2.5) Not as evident but equally important is the recognition that not only is risk caused but the impact is "triggered". The risk trigger is an event or situation that signals that it is about to occur (Glossary, pg. 566). Risk conditions are factors that affect or otherwise contribute to risk, such as project stakeholders. Risk implicitly retains some residual element of probability in it, or a value of less than 1.0 with risk that approaches a level of near certainty is termed "issues" or "realized risk" .(Op. Cit., pg 309). PMI also links risk to underlying variations in project outcomes. (Ibid.) Risk is based upon data and information which can be assessed as to its quality (Sec. 11.3.2.3). This analysis looks to determine the value of the information and examines the degree to which the risk information is understood as well as its " ... accuracy, quality, reliability and integrity ... " (Ibid.) Part of that is understanding the relationship between occurrence and impact as outlined in PMl's Figure 11-10 Risk Impact Matrix and developing a model for prioritizing risk products and setting the threshold for action.
====Commentary:====
For defining and identifying risk, PMI notes in Sec. 11.2.2.4 that " ... Every project and its plan is conceived and developed based on a set of hypotheses, scenarios, or assumptions." Part of the risk analysis process is identifying risks to the project, such as inaccuracy, instability, inconsistency, or incompleteness of assumptions. Similarly, the quality of management plans, as well as their consistency with others and the project objectives and assumptions are 11 •• .indicators of risk in the project." (Sec. 11.2.2.1) 
Similarly, the role of engineering economics in risk is embedded or implied in the PMI definition of risk when the BoK discusses risk identification as a process of assessing which risks out of the total risk exposure for the project "may affect" the project and distilling their characteristics. Embedded in this statement is the concept that not all risks can cause economic impacts (positive and negative) to the project. Also embedded is the requirement to understand risk characteristics to identify project risk (Cost and schedule). Lastly, risk identification is an iterative process of incrementing project risk documentation, much like project scope, cost, and schedule documentation. 
Lastly, PMI acknowledges the complex constitution of risk aggregates or the "sum" of all risks in its material, but is it a sum? Risk information has descriptive and explanatory content. These are knowledge and meta-knowledge components. Understanding project assumptions is project-specific knowledge, but identifying risks from assumption inaccuracy, instability, inconsistency, or incompleteness requires professional or program meta-knowledge. 
'''Uncertainty exists in all projects, but risk, a subset of uncertainty with material economic impacts, is a key meta-knowledge element for civil engineering practice.'''
===Software Development Process- Risk Focused===
The Unified Process requires the project team to focus on addressing the most critical risks early in the project life
cycle. The deliverables of each iteration, especially in the Elaboration phase, must be selected to ensure that the greatest risks are addressed first. <ref> Booch, Grady, Ivar Jacobson, and James Rumbaugh. "The unified software development process."
Reading: Addison Wesley (1999).</ref>
===American Society of Civil Engineers (ASCE)===
ASCE does not define uncertainty, but the ASCE CE Body of Knowledge (CEBoK) uses the term in 22 places (uncertainty) and describes properties in 13 places (uncertainties). 
The CEBoK does not use the phrase "uncertainty and risk" as PMI and AACE do in their publication. ASCE reverses the two (Risk and uncertainty) in twelve places regarding technical outcomes (Outcome 12). A fundamental difference between PMI and ISO is that risk is considered a subset of uncertainty. ASCE does not define "risk" but uses the term 25 times in the same document. Almost all are in the context of variation of design parameters and not in the classical context of cost, schedule uncertainty, and risk.
====Commentary:====
...
=== American Association of Cost Engineers (AACE) ===
AACE defined uncertainty, risk, and other related terms in its Risk Management Dictionary. <ref>Risk Management Committee uRisk Management Dictionary, Cost Engineering Vol. 37/No. 10, OCTOBER 1995. </ref>
AACE's overall strategy was to define risk and other terms that stem from base uncertainty. AACE first defined uncertainty as .... "(t)he total range of events that may happen and produce risks (including both threats and opportunities) affecting a project (see opportunities, events, conditions, risk, and threats" where the following sub-definitions apply:
** Biases--A lack of objectivity based on the individual's position or perspective. Systematic and predictable relationships between a person's opinion or statement and his/her underlying knowledge or circumstances. Note: there may be "system biases" as well as "individual biases."
** Condition (Uncertain Condition)-Any specific identifiable circumstance (such as the rate of inflation or the quality of labor available) that might affect the outcome of the project
** Event (Uncertain Condition)-is a specific identifiable action (such as a large government project being started in the same labor area as your project) or an act of nature that might happen and that (if it does happen) could affect the outcome of the project.
** Opportunities are Uncertain events that could improve the results or improve the probability that the desired outcome will happen
** Threats are Uncertain events that are potentially negative or reduce the probability that the desired outcome will happen.
*** AACE defines Risk as an "ambiguous term" (sic) that is synonymous with uncertainty or a negative subset such as "threats", or an overall negative impact of all possible uncertainties.
====Commentary:====
...
==Limitations of the definition==
...
==Working definition==
...
==Beneficial outcomes==
...
==See also==
Wiki articles on the project.

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==Notes==
<references group="note" />

# See McDaniels et al. 2004 for an extensive discussion and bibliography on the historical development of Risk Analysis and some key milestones in the 20th century.
# Open item
# The Merriam-Webster dictionary defines uncertainty:
::"the quality or state of being uncertain," which is a circular definition. Likewise, one could define uncertainty as the state of not being certain. Synonyms are distrust, doubt, misgiving, mistrust, reservation, skepticism, and suspicion. Another writer added indefinite, indeterminate, not certain to occur, problematical, unreliable, untrustworthy, unknown beyond doubt, dubious, doubtful, not clearly identified or defined, not constant, variable, and fitful to the list.
::Han, Paul KJ, William MP Klein, and Neeraj K. Arora. "Varieties of Uncertainty in Health Care: A Conceptual Taxonomy." Medical Decision Making 31.6, (2011 ): 828-838.
::: Han, ct. al. noted that any definition of uncertainty encompasses " ... numerous types, sources, and manifestations of uncertainty, and ... (any) ... useful working definition of uncertainty needs to specify the concept underlying these varied meanings of the term." (Ibid.)
<ol start="4">
<li> Implicit in this definition of uncertainty as a "state of' is ... </li>
: " ... a conceptualization of uncertainty as a subjective, cognitive experience of people--a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one's lack of knowledge, without which one could not feel uncertain; '''uncertainty is a form of "meta-cognition" ... ( or alternatively, its main component, meta-knowledge) ... -a knowing about knowing.'''" (Han, 2011, op. cit., Emphasis added)
</ol>
<ol start="5">
<li> Cox wanted his system to satisfy the following conditions:</li>
: Divisibility and comparability- The plausibility of a statement is a real number and depends on the information we have related to the statement.
: Common sense - Plausibilities should vary sensibly with the assessment of plausibilities in the model.
: Consistency - If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). -preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf
</ol>
<ol start="6">
<li> The utility of existing taxonomies of uncertainty in civil engineering and, by extension, "risk" has been unsatisfactory for this very reason. This is particularly true in cases where the source of the uncertainty is conflicting versus incomplete information. See Regan, et. al.,"A taxonomy and treatment of uncertainty for ecology and conservation biology" (2002)
for a survey of scientific taxonomies for uncertainty.</li>
<li> Regan et al. argue that " ... genuine examples of this kind of uncertainty are hard to find. Even classic cases of random experiments like coin tosses and the throwing of dice are deterministic; it is just that we do not have enough information about the dynamic processes and initial conditions to make any sensible estimates about the outcomes. Such processes are, for all intents and purposes, inherently random, but they are not genuinely inherently random. For similar reasons, complex systems such as ecosystems and weather patterns are unlikely to be inherently random. Similarly, chaotic systems are entirely deterministic. They are unpredictable because the deterministic processes generating them and the relevant initial conditions are hard
to fully specify (see Stewart 1989, Sugihara et al. 1990)." (Regan (2002) </li>
<li> The CEBoK uses the phrase "uncertainty and risk" twice in reference to scheduling (Section 6.5,2) and cost estimating (Section 7.2,2) as well as reversing the two (Risk and Uncertainty) also in two places in discussing project life cycle (both in Section 2.4.1) </li>
</ol>

==References==
<references />

Uncertainty and Risk in Civil Engineering Practice

2024-11-24T16:02:51Z

WikiSysop:

'''Life is short and the art long; the occasion instant, experiment perilous, decision difficult.''' (Hippocrates as quoted in Fox,)
:''See also [[Project Definition]] , [[Project Life Cycle and Phase Models]] , [[Project Delivery Methods]]''
:''See also [[Technical Outcomes for Civil Engineering:Risk and Uncertainty]]''
==Basic Considerations==
As human beings,'' ... being alive means seeking opportunities and taking risks.'' As knowledge professionals
living in the 21st century, this means coping with an increasingly complex number of uncertainties for humans
living in this environment. We seek to understand better how these uncertainties can be characterized and
managed. The essence of this article and the experience for engineers in general and civil engineers in particular is
that manageable uncertainty is, by definition, termed risk, and its kindred cousin is termed hazard. This causes us to experience the
:" ''... human dread of and fascination for risk and the increasingly important role of risk analysis within societies ...'' " <ref name="McDaniels"> McDaniels, Timothy, and Mitchell Small. Risk analysis and society: an interdisciplinary characterization of the field. Cambridge University Press, 2004, page 1 </ref> (Ibid.)
and, by extension, civil engineering. McDaniels et al. argue that risk management has been
fundamental to our social and governance development for the past 10,000 years. The scale and shape of the
uncertainties faced in this period shaped the societies that have developed today. The central thrust of this effort
over the centuries has been to reshape and re-frame our understanding and conception of uncertainty from one of
complete unknowing and simple acceptance as our fate in life to one of management (cf "''Against the Gods''"
concept in Bernstein's book <ref name = Bernstein">Bernstein, Peter L., and Jesse Boggs. Against the gods. Simon & Schuster 1997.</ref>.
: ''All the knowledge professions and disciplines have struggled with managing uncertainty, for it is impossible to manage unbounded uncertainty.'' <ref group="note">See McDaniels et al. 2004 for an extensive discussion and bibliography on the historical development of Risk Analysis and some key milestones in risk analysis in the 20th century.</ref>
As outlined below, professions such as civil engineering have successfully acted in the face of uncertainty. It lies in the profession's ability to reduce a wide variety of uncertainties into increasingly smaller and crucially bounded subsets that can be managed. These are called 'risks. More recently, this process has evolved, and individual disciplines such as Civil Engineering (CE) have developed their knowledge and models to perform risk analysis. Risk is also taught as a distinct discipline and specialty practice within civil engineering. Still, it has some unique features that set it apart from other more classical practices within CE. As such, it is one of the first of some very specialized CE practices that use knowledge about civil engineering knowledge, or meta-knowledge. Other examples of civil engineering metaknowledge are project controls and quality controls.
==Semantic, Epistemic and Logical frameworks==
The semantic, epistemic, and logical frameworks for uncertainty and risk have several dimensions and layers of logical frameworks. The semantics problems include interchangeable usages for risk and uncertainty and hazard, uncertain and imperfect, and a lack of definitional material or context. The semantical scheme for this article will be to proceed from the distinction between certainty and uncertainty through marginal refinements and reductions of uncertainty up to the point of causal uncertainties. Beyond this point of semantics will be the logic frameworks or the subject of taxonomies of professional knowledge. First, it is about simple, testable phenomena, and then, it moves on to complex taxonomy schemes for artificially constructed phenomena or engineering projects.
<ref> Bernstein, Peter L., and Jesse Boggs. Against the gods. Simon & Schuster Audio, 1997. </ref> <ref group="note">TBD</ref>

==Semantics framework==
===Certainty and Uncertainty===
''''' There is no such thing as absolute certainty, but there is assurance sufficient for the purposes of human life.''''' ([https://en.wikipedia.org/wiki/John_Stuart_Mill John Stuart Mill]) 
'''''If you tried to doubt everything, you would not get as far as doubting anything. The game of doubting itself presupposes certainty.''''' ([https://en.wikipedia.org/wiki/Ludwig_Wittgenstein Wittgenstein] # 115 from On [https://en.wikipedia.org/wiki/On_Certainty Certainty]) 
It is important to note that the references to epistemic or epidemiological knowledge in this article are assumed to relate to three forms of knowledge, namely:
* Knowledge that ( [https://en.wikipedia.org/wiki/Descriptive_knowledge descriptive or declarative or propositional knowledge])
* Knowledge how ( or "[https://en.wikipedia.org/wiki/Procedural_knowledge know-how]"), and
* [https://en.wikipedia.org/wiki/Knowledge_by_acquaintance Knowledge by acquaintance].
'''Certainty''' has been defined as "an epistemic property" of knowledge in all its forms and the state of our beliefs about that knowledge.
<ref group="note">Certainty, Stanford Encyclopedia of Philosophy, accessed on September 12, 2015, at http://plato.stanford.edu/entries/certainty/#ConCer </ref>
Certainty about any belief about knowledge implies that it is not subject to doubt or skepticism. This immunity to criticism can be dogmatically based, emphasizing the importance of a propositional-based sense of truth over experiential, sensory perceptions. <ref name="Note4"/>
: '''The demarcation line between dogmatic and non-dogmatic beliefs lies in the presence and recognition of specific criteria and information that would make the believer change their beliefs.'''
An empirical framework of [https://en.wikipedia.org/wiki/Testability testability] and [https://en.wikipedia.org/wiki/Falsifiability falsification] is required to recognize this and limit dogmatism. For example, one could argue that people would change their minds if God asked them to. Similarly, one could construct a concept of epistemic as opposed to dogmatic belief certainty as the ability to know anything that one chooses to know and can be known or [https://en.wikipedia.org/wiki/Omniscience inherent omniscience].

A second kind of certainty is epistemic, when conviction reflects the highest possible support for a belief. In this sense, knowledge is separate from beliefs, although someone may have beliefs about a property, such as certainty of knowledge. Logically, it has been shown that there will be unprovable statements within the system for any such system of knowledge. Secondly, the knowledge system cannot demonstrate its own consistency. ([https://en.wikipedia.org/wiki/Gödel%27s_incompleteness_theorems Gödel's incompleteness theorems])
: '''Certainty, in real life, is useless or often damaging (the idea is that "total security from error" is impossible in practice, and a complete "lack of doubt" is undesirable)''' ([[https://en.wikipedia.org/wiki/Certainty Physicist Carlo Rovelli]])
'''Uncertainty''', on the other hand, arises immediately in the slightest amount of doubt or criticism. <ref name="Note3">[Note 3]</ref> Similarly, a general definition of uncertainty could be given as "...any departure from the unachievable goal of complete [https://en.wikipedia.org/wiki/Determinism determinism]." <ref name="Reference5">[5]</ref> Reasoning under uncertainty is very different than performing the same under certainty. In reasoning under certainty, one has complete knowledge and deduces without doubt and equally important, without limitation, thereby concluding free from error. Reasoning under uncertainty, one works in a state of incomplete, inconsistent, and limited knowledge; doubts cloud any statements or assertions and thereby taint any deductions/inferences resulting in the potential for error. <ref name="Note4">[Note 4]</ref>
:'''Knowledge professions reason in a state of incomplete, inconsistent and limited knowledge with doubts that cloud any statements or assertions and taint any deductions/inferences resulting in the potential for error.'''
:''' It is also important to note the semantical and logical correlation between 'reasoning under uncertainty' or 'acting in the face of uncertainty' with the 'potential for' or 'presence of' error. The presence of uncertainty is invariably linked to the potential presence of error.''' The [https://en.wikipedia.org/wiki/Contingency_(philosophy) contingent] nature of uncertainty logically implies the contingent nature of error.
:'''Knowledge Professions use error as a proxy for uncertainty such that within discipline knowledge frameworks, uncertainty can be managed.''' The rationale for this belief is that error can be reliably described, quantified, explained, and ultimately reduced in ways that uncertainty cannot.

'''Uncertainty''' implies a range of variation that is impossible in certainty. Concepts and definitions of uncertainty are unbounded and vast but start from the point of complete and total ignorance. In describing uncertainty, statements can range from complete ignorance to relatively high degrees of belief that there is less potential for error in our reasoning or action. The latter is based upon confidence in justified knowledge, favorable past experience, and reliable parameters/models. One can't make that statement about ranges in certainty. If certainty, by definition, precludes doubt of any type or nature, then how could that be graduated to any degree? 
Uncertainty, on the other hand, can be reduced through disciplined efforts to acquire knowledge. In short, we can reduce or even manage uncertainty (to a degree); how can certainty be improved? Are there higher degrees of perfection? The answer is no. We reason from an uncertain starting point. Yet this effort presumes, as Mill and Wittgenstein postulated, that we can develop or acquire an increasing degree of belief or conviction that there is less potential for error in our reasoning or action. This position assumes that some way exists to identify what a lesser degree of uncertainty would look like.
:'''Knowledge professions acquire an increasing belief or conviction that there is less potential for error in their reasoning or action based on justified knowledge, favorable past experience, and reliable parameters/models.'''
Uncertainty is, in some form, amenable to description and explanation through observation and analysis. It is explainable and predictable to such a degree that it interests professionals such as scientists and engineers. Professional knowledge of this type has explanatory or predictive power, albeit limited in scope and application to relevant subjects such as physical objects or phenomena and functionality.
:'''The very act of reducing the scope of uncertainty to a limited set of phenomena implies choice and, ultimately, the decision to act in the face of such uncertainty, but acting or judging in this manner adds more value than doing the same under relative ignorance.'''
'''Uncertainty''', as described above, implies a hybrid nature. Examples of this are economic or decision-theoretic applications. In economics, [https://en.wikipedia.org/wiki/Perfect_information perfect information] allows one within the limiting framework of perfect competition in economic games to make decisions with 'perfect knowledge.' The crucial difference here is the distinction between '[https://en.wikipedia.org/wiki/Perfection perfection],' broadly, a state of completeness and lawlessness, and 'perfect, which can be thought of as an analytical limit that is approachable but can never be attained. Arbitrarily delimiting the realm of knowledge into defined, finite boundaries allows the simulated production of perfect choice, assumed crucially, to be free from error. The hybrid nature of this form of 'simulated' certainty produced within boundaries defined by assumptions and, therefore, clouded by doubt is itself a form of uncertainty. The importance of this is the semantical and logical association that exists between certainty and perfection versus the hybrid concept of a perfect anything, whether knowledge, infraction, competition, etc., or in the case of engineering, elastic, permeable, conductive, etc., being contained within an admittedly imperfect matrix of uncertainty.
:'''Simply put, a finite, heavily bounded piece of uncertain knowledge can be improved through the simulated use of [https://www.merriam-webster.com/dictionary/assumed assumedly] perfect information, but the perfection of knowledge cannot be attained or simulated.'''
Engineering and Economics, for example, simulate finite elements of perfect knowledge or information, heavily bounded with assumptions and a range of applications. By doing so, these professions reduce the bounds and variability of uncertainty and thereby manage it. Using these hybrid models to acquire knowledge takes place in an economy (regulated or institutional) and environment (transparent and structured), which puts pressure on the various professions to recognize and use qualitative and quantitative knowledge better to reduce uncertainties in their activities. The challenges in this reflect the nature of the profession's or discipline's knowledge (e.g., scientific, engineering, or medical), the policies and structure of the relevant professional knowledge bodies, process objectives and constraints, and "the elusive demands of politics." [6] One common theme runs through all of these efforts to address the presence of uncertainty in professional activities. 
:"The available scientific information upon which a decision must be made is almost always a mixture of engineering knowledge and uncertainty. Regardless of the information or methodology, there is the potential for error. This is true regardless of the relevant science, whether archaeology or aeronautics, economics or engineering, pharmacology or toxicology or epidemiology. Achieving the best social decisions requires not only understanding and using what we know but also appreciating and weighing the extent of our uncertainty. In making the best use of ... information in ... decision-making, it is still true that the beginning of wisdom is knowing what it is we do not know." (Vern, Op. cit., Reformatted and engineering substituted for scientific) 
'''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty to develop explanations, calculate, and make predictions. This professional knowledge converges on but never reaches certainty, producing a "legitimacy" from the fruitfulness of its use.''' <ref>Randomness Is Unpredictability, Antony Eagle, The British Journal for the Philosophy of Science, Vol. 56, No. 4 (Dec., 2005), pp. 749-790 </ref> 
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=== Bias and Uncertainty ===
Up to this point, the concept of error, as outlined in uncertainty, could easily be simulated by a truly random variable. There should be no detectable differences around the unseen or specified statistical mean. However, several instances in experience point to a tendency towards one extreme or a pattern of error, a collective that is in itself an error of errors, namely [https://en.wikipedia.org/wiki/Bias bias].

Arguably, this could be part of the choice uncertainty discussion below, but it makes more sense to be looked at on its own. Acting in the face of uncertainty is an exercise of personal knowledge or experience. Whether using professional knowledge or individual experience, the potential is there to make a series of choices that, in some instances, make errors more likely than they would be if considered in the context of a statistical analysis.

This tendency for an individual to make the same error in a predictable or repeatable manner is termed bias. Personal experience, beliefs, knowledge, data, and models all have inherent built-in factors, errors, or defects that create predictable error patterns when applied in decision-making. Walker (1998) argues implicitly that such bias can also be considered systemic error. This type of uncertainty is difficult to identify except on theoretical grounds when the magnitude and direction of the bias are known. Most of these biases, whether systemic or knowledge-based, individual or practice-based, require disciplined efforts to "validate" such methods, models, and judgments at all layers while bounding the uncertainty.
: This is the challenge to professional knowledge that must be overcome, namely, developing and validating models and methods for managing bounded uncertainty that minimize this tendency towards repeatable groups of error or bias. Therefore, from this point forward, the discussion will refer to 'uncertainty and its proxies, error, and bias.'
===Propagating Error and Uncertainty===
Partitioning uncertainty and its proxies, error, and bias into logically distinct subsets involves or is associated with multiple-step execution or choice frameworks. Combining those measurements or choices into single parameters or choices involves, in some cases, assembling the data/information in a unique or limited range of sequences. This implies that associated errors in observing, calculating, or choosing will have unequal impacts or consequences depending on where in the chain the error occurs. Also implied in this is the possibility that subsequent errors will be causally linked to some degree. (In statistical practice, this depends upon whether the errors are independent of each other or correlated, specifically, co-variant.)
:'''Professions develop knowledge of how uncertainty and its proxies, error, and bias propagate through an ordered and normative series of observations or choices. This allows the profession to prioritize error and bias reduction to achieve an optimized reduction of uncertainty.'''
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===Cognition, Metacognition and Uncertainty===
The concept of choice and the resultant error in uncertainty can be further delimited depending on the role of [https://en.wikipedia.org/wiki/Cognition cognition], [https://en.wikipedia.org/wiki/Metacognition meta-cognition], and independent external elements. The rationale for this argument is that such choice activity in the face of uncertainty is a conscious, cognitive act.

An argument could be made that such cognition is a prerequisite for exercising choice and introducing error, as presented and discussed above. The counterfactual to this argument is the example of uninformed choice. Choices introduce as much as errors or more as informed choices could be made. (???) Choice, in this sense, is indifferent to knowledge. Purposeful choice in an environment of objectives requires knowledge, method, and experience, but uninformed, speculative choice does not.
:"''' Cognition''' is the set of all mental abilities and processes related to knowledge, attention, memory and working memory, judgment and evaluation, reasoning and "computation," problem-solving and decision making, comprehension and production of language, etc. Human cognition is conscious and unconscious, concrete or abstract, as well as intuitive (like knowledge of a language) and conceptual (like a model of a language). Cognitive processes use existing knowledge and generate new knowledge." (Wikipedia)
:"'''Metacognition''' is "cognition about cognition", or "knowing about knowing" and can take many forms. It includes knowledge about when and how to use particular strategies for learning or problem-solving. There are generally two aspects of [https://en.wikipedia.org/wiki/Metacognition metacognition]: knowledge about cognition and regulation of cognition."
Some types of metacognition knowledge are:
:'''Person knowledge''' (declarative knowledge), which is understanding one's own capabilities.
:'''Task knowledge''' (procedural knowledge), which is how one perceives the difficulty of a task, which is the content, length, and type of assignment.
:'''Strategic knowledge''' (conditional knowledge) is one's capability to use strategies to learn information. (Accessed at Wikipedia)
Like metacognitive knowledge, metacognitive regulation or "cognitive control" contains three essential skills.
:'''Planning:''' refers to the appropriate selection of strategies and the correct allocation of resources that affect task performance.
:'''Monitoring:''' refers to one's awareness of comprehension and task performance.
:'''Evaluating:''' refers to appraising the final product of a task and the efficiency at which the task was performed. This can include re-evaluating strategies that were used. (Accessed at Wikipedia)
Nothing has been said up to this point that limits this structure to an individual, or a collective, or two entities making cognitive choices that are incompatible or inconsistent to varying degrees. Interpretations of this scheme include an individual professional exercising judgment in a decision that, in turn, relies upon the collective judgment and decision of the discipline as an underlying basis versus the decision of another individual to oppose/protest or otherwise contest that decision. Thus, the framework of cognitive/meta-cognitive applies to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, economics of regulatory practice, and game theory.
:By solving useful problems in the face of inherent uncertainty, discipline-specific frameworks of cognitive/meta-cognitive activity apply to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, the economics of regulatory practice, and game theory.
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===Probability and Uncertainty===
Probability is a [https://en.wikipedia.org/wiki/Coherence_theory_of_truth coherent] approach to uncertainty in the physical and mathematics or "classical" domains such as engineering. [8] and is the "...is certainly the best-known and most widely used formalism for quantifying uncertainty.[9] [https://en.wikipedia.org/wiki/Cox%27s_theorem Cox’s theorem] ("Any measure of belief is [https://en.wikipedia.org/wiki/Isomorphism isomorphic] (but not necessarily equal) to a probability measure") is a well-known argument for the validity of that argument. [Note 5]

Another writer, [https://en.wikipedia.org/wiki/Frank_Knight Knight] (1921,1956), presented the following taxonomy of probabilities: [10]
: '''Classical 'a priori' probability:''' As an idealized model, numerical probabilities are computed based on generalized principles of assigning equal likelihoods and mutually exhaustive possible outcomes (Runde, 1998) to all set members. Such probabilities are assigned to "...outcomes based on a judgment of indifference between those outcomes, that is, based on the absence of any evidence of real influences in play that may render any one outcome more or less probable than any other." (Runde, op. cit.) This builds upon Knights's concept of an 'absolutely homogeneous classification of instances that are completely identical except for [https://en.wikipedia.org/wiki/Indeterminate_(variable) indeterminate] factors. This judgment of probability or logical probability is on the same logical plane as the propositions of mathematics and ultimately [https://en.wikipedia.org/wiki/Inductivism inductions] from experience.'(Knight, 1956, pg.225)
: '''Bayesian 'a priori' probability:''' In contrast to interpreting probability as the "frequency" or "propensity" of some phenomenon, [https://en.wikipedia.org/wiki/Bayesian_probability Bayesian probability] is a quantity that we assign to represent a state of knowledge or a state of belief. In this view, probability is assigned to a hypothesis, often using the basis of past or prior experience. In contrast, under the frequentist view, a hypothesis is typically tested without being assigned a probability based on prior experience. The Bayesian interpretation of probability can be seen as an extension of propositional logic that enables reasoning with hypotheses, i.e., propositions whose truth or falsity is uncertain. Bayesian probability belongs to the category of evidential probabilities; to evaluate the probability of a hypothesis, the Bayesian probabilist specifies some prior probability, which is then updated in the light of new, relevant data (evidence). The Bayesian interpretation provides a standard set of procedures and formulae to perform this calculation. [11]
: '''Statistical or 'a posterior probability:''' Empirical evaluation of the frequency of association between predicates, not analyzable into varying combinations of equally probable or logical, 'a priori' alternatives. Knight argued that any "high degree of confidence" that the judgment that experience will remain valid for future predictions is "...still based on an 'a priori" judgment of indeterminateness." (Knight, Op. Cit.) Knight's argument to support this is what will be discussed further below that first, "...the impossibility of eliminating all factors not really indeterminate; and, second, the impossibility of enumerating the equally probable alternatives involved and determining their mode of combination so as to evaluate the probability by a priori calculation." (Knight, Op. Cit.) Knight noted that the main difference between this form and that of logical or inductive probability was the presence of empirical information, which allowed the analyst to identify propensity and direction in the observed phenomena. In this case, 'a priori' probability values may be derived from first principles and "statistical probabilities are determined a posteriori by the empirical method of counting instances." (Runde [1998] quoting Knight, op. cit.)
:'''Estimates:''' The distinction here is that there is no valid basis ('a priori' or 'a posteriori') for any classifying phenomena. For Knight, this form of probability presented the greatest logical difficulties of all ... but its distinction from the other types must be emphasized, and some of its complicated relations indicated..." (pp. 224-5, emphasis in the original)

Arguably, Knight's concept of probability can be viewed as "..a continuum of probability situations, depending on the degree of homogeneity of the 'instances' in question" [12] ; i.e. going from a logically distinct but equally likely set of elements to a unique set with one member with statistical frequency in the middle. Knight's main motivation for distinguishing between a priori probability and statistical probability underscores his opinion that "(i) the 'mathematical or a priori type of probability is practically never met with in business, while the second is extremely common'; and (ii) 'the statistical treatment never gives closely accurate quantitative results' (Runde quoting Knight pp. 215-16). Runde argues that Knight couldn't conceive of (empirically tabulated) occurrences that are used in daily commerce classes of instances that we have to make do within the course of everyday economic life could be divided into subclasses of instances that are sufficiently homogeneous to permit the determination of what he calls 'real' probability (p. 217).
:'''In modeling degrees of uncertainty, any measure of belief is isomorphic but not necessarily equal to a probability or statistics measure'''.
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===Explanation, Prediction and Uncertainty===
It should be clear that the general notion of uncertainty synonymous with complete ignorance must be bounded as part of a scheme to produce a useful concept of uncertainty in professional practice such as civil engineering.
:The first principle that must be introduced is that such uncertainty must be bounded by reliable and valuable knowledge gained from past experience combined with analytical and computational capabilities to address an immediate and real problem of interest. This could be viewed as the economic interest argument. Only uncertainties associated with physical phenomena and economic scarcity will be addressed.
:The second principle is that the knowledge model is capable of producing statements or assertions in the form of hypotheses that themselves are testable. [https://en.wikipedia.org/wiki/Testability Testability], in this sense, is defined as the property applying to an empirical hypothesis and involves two components:
::The logical property that is variously described as contingency, defeasibility, or falsifiability, which means that counterexamples to the hypothesis are logically possible. Contrast this to a [https://en.wikipedia.org/wiki/Tautology_(logic) logical tautology], which is always true that is true in every possible interpretation. The practical feasibility of observing a reproducible series of such counterexamples if they do exist. In short, a hypothesis is testable if there is some real, non-zero expectation of deciding whether it is true or false of verifiable, reproducible experience. Upon this property of its constituent hypotheses rests the ability to decide whether a theory can be supported or falsified by actual experience data. (Source Wikipedia.)
'''Knowledge models must be capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria described above in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error and bias.'''
===Variability and Aleatory Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge referred to as epistemic uncertainty. Acting in the face of epistemic uncertainty introduces choice uncertainty (discussed below) and results in the production of explanations and predictions. It is implied that there will be testable hypotheses in the form of outcomes of interest to the knowledge professions. Inherent in nature is a variation of results, which may appear to uncertain but isn't. Observations of phenomena will not be the same, and part of the experimental process is to narrow those results down to within an acceptable level of tolerance. Statistical measures seek to identify underlying and unobserved measures of central tendency. These parameters are not directly observed but are derived to a degree of confidence.
In some cases, variation around a mean or within a statistical range may be perfectly acceptable to the knowledge profession. Examples of this are in the physical sciences and medicine. In Engineering and, to a lesser extent, economics, the process or phenomenon may be required to be more controlled to a narrower range than what a natural or "unmanaged" amount of variation would allow. Several courses of action present themselves.
:The first would be to ignore the aleatory phenomenon and choice, which is rejected outright.
:The second would be to assume that, in effect, such variation is itself a form of minimally managed uncertainty and treat it the same as epistemic uncertainty. This is not a desirable outcome as reducing Aleatory error and bias requires
:A third approach would be to acknowledge that process variation is, in some forms, manageable. This means making choices and acting in a way that presumes that the natural occurrence of error can be reduced, or more importantly, bias can be reduced. There is ample precedent for the third approach in civil engineering design and project management. In this sense, the argument is made that the aleatory response to managed efforts results in an elastic variation range. Presumably, an effective management effort either "shifts" the central tendency of the process results or reduces the variation range of occurrence or some other population parameter. Choosing to act and accept that narrower range rather than the broader natural variation may result in an outcome that, while well within the bounds of the expected variation, is outside the arbitrary control limits established by the choice and action. While this phenomenon mimics the broader uncertainty and its proxy error, it is not an error. The information on the variation was known, and the ability to produce narrower results was an error, albeit a pseudo-error, when compared to the broader classes of uncertainty discussed above, such as total ignorance.
::Looking at uncertainty using this third approach means that the error in choosing a new target parameter for this process may contain epistemic errors (our knowledge of the phenomenon may be erroneous, or the model flawed), aleatory uncertainties as discussed, or, more importantly, choice error or bias in thinking that we can move the process results to meet the requirements.
:'''In civil engineering practice, this may mean applying professional judgment in developing partitioning schemes or allocating uncertainty between the three dimensions (Epistemic, Aleatory, and Behavioral) of uncertainty.'''
Lastly, raising the topic like this brings the questions of effectiveness and efficiency to the fore. Advancing knowledge models that reduce error and bias at first are largely choice models resulting in justifications and favorable experiences. At some point in the process, the knowledge profession advances explanations and predictions that require reliable mathematical or logical concepts that are reduced to models and parameters. The question of efficiency becomes important; the broader uncertainty may have been reduced, but the process is economically inefficient. The profession is urged to become more frugal or, for example, reduce the variability of the outcomes. Knowledge professions often operate under conditions that require them to acquire knowledge and reduce uncertainty effectively and efficiently. Doing so requires understanding both epistemic uncertainty, as discussed above, and statistical process variation or aleatory variation.
:'''Knowledge professions manage and reduce uncertainty in the form of error and bias in an economically effective and efficient manner. This effort requires understanding both epistemic and aleatory uncertainty.'''
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===Choice and Behavioral Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge, referred to as epistemic uncertainty, as well as the inherent variations in results, or what was termed aleatory uncertainty. Acting in the face of that epistemic and aleatory uncertainty introduces yet another uncertainty into the process, namely choice uncertainty. This uncertainty reflects uncertainties associated with first-person and third-person choice. This is particularly relevant for engineering with project stakeholders.
===Summary of Semantics Framework for Uncertainty===
Professional disciplines have gradually re-framed their understanding of uncertainty and, thru experience and analysis, have arrived at the following meta-knowledge concepts of uncertainty:
* '''Uncertainty and its proxy, error, have three components: [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic epistemic uncertainty], [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic aleatory uncertainty], and behavioral uncertainty.'''
* '''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty using non-dogmatic beliefs, which recognize the existence of specific criteria and information that would make the believer change their beliefs using coherent approaches such as probability.'''
* '''Knowledge professions develop knowledge of how uncertainty and its proxy, error, propagate through an ordered and normative series of observations or choices that meet testability criteria in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error. An error can then be described, explained, and reduced in ways that uncertainty cannot. This allows the professions to prioritize error reduction to achieve an optimized reduction of uncertainty and produce a "legitimacy" from the fruitfulness of its use.'''
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===Uncertainty versus Risk/Hazard===
Up to this point, nothing has been said about risk or hazard. Now, risk can be defined. Simply put risk or hazard are finite, logically distinct subsets of the broader uncertainty. Risk and hazard are currently defined interchangeably. First and foremost, risk is a specific subset of general uncertainty. Any arbitrary subset of general uncertainty can be defined as a risk if it meets the following criteria:
* Uncertainty is associated with the potential for material error when acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena.
* Uncertainty and its proxy, error, can be partitioned into logically distinct subsets associated with multiple-step execution or choice frameworks in physical environments linked to a unique or limited range of sequences.
**Subcriteria 1:Resulting errors will have varying impacts or consequences depending on where the error occurs and the degree of causal linkage or influence in the chain.
**Subcriteria 2:Potential errors can be sequenced and prioritized to achieve an optimized reduction of error or bias and, by proxy, uncertainty over time.
* Uncertainty and its proxies, error bias, and environments are isomorphic to coherent approaches such as probability.
* Uncertainty and its proxies, error, and bias are isomorphic to coherent approaches such as knowledge models capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria in 'reasoning under uncertainty.'
This is a definition of general risk. There is no differentiation for engineering or economics, medicine or insurance. There is no difference between a 'good' risk consequence and a 'negative' one. Redefining general risk into simpler terms gives the following:
:'''General Risk is a subset of general uncertainty that is associated with the potential for material errors and biases when sequentially acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena; isomorphic to probability measures and methods and capable of testability.'''
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==Logical Framework for Specific Risk Taxonomies==
As noted above, at the most fundamental level, uncertainty is ".....the subjective perception of ignorance." [13] [Note 6] At this point in the discussion, the focus has moved from discussing uncertainty to its framed subset, risk.
:'''Further, the discussion moves from general risk to a professional or discipline-specific risk [https://en.wikipedia.org/wiki/Taxonomy taxonomy]. Beyond this point, risk will be used in place of uncertainty.'''
Han et al. note that, in general, "(t) taxonomies are valuable not only in their comprehensiveness but in their coherent reduction of uncertainty to conceptually discrete elements."
Uncertainty (and, by implication, risk or hazard) taxonomies offer an approach or tool to more precisely identify and define ontological uncertainty so that it can be quantified, analyzed, and communicated. Viewed this way, uncertainty is not a single, monolithic phenomenon but "...multi-dimensional with theoretically distinct domains and constructs that are potentially measurable and related to different outcomes, mechanisms of action, and management strategies." [14] Taxonomic schemes for classifying the different kinds of scientific and, by extension, engineering uncertainty require identifying the various kinds of potential error associated with descriptive scientific or engineering information and knowledge. An example of this is the following quote from a legal authority:
:"I would especially stress the need for an agency to disclose the uncertainty that surrounds its determinations. '''And by uncertainty, I mean the agency's ignorance as well as its quantitative estimates of error."''' (Emphasis added) [15]
'''Given the necessity of acting in the face of enormous uncertainties [16], discipline taxonomies must offer a clearly coherent and probabilistic approach to uncertainty that is sufficiently general in nature, logically distinct and exhaustive in scope and "...provide decision-makers with a foundation for understanding the nature of discipline information..."''' [17]
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=== Scientific Risk Taxonomies ===
The [https://en.wikipedia.org/wiki/Scientific_method scientific method] is a body of techniques for investigating phenomena in its environment, acquiring new knowledge, or correcting and integrating previous knowledge. Inherent in that process are uncertainties of many forms. Any one taxonomy of [https://en.wikipedia.org/wiki/Uncertainty scientific uncertainty] has largely been focused on statistical models used to assess and quantify sampling, errors, and parameters, although several different taxonomies are conceivable: note-24 [18]
====Environment-specific, Phenomena oriented, model-centric taxonomy of uncertainties====
** [https://en.wikipedia.org/wiki/Uncertainty_quantification#Sources_of_uncertainty '''Parameter uncertainty'''] the source of which is model parameters that are inputs to the computer model (mathematical model) but whose exact values are unknown and cannot be controlled, or whose values cannot be exactly inferred by statistical methods. Subsets of this type of uncertainty include but are not limited to:
***'''Experimental uncertainty''' is also known as observation error, which comes from the variability of experimental measurements. An example is repeating an experiment measurement several times using exactly the same settings for all inputs/variables and recording the variability. It is a subset of the larger uncertainty component, parameter uncertainty.
*** '''Parametric variability''' comes from the variability of the input variables of the phenomena model and is also a subset of the larger uncertainty component, parameter uncertainty.
** '''Structural uncertainty''', or model inadequacy, model [https://en.wikipedia.org/wiki/bias bias] or "[https://en.wikipedia.org/wiki/Systemic_bias systemic bias]", or model discrepancy, which comes from the lack of knowledge of the underlying true state of the phenomenon or its environment. It depends on how accurately a mathematical model describes the true state of the phenomenon or what is termed "model fitness". Due to the inherent nature of uncertainty in any knowledge body (scientific or engineering), models are only an approximation to reality. Therefore, any conclusions drawn from the model can be very misleading when the underlying basis is not plausible or lacks validity. '''note-25 [19]]'''
*** What is missing in this context is any concept of output variability due to the model itself. Part of this is due to the lack of recognition of "process" in scientific investigation. The concept of the scientific method started with a single investigator, such as Galileo or Michael Faraday, working in their laboratories. It has grown into planet and solar system scale experiments such as data gathered on planetary flybys such as Mars, Pluto, and Ceres to the hunt for the Higgs Boson. Ultimately, science has become more like engineering in the scale and complexity of its investigations and theory aggregates, such as the "Unified theory" of particle physics. Subsets of this type of uncertainty include but are not limited to:
*** '''Algorithmic uncertainty''', or numerical uncertainty, comes from numerical errors and numerical approximations per implementation of the computer model. Most models are too complicated to solve exactly. For example, the [https://en.wikipedia.org/wiki/Finite_element_method finite element method] or [https://en.wikipedia.org/wiki/Finite_difference_method finite difference method] [may be used to approximate a solution [https://en.wikipedia.org/wiki/Partial_differential_equation partial differential equation], which introduces numerical errors. Other examples are numerical integration and infinite sum truncation, which are necessary approximations in numerical implementation.
*** '''Interpolation uncertainty''' comes from a lack of available data collected from computer model simulations or experimental measurements. For other input settings that don't have simulation data or experimental measurements, one must interpolate or extrapolate to predict the corresponding responses.
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====Commentary====
A more comprehensive taxonomy of uncertainties that acknowledges knowledge, data, and linguistic uncertainties is:
*'''Epistemic uncertainty''' -[https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Epistemic uncertainty] or systematic uncertainty, in contrast, reflects limitations in the current “state of knowledge” underlying models themselves, originates from competing theories or models, is not readily quantifiable, and is manifest by subjective confusion or indecision. It includes uncertainty due to measurement limitations, insufficient data, extrapolations and interpolations, and variability over time and space. Epistemic uncertainty is uncertainty about "...some determinate fact ... because of a lack of complete information. '''cite_note-26 [20]]''' Epistemic uncertainty can be classified into six main types:'''cite_note-27 [21]]'''
*''' Measurement error''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Observational_error Measurement error] is uncertainty that manifests itself as (apparently) random variation in the measurement of a quantity. Repeated measurements will vary and demonstrate classic statistical behavior.
* '''Systematic error''' - [https://en.wikipedia.org/wiki/Observational_error Systematic error] occurs due to bias in the measuring equipment, model, analysis, observation, or sampling procedure. It is formally defined as the difference between the true value of the quantity of interest and the value to which the mean of the results converges as sample or data sizes increase. Unlike measurement error, it is not (apparently) random, and therefore, results subject to systematic error alone do not vary about a true value. Systematic error can result from deliberate judgment to exclude (or include) data, parameters, or models that ought not to be excluded (or included).
** "The only way to deal with systematic error is to recognize a bias in the process, model, or procedure and remove it. Systematic error, however, is notoriously difficult to recognize except on theoretical grounds. Corrections may only be applied when the magnitude and direction of the bias are known. Such corrections underlie the application of double-sampling methods in environmental science." (Regan, et. al., op. cit.)
*'''Natural variation''' - [https://en.wikipedia.org/wiki/Natural_process_variation Natural variation] or underlying process variation occurs in systems that "...change (concerning time, space, or other variables) in ways that are difficult to predict...across the full range of temporal and spatial values (or other related variables)." '''note-28 [22]'''
** This form of uncertainty is reduceable utilizing tools such as [https://en.wikipedia.org/wiki/Statistical_process_control statistical process control] and replication of experiments or observations.
*'''Inherent randomness''' - [https://en.wikipedia.org/wiki/Randomness randomness] or [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty aleatory uncertainty] exists because the system is"...in principle, irreducible to a deterministic one (the most well-known case is described by [https://en.wikipedia.org/wiki/Uncertainty_principle Heisenberg’s uncertainty principle] in quantum mechanics)." '''note-29 [23], note-30 [Note 7]'''
** Even if the argument is accepted that it is unlikely that any given physical system at some level is inherently random, it is important for any taxonomy to distinguish between systems that appear random due to incomplete or inconsistent information and those that are intrinsically random. (Again, see Regan (2002)).
*'''Model uncertainty''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling Model uncertainty] occurs in the process of generating a model as a conceptual representation of a particular phenomenon to create a [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling#Overview simplified reflection of reality]. This selectivity of factors and variables creates uncertainty in at least three ways. (Regan (2002))
** '''Variables and processes''' that are regarded as relevant often represent a trade-off between system knowledge and assumed states and model objectives;
** '''Models''' depict observed processes using logical or mathematical constructs based on underlying theories about system states or dynamics using continuous equations to describe discrete processes.
** '''Curve fitting''' (including interpolation and extrapolation) with mathematical expressions using model variables where inputs are discrete data points.
** Regan (2002) argues that '''model uncertainty''' is "...notoriously difficult to quantify and impossible to eliminate ...(and)...(t)he only reliable way of determining how appropriate a model is for prediction is to perform validation studies."
** Subjective judgment occurs due to the interpretation of scarce or error-prone data.
*** The only way to address this type of uncertainty is to "...assign a degree of belief about an event in the form of a subjective probability." (Regan (2002))
*Linguistic uncertainty - Linguistic uncertainty, or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. '''note-31 [24]], note-32 [25]]'''
** '''Context dependence''' is uncertainty concerning the context in which a statement is to be understood;
** '''Ambiguity''' occurs when words have multiple meanings, and further reduction to a single meaning is not logically possible in a given context.
** '''Underspecificity''' occurs in the presence of "unwanted generality" or multiple interpretations, and reduction to a smaller set or even a single interpretation is not logically possible.
*Stochastic or statistical uncertainty - Stochastic or statistical uncertainty, which is sometimes known as [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Aleatoric uncertainty] as well as the subject of [https://en.wikipedia.org/wiki/Stochastic_control stochastic control] pertains to the parameters of a risk model, originates from sampling or measurement error, and can be quantified and mathematically expressed (e.g., using confidence intervals).
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== Uncertainty Taxonomies in a Legal Environment ==
From a legal perspective, a definition of taxonomy is dictionary-based, such as "the systematic distinguishing, ordering, and naming of type groups within a subject field." given by Walker (op. cit.). Such legal concepts of scientific or engineering taxonomies are not required to be epistemically complete. Still, they must cover "the most significant aspects of ...(current, good discipline practices) ... and of the descriptive information commonly encountered by decision-makers" in a logically distinct manner. (Walker, 1991, p. 571) These taxonomies must be sufficient to catalog the types of uncertainty associated with descriptive assertion, including the critical subset of cause and effect assertions. This legal/decision-maker framework has presented descriptive uncertainty as having six components (Walker, 1991). Although not part of the Walker taxonomy, underlying the scheme is linguistic uncertainty, or "vagueness," as a source of uncertainty, including uncertainties due to context dependence, ambiguity, and under-specificity. '''note-33 [26]]''' Arguably, these uncertainties underlie the most basic element in the taxonomy.
* '''Linguistic '''- or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. (Regan, 2002)
* '''Conceptual'''- the definition by choice and design of descriptive concepts or variables to be used as [https://en.wikipedia.org/wiki/Predicate_(grammar) predicates] where the subject of an assertion identifies what is being discussed and the predicate provides information about the topic or characterizations such as what the subject is, what the subject is doing, or what the subject is like (Wikipedia).
* "Conceptual uncertainty, or the potential for conceptual error, arises whenever predication occurs. Whenever a concept is used to describe something, using certain concepts instead of others begins to structure how we understand the object, event, or instance under discussion. Predication or conceptualization generates useful information about things, but it also can inhibit our ability to think about those things with concepts other than those selected. The concepts used may not be the most fruitful or the best designed- either for scientific purposes or for making wise, fair, effective, and efficient decisions." (Edited for reading, Walker, 1991)
* Similarly, choice and design of descriptive variables enhance and restrict data set membership, creating potentially incomplete, incompatible, or inconsistent data sets, "...the classification categories employed, and the relationships among those categories (nominal, ordinal or scalar)." '''note-34 [27]]'''
* Concept uncertainty '''note-35 [28]''' like systematic error discussed above is notoriously difficult to recognize, except on theoretical grounds, and the magnitude and direction of the bias are known.
Both terms could be analogized to that of [https://en.wikipedia.org/wiki/Accuracy_and_precision#Terminology_of_ISO_5725 accuracy] in [https://www.bipm.org/en/publications/guides/ ISO 5725] which looks at the ability of the concept or system's ability to produce results that are proximate to the "true" value or state of the phenomenon. Conceptual uncertainty and systemic bias are related to another measure of uncertainty: validity. Walker has defined validity as the ability of a concept to quantitatively describe and explain what the phenomenon objective "truly" is. Walker also ties the concepts together when he states, "Validity concerns the "accuracy" of the measurement data, not its precision." (Walker, 1998) 
Additionally, conceptual uncertainty and associated validity issues could be propagated throughout this taxonomy. Every component of action and decision in the face of uncertainty raises validity issues. Walker recognized this when the author defined epistemic choice for concepts used throughout the other uncertainty components or layers.
* '''Measurement''' uncertainty or Misclassification error- the application of the underlying concepts or variables to specific, individual cases and the uncertainty of the reliability (Walker, 1998) of the resultant data. (It is important to remember that assessment observation and measurement are used interchangeably in this analysis.) Thus, an assessment method or procedure is considered "reliable" or "precise" in the scientific terms of ISO 5715 if it repeatedly produces consistent results.
* Measurement uncertainty is logically distinct from conceptual uncertainty, which can only occur after any conceptual uncertainty has been established. Still, misclassification can occur even when the conceptual uncertainty has been minimized. Conversely, underlying errors or errors made in measurement or classification propagate thru the reasoning chain and reduce the quality or value of the final result.
* While uncertainties associated with quantitative measurements or assessments can be statistically evaluated and described, uncertainties related to qualitative assessments are more problematic.
**Working through the taxonomic chain from base concept to causal explanation requires amassing and integrating qualitative information and quantitative data into input parameters for mathematical models. Developing procedures for producing consistent, repeatable sets of information from assessments that minimize conceptual error or systemic bias requires professional efforts to "validate" such methods, models, and judgments at all layers in the process. Validation as a process is something that civil engineering as a practice needs to embrace more frequently.
* '''[https://en.wikipedia.org/wiki/Sampling Sampling]'''- in the classical statistics sense, is the selection of a limited subset of a larger population to use as a basis for making estimates about the population in the form of attributes, proprieties, or parameters. The resulting information would be determinative in making forecasts about future population sampling or possessing a desired amount of predictive power. The uncertainty about the ability of this information to correctly predict future samples is termed sampling error.
* '''Modeling'''- Modeling uncertainty arises whenever a claim is made that out of a class of candidate relationships and variable pairings, one variable pairing inclusive of constants is chosen that possesses a particular or persistent mathematical relationship to another variable X. Modeling errors arise in selecting the wrong variable pair and constants or by incorrectly specifying its constants. (Walker, 1991, pg. 599)
** '''Causal'''- In causal modeling and analysis, the relationship between causal variables is not strictly a mathematical function. Still, it makes testable predictions and explains "...how a system of variables works, why a system works the way it does, or why it makes sense to think of certain variables as a system" at all." (Walker, 1991, pg. 609)
** '''Epistemic'''- the choice of interpretations for fundamental, logical concepts used throughout the other components or layers.
The components of this uncertainty taxonomy can combine in different ways to "...produce different aggregate uncertainties." '''note-36 [29]'''
:''' Only the scientific and engineering professions possess the capacity to develop and validate procedures and models that combine the different kinds of qualitative '''note-37 [30]''' and quantitative information and their associated potential for error into an aggregate measure or "scalar variables" of uncertainty.'''note-38 [31]]
Scalar variables are quantitative variables whose categories are related by some measure of the relevant property's incremental frequency, degree, or amount. (Walker, 1991) Examples are Modulus of Elasticity, Compression stress in elastic materials, etc.

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=== Economics Uncertainty Taxonomies ===
Like scientific concepts of uncertainty, economics is environment-specific (in this case, read sector of economy or markets), phenomenon-oriented (in this case, read human activity and manufactured phenomena such as firms and markets guided by human judgment and decision), and model-centric.
:''' Economics is not interested in all forms of uncertainty, only specific, meaningful subsets possessing casual connections with material impacts, often labeled as 'risks'.'''
One of its earlier writers ([https://en.wikipedia.org/wiki/Frank_Knight#Life_and_career Knight], 1921, 1948, 1957) attempted to define "uncertainty" as the presence of "defects of managerial knowledge" (or [https://en.wikipedia.org/wiki/Knightian_uncertainty knightian uncertainty] and thus risk centric) as risk that is immeasurable, not possible to calculate.(Wikipedia) Risk in this sense was defined as "the ordinary risks of business activity which can ... be reduced... by applying the insurance principle." Knight does acknowledge that risk is a specific and unambiguous subset of uncertainty. (Risk, Uncertainty and Profit, 1957, pg. 19) For Knight, measuring uncertainty meant measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences, which Knight referred to as "the imperfection of knowledge". (Risk, Uncertainty and Profit, 1957, pg. 197) One of Knight's many contributions to this analysis was recognizing information and knowledge's role in economic activity.
:"''(W)e are concerned only to emphasize the fact that '''knowledge is in a sense variable in degree and that the practical problem may relate to the degree of knowledge rather than to its presence or absence in toto.''' We live only by knowing something about the future, while the problems of life, or conduct at least, arise from knowing so little. This is as true of business as of other spheres of activity. The situation's essence is action according to opinion, of greater or less foundation and value, neither entire ignorance nor complete and perfect information, but partial knowledge. If we are to understand the workings of the economic system, we must examine the meaning and significance of uncertainty; and to this end, some inquiry into the nature and function of knowledge itself is necessary.''" (Knight, pg. 199) Emphasis added
Knight also reinforced the difficulties in classifying uncertainties when he analyzed life insurance. In this case, accidental death was an uncertain phenomenon compared to sickness and accident, where an "...objective description and classification of cases was impossible..." (Knight, pg. 248) Another factor that made uncertainties ineligible as risks and, therefore, uninsurable because they were unclassifiable was the exercise of judgment in making decisions by the businessman. (Knight, pg. 251)
This is a simple classification of economic uncertainty and, as an idealization, is philosophically controversial. '''note-39 [32]'''
* Uncertainty and information about the economic environment are distinct from uncertainty about others’ behavior or choices.
* Risk as a specific subset of uncertainty implies that "...all possible acts are known, all possible outcomes arising from each act are known, and it is possible to assign probabilities to each act." '''note-40 [33]'''
Like the economic [https://en.wikipedia.org/wiki/Theory_of_the_firm theory of the firm], an economic theory of uncertainty seeks to explain and predict the nature of a specific subset of uncertainty (in this case, risk relating to physical phenomena and human activity), including its behavior, structure, and relationship to the universal uncertainty of physical phenomena. In this sense, uncertainty, like firms and markets, can establish price equilibriums under the right circumstances different from those that might have occurred in either of the other two. Such a theory of uncertainty offers a partitioning scheme that decomposes uncertainty into distinct and meaningful subsets, of which risk is a significant one. It also explains how the presence and variability of knowledge cause or " drive" economic consequences. Unlike physical phenomena, economic activity creates artificial objects such as products, firms, mediums of exchange, markets, economies, and knowledge about those activities. The economics of uncertainty exists as an alternative to "certainty" models that assume away imperfect knowledge and unknown choice/judgment preferences when it is more efficient for economic decision-making. This also allows the introduction of the logical concept of "hazard" versus "risk". Frank used hazard extensively in Risk, Uncertainty, and Profit, but not to the extent that one could say it was interchangeable. An example of this is in the term "[https://en.wikipedia.org/wiki/Moral_hazard Moral hazard]," where a firm could engage in "riskier" and potentially "uninsurable" behavior if the information were transparent, and, therefore, insurance coverage of losses is limited. note-41 [34]], note-42 [35]]

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=== Uncertainty and Risk in Policy and Regulatory Environment ===
:''"Hume's 'just reasoner', when faced with a difficult public policy decision in current times, would probably commission a risk analysis. But how could they incorporate 'a degree of doubt, caution, and modesty?"'' '''note-43 [36]'''
The problem of "decision-making in the face of uncertainty" is how to regulate based on incomplete information that has the potential to be materially inaccurate. A contextual assumption is that we need to evaluate decision rules for dealing with uncertainty. As a social enterprise, risk regulation, whatever its substantive objectives, should be as '''effective, efficient, and equitable''' as practically possible. These "three E's" form a set of "process objectives" or "meta-goals." From the uncertainty standpoint, causal information can be usefully divided into two major categories: information about groups and information about individuals.

Each category, group, and individual has its distinctive types of inherent uncertainty. These are logically distinct, generally independent, and cumulative, contributing to '''(?????)'''

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=== Engineering Uncertainty Taxonomies ===
Like scientific and economic concepts of uncertainty, engineering is environment-specific, phenomena-oriented, model-centric, external choice, and driven by decision-making in the face of uncertainty. Like economics and unlike science, engineering is not interested in all forms of uncertainty, only those with determinable and material consequences. Like economics, engineering conceives risk as a specific and unambiguous subset of uncertainty determined by measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences. Also, engineering recognizes the role information and, by extension, knowledge plays in constructing the built environment.
'''note-44 [37]]''' Like economics, engineering differs from economics and scientific uncertainty in its interest in behavioral uncertainty.
*"Behavioral or interaction uncertainty is how individuals or organizations act or interact. Behavioral uncertainty arises from four sources: design uncertainty, requirement uncertainty, volitional uncertainty, and human errors." '''note-45 [38]]'''
** A design uncertainty is a choice among alternatives over which an individual or group of individuals exercises direct control but has not yet decided upon.
** Requirement uncertainty includes parameters of interest to and determined by the stakeholder, independent of the engineer or designer.
** Volitional uncertainty is uncertainty about what the subject him/herself will decide. Other people’s future actions and conduct are not entirely predictable, particularly when dealing with other organizations.
** Human errors occur during the development of a system or project due to blunders or mistakes by an individual or individuals.

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==Practice Frameworks for Uncertainty and Risk in Civil Engineering Practice==
===PMIBoK===
PMI does not define uncertainty, but the PMI Body of Knowledge uses the term in 45 places (uncertainty) and as describing properties in 8 places (uncertainties). [See Note 8] PMI does define "risk" and uses the term over 1,300 times in the same document. In its glossary, PMI also defines terms such as "threat" and "opportunity" but not events.[391 The BoK contextually defines uncertainty when it states that project risk has its origins in the uncertainty present in all projects. (Op. Cit., pg 309) 
'''Uncertainty''' is contextually defined and taken as having the property of affecting project execution and the ability to meet stakeholder expectations. Uncertainty is more than the sum of all individual risks (known and unknown). The nature of this uncertainty is not defined, only its capacity to affect something else or its properties. 
'''Risk''' is defined as " ... an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives." (Ibid.) Beyond this formal definition, PMI adds context when, in its risk management section (Sec. 11 ), the BoK states that risk is a caused event or condition with multiple causes and impacts. The BoK even offers specific techniques for mapping cause-and-effect relationships and influence diagrams. (Sec. 11.2.2.5) Not as evident but equally important is the recognition that not only is risk caused but the impact is "triggered". The risk trigger is an event or situation that signals that it is about to occur (Glossary, pg. 566). Risk conditions are factors that affect or otherwise contribute to risk, such as project stakeholders. Risk implicitly retains some residual element of probability in it, or a value of less than 1.0 with risk that approaches a level of near certainty is termed "issues" or "realized risk" .(Op. Cit., pg 309). PMI also links risk to underlying variations in project outcomes. (Ibid.) Risk is based upon data and information which can be assessed as to its quality (Sec. 11.3.2.3). This analysis looks to determine the value of the information and examines the degree to which the risk information is understood as well as its " ... accuracy, quality, reliability and integrity ... " (Ibid.) Part of that is understanding the relationship between occurrence and impact as outlined in PMl's Figure 11-10 Risk Impact Matrix and developing a model for prioritizing risk products and setting the threshold for action.
====Commentary:====
For defining and identifying risk, PMI notes in Sec. 11.2.2.4 that " ... Every project and its plan is conceived and developed based on a set of hypotheses, scenarios, or assumptions." Part of the risk analysis process is identifying risks to the project, such as inaccuracy, instability, inconsistency, or incompleteness of assumptions. Similarly, the quality of management plans, as well as their consistency with others and the project objectives and assumptions are 11 •• .indicators of risk in the project." (Sec. 11.2.2.1) 
Similarly, the role of engineering economics in risk is embedded or implied in the PMI definition of risk when the BoK discusses risk identification as a process of assessing which risks out of the total risk exposure for the project "may affect" the project and distilling their characteristics. Embedded in this statement is the concept that not all risks can cause economic impacts (positive and negative) to the project. Also embedded is the requirement to understand risk characteristics to identify project risk (Cost and schedule). Lastly, risk identification is an iterative process of incrementing project risk documentation, much like project scope, cost, and schedule documentation. 
Lastly, PMI acknowledges the complex constitution of risk aggregates or the "sum" of all risks in its material, but is it a sum? Risk information has descriptive and explanatory content. These are knowledge and meta-knowledge components. Understanding project assumptions is project-specific knowledge, but identifying risks from assumption inaccuracy, instability, inconsistency, or incompleteness requires professional or program meta-knowledge. 
'''Uncertainty exists in all projects, but risk, a subset of uncertainty with material economic impacts, is a key meta-knowledge element for civil engineering practice.'''
===Software Development Process- Risk Focused===
The Unified Process requires the project team to focus on addressing the most critical risks early in the project life
cycle. The deliverables of each iteration, especially in the Elaboration phase, must be selected to ensure that the greatest risks are addressed first. <ref> Booch, Grady, Ivar Jacobson, and James Rumbaugh. "The unified software development process."
Reading: Addison Wesley (1999).</ref>
===American Society of Civil Engineers (ASCE)===
ASCE does not define uncertainty, but the ASCE CE Body of Knowledge (CEBoK) uses the term in 22 places (uncertainty) and describes properties in 13 places (uncertainties). 
The CEBoK does not use the phrase "uncertainty and risk" as PMI and AACE do in their publication. ASCE reverses the two (Risk and uncertainty) in twelve places regarding technical outcomes (Outcome 12). A fundamental difference between PMI and ISO is that risk is considered a subset of uncertainty. ASCE does not define "risk" but uses the term 25 times in the same document. Almost all are in the context of variation of design parameters and not in the classical context of cost, schedule uncertainty, and risk.
====Commentary:====
...
=== American Association of Cost Engineers (AACE) ===
AACE defined uncertainty, risk, and other related terms in its Risk Management Dictionary. <ref>Risk Management Committee uRisk Management Dictionary, Cost Engineering Vol. 37/No. 10, OCTOBER 1995. </ref>
AACE's overall strategy was to define risk and other terms that stem from base uncertainty. AACE first defined uncertainty as .... "(t)he total range of events that may happen and produce risks (including both threats and opportunities) affecting a project (see opportunities, events, conditions, risk, and threats" where the following sub-definitions apply:
** Biases--A lack of objectivity based on the individual's position or perspective. Systematic and predictable relationships between a person's opinion or statement and his/her underlying knowledge or circumstances. Note: there may be "system biases" as well as "individual biases."
** Condition (Uncertain Condition)-Any specific identifiable circumstance (such as the rate of inflation or the quality of labor available) that might affect the outcome of the project
** Event (Uncertain Condition)-is a specific identifiable action (such as a large government project being started in the same labor area as your project) or an act of nature that might happen and that (if it does happen) could affect the outcome of the project.
** Opportunities are Uncertain events that could improve the results or improve the probability that the desired outcome will happen
** Threats are Uncertain events that are potentially negative or reduce the probability that the desired outcome will happen.
*** AACE defines Risk as an "ambiguous term" (sic) that is synonymous with uncertainty or a negative subset such as "threats", or an overall negative impact of all possible uncertainties.
====Commentary:====
...
==Limitations of the definition==
...
==Working definition==
...
==Beneficial outcomes==
...
==See also==
Wiki articles on the project.

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==Notes==
<references group="note" />

# See McDaniels et al. 2004 for an extensive discussion and bibliography on the historical development of Risk Analysis and some key milestones in the 20th century.
# Open item
# The Merriam-Webster dictionary defines uncertainty:
::"the quality or state of being uncertain," which is a circular definition. Likewise, one could define uncertainty as the state of not being certain. Synonyms are distrust, doubt, misgiving, mistrust, reservation, skepticism, and suspicion. Another writer added indefinite, indeterminate, not certain to occur, problematical, unreliable, untrustworthy, unknown beyond doubt, dubious, doubtful, not clearly identified or defined, not constant, variable, and fitful to the list.
::Han, Paul KJ, William MP Klein, and Neeraj K. Arora. "Varieties of Uncertainty in Health Care: A Conceptual Taxonomy." Medical Decision Making 31.6, (2011 ): 828-838.
::: Han, ct. al. noted that any definition of uncertainty encompasses " ... numerous types, sources, and manifestations of uncertainty, and ... (any) ... useful working definition of uncertainty needs to specify the concept underlying these varied meanings of the term." (Ibid.)
<ol start="4">
<li> Implicit in this definition of uncertainty as a "state of' is ... </li>
: " ... a conceptualization of uncertainty as a subjective, cognitive experience of people--a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one's lack of knowledge, without which one could not feel uncertain; '''uncertainty is a form of "meta-cognition" ... ( or alternatively, its main component, meta-knowledge) ... -a knowing about knowing.'''" (Han, 2011, op. cit., Emphasis added)
</ol>
<ol start="5">
<li> Cox wanted his system to satisfy the following conditions:</li>
: Divisibility and comparability- The plausibility of a statement is a real number and depends on the information we have related to the statement.
: Common sense - Plausibilities should vary sensibly with the assessment of plausibilities in the model.
: Consistency - If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). -preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf
</ol>
<ol start="6">
<li> The utility of existing taxonomies of uncertainty in civil engineering and, by extension, "risk" has been unsatisfactory for this very reason. This is particularly true in cases where the source of the uncertainty is conflicting versus incomplete information. See Regan, et. al.,"A taxonomy and treatment of uncertainty for ecology and conservation biology" (2002)
for a survey of scientific taxonomies for uncertainty.</li>
<li> Regan et al. argue that " ... genuine examples of this kind of uncertainty are hard to find. Even classic cases of random experiments like coin tosses and the throwing of dice are deterministic; it is just that we do not have enough information about the dynamic processes and initial conditions to make any sensible estimates about the outcomes. Such processes are, for all intents and purposes, inherently random, but they are not genuinely inherently random. For similar reasons, complex systems such as ecosystems and weather patterns are unlikely to be inherently random. Similarly, chaotic systems are entirely deterministic. They are unpredictable because the deterministic processes generating them and the relevant initial conditions are hard
to fully specify (see Stewart 1989, Sugihara et al. 1990)." (Regan (2002) </li>
<li> The CEBoK uses the phrase "uncertainty and risk" twice in reference to scheduling (Section 6.5,2) and cost estimating (Section 7.2,2) as well as reversing the two (Risk and Uncertainty) also in two places in discussing project life cycle (both in Section 2.4.1) </li>
</ol>

==References==
<references />

Uncertainty and Risk in Civil Engineering Practice

2024-11-24T15:59:28Z

WikiSysop:

'''Life is short and the art long; the occasion instant, experiment perilous, decision difficult.''' (Hippocrates as quoted in Fox,)
:''See also [[Project Definition]] , [[Project Life Cycle and Phase Models]] , [[Project Delivery Methods]]''
:''See also [[Technical Outcomes for Civil Engineering:Risk and Uncertainty]]''
==Basic Considerations==
As human beings,'' ... being alive means seeking opportunities and taking risks.'' As knowledge professionals
living in the 21st century, this means coping with an increasingly complex number of uncertainties for humans
living in this environment. We seek to understand better how these uncertainties can be characterized and
managed. The essence of this article and the experience for engineers in general and civil engineers in particular is
that manageable uncertainty is, by definition, termed risk, and its kindred cousin is termed hazard. This causes us to experience the
:" ''... human dread of and fascination for risk and the increasingly important role of risk analysis within societies ...'' " <ref name="McDaniels"> McDaniels, Timothy, and Mitchell Small. Risk analysis and society: an interdisciplinary characterization of the field. Cambridge University Press, 2004, page 1 </ref> (Ibid.)
and, by extension, civil engineering. McDaniels et al. argue that risk management has been
fundamental to our social and governance development for the past 10,000 years. The scale and shape of the
uncertainties faced in this period shaped the societies that have developed today. The central thrust of this effort
over the centuries has been to reshape and re-frame our understanding and conception of uncertainty from one of
complete unknowing and simple acceptance as our fate in life to one of management (cf"Against the Gods"
concept in Bernstein's book [21).
: ''All the knowledge professions and disciplines have struggled with managing uncertainty, for it is impossible to manage unbounded uncertainty.'' <ref group="note">See McDaniels et al. 2004 for an extensive discussion and bibliography on the historical development of Risk Analysis and some key milestones in risk analysis in the 20th century.</ref>
As outlined below, professions such as civil engineering have successfully acted in the face of uncertainty. It lies in the profession's ability to reduce a wide variety of uncertainties into increasingly smaller and crucially bounded subsets that can be managed. These are called 'risks. More recently, this process has evolved, and individual disciplines such as Civil Engineering (CE) have developed their knowledge and models to perform risk analysis. Risk is also taught as a distinct discipline and specialty practice within civil engineering. Still, it has some unique features that set it apart from other more classical practices within CE. As such, it is one of the first of some very specialized CE practices that use knowledge about civil engineering knowledge, or meta-knowledge. Other examples of civil engineering metaknowledge are project controls and quality controls.
==Semantic, Epistemic and Logical frameworks==
The semantic, epistemic, and logical frameworks for uncertainty and risk have several dimensions and layers of logical frameworks. The semantics problems include interchangeable usages for risk and uncertainty and hazard, uncertain and imperfect, and a lack of definitional material or context. The semantical scheme for this article will be to proceed from the distinction between certainty and uncertainty through marginal refinements and reductions of uncertainty up to the point of causal uncertainties. Beyond this point of semantics will be the logic frameworks or the subject of taxonomies of professional knowledge. First, it is about simple, testable phenomena, and then, it moves on to complex taxonomy schemes for artificially constructed phenomena or engineering projects.
<ref> Bernstein, Peter L., and Jesse Boggs. Against the gods. Simon & Schuster Audio, 1997. </ref> <ref group="note">TBD</ref>

==Semantics framework==
===Certainty and Uncertainty===
''''' There is no such thing as absolute certainty, but there is assurance sufficient for the purposes of human life.''''' ([https://en.wikipedia.org/wiki/John_Stuart_Mill John Stuart Mill]) 
'''''If you tried to doubt everything, you would not get as far as doubting anything. The game of doubting itself presupposes certainty.''''' ([https://en.wikipedia.org/wiki/Ludwig_Wittgenstein Wittgenstein] # 115 from On [https://en.wikipedia.org/wiki/On_Certainty Certainty]) 
It is important to note that the references to epistemic or epidemiological knowledge in this article are assumed to relate to three forms of knowledge, namely:
* Knowledge that ( [https://en.wikipedia.org/wiki/Descriptive_knowledge descriptive or declarative or propositional knowledge])
* Knowledge how ( or "[https://en.wikipedia.org/wiki/Procedural_knowledge know-how]"), and
* [https://en.wikipedia.org/wiki/Knowledge_by_acquaintance Knowledge by acquaintance].
'''Certainty''' has been defined as "an epistemic property" of knowledge in all its forms and the state of our beliefs about that knowledge.
<ref group="note">Certainty, Stanford Encyclopedia of Philosophy, accessed on September 12, 2015, at http://plato.stanford.edu/entries/certainty/#ConCer </ref>
Certainty about any belief about knowledge implies that it is not subject to doubt or skepticism. This immunity to criticism can be dogmatically based, emphasizing the importance of a propositional-based sense of truth over experiential, sensory perceptions. <ref name="Note4"/>
: '''The demarcation line between dogmatic and non-dogmatic beliefs lies in the presence and recognition of specific criteria and information that would make the believer change their beliefs.'''
An empirical framework of [https://en.wikipedia.org/wiki/Testability testability] and [https://en.wikipedia.org/wiki/Falsifiability falsification] is required to recognize this and limit dogmatism. For example, one could argue that people would change their minds if God asked them to. Similarly, one could construct a concept of epistemic as opposed to dogmatic belief certainty as the ability to know anything that one chooses to know and can be known or [https://en.wikipedia.org/wiki/Omniscience inherent omniscience].

A second kind of certainty is epistemic, when conviction reflects the highest possible support for a belief. In this sense, knowledge is separate from beliefs, although someone may have beliefs about a property, such as certainty of knowledge. Logically, it has been shown that there will be unprovable statements within the system for any such system of knowledge. Secondly, the knowledge system cannot demonstrate its own consistency. ([https://en.wikipedia.org/wiki/Gödel%27s_incompleteness_theorems Gödel's incompleteness theorems])
: '''Certainty, in real life, is useless or often damaging (the idea is that "total security from error" is impossible in practice, and a complete "lack of doubt" is undesirable)''' ([[https://en.wikipedia.org/wiki/Certainty Physicist Carlo Rovelli]])
'''Uncertainty''', on the other hand, arises immediately in the slightest amount of doubt or criticism. <ref name="Note3">[Note 3]</ref> Similarly, a general definition of uncertainty could be given as "...any departure from the unachievable goal of complete [https://en.wikipedia.org/wiki/Determinism determinism]." <ref name="Reference5">[5]</ref> Reasoning under uncertainty is very different than performing the same under certainty. In reasoning under certainty, one has complete knowledge and deduces without doubt and equally important, without limitation, thereby concluding free from error. Reasoning under uncertainty, one works in a state of incomplete, inconsistent, and limited knowledge; doubts cloud any statements or assertions and thereby taint any deductions/inferences resulting in the potential for error. <ref name="Note4">[Note 4]</ref>
:'''Knowledge professions reason in a state of incomplete, inconsistent and limited knowledge with doubts that cloud any statements or assertions and taint any deductions/inferences resulting in the potential for error.'''
:''' It is also important to note the semantical and logical correlation between 'reasoning under uncertainty' or 'acting in the face of uncertainty' with the 'potential for' or 'presence of' error. The presence of uncertainty is invariably linked to the potential presence of error.''' The [https://en.wikipedia.org/wiki/Contingency_(philosophy) contingent] nature of uncertainty logically implies the contingent nature of error.
:'''Knowledge Professions use error as a proxy for uncertainty such that within discipline knowledge frameworks, uncertainty can be managed.''' The rationale for this belief is that error can be reliably described, quantified, explained, and ultimately reduced in ways that uncertainty cannot.

'''Uncertainty''' implies a range of variation that is impossible in certainty. Concepts and definitions of uncertainty are unbounded and vast but start from the point of complete and total ignorance. In describing uncertainty, statements can range from complete ignorance to relatively high degrees of belief that there is less potential for error in our reasoning or action. The latter is based upon confidence in justified knowledge, favorable past experience, and reliable parameters/models. One can't make that statement about ranges in certainty. If certainty, by definition, precludes doubt of any type or nature, then how could that be graduated to any degree? 
Uncertainty, on the other hand, can be reduced through disciplined efforts to acquire knowledge. In short, we can reduce or even manage uncertainty (to a degree); how can certainty be improved? Are there higher degrees of perfection? The answer is no. We reason from an uncertain starting point. Yet this effort presumes, as Mill and Wittgenstein postulated, that we can develop or acquire an increasing degree of belief or conviction that there is less potential for error in our reasoning or action. This position assumes that some way exists to identify what a lesser degree of uncertainty would look like.
:'''Knowledge professions acquire an increasing belief or conviction that there is less potential for error in their reasoning or action based on justified knowledge, favorable past experience, and reliable parameters/models.'''
Uncertainty is, in some form, amenable to description and explanation through observation and analysis. It is explainable and predictable to such a degree that it interests professionals such as scientists and engineers. Professional knowledge of this type has explanatory or predictive power, albeit limited in scope and application to relevant subjects such as physical objects or phenomena and functionality.
:'''The very act of reducing the scope of uncertainty to a limited set of phenomena implies choice and, ultimately, the decision to act in the face of such uncertainty, but acting or judging in this manner adds more value than doing the same under relative ignorance.'''
'''Uncertainty''', as described above, implies a hybrid nature. Examples of this are economic or decision-theoretic applications. In economics, [https://en.wikipedia.org/wiki/Perfect_information perfect information] allows one within the limiting framework of perfect competition in economic games to make decisions with 'perfect knowledge.' The crucial difference here is the distinction between '[https://en.wikipedia.org/wiki/Perfection perfection],' broadly, a state of completeness and lawlessness, and 'perfect, which can be thought of as an analytical limit that is approachable but can never be attained. Arbitrarily delimiting the realm of knowledge into defined, finite boundaries allows the simulated production of perfect choice, assumed crucially, to be free from error. The hybrid nature of this form of 'simulated' certainty produced within boundaries defined by assumptions and, therefore, clouded by doubt is itself a form of uncertainty. The importance of this is the semantical and logical association that exists between certainty and perfection versus the hybrid concept of a perfect anything, whether knowledge, infraction, competition, etc., or in the case of engineering, elastic, permeable, conductive, etc., being contained within an admittedly imperfect matrix of uncertainty.
:'''Simply put, a finite, heavily bounded piece of uncertain knowledge can be improved through the simulated use of [https://www.merriam-webster.com/dictionary/assumed assumedly] perfect information, but the perfection of knowledge cannot be attained or simulated.'''
Engineering and Economics, for example, simulate finite elements of perfect knowledge or information, heavily bounded with assumptions and a range of applications. By doing so, these professions reduce the bounds and variability of uncertainty and thereby manage it. Using these hybrid models to acquire knowledge takes place in an economy (regulated or institutional) and environment (transparent and structured), which puts pressure on the various professions to recognize and use qualitative and quantitative knowledge better to reduce uncertainties in their activities. The challenges in this reflect the nature of the profession's or discipline's knowledge (e.g., scientific, engineering, or medical), the policies and structure of the relevant professional knowledge bodies, process objectives and constraints, and "the elusive demands of politics." [6] One common theme runs through all of these efforts to address the presence of uncertainty in professional activities. 
:"The available scientific information upon which a decision must be made is almost always a mixture of engineering knowledge and uncertainty. Regardless of the information or methodology, there is the potential for error. This is true regardless of the relevant science, whether archaeology or aeronautics, economics or engineering, pharmacology or toxicology or epidemiology. Achieving the best social decisions requires not only understanding and using what we know but also appreciating and weighing the extent of our uncertainty. In making the best use of ... information in ... decision-making, it is still true that the beginning of wisdom is knowing what it is we do not know." (Vern, Op. cit., Reformatted and engineering substituted for scientific) 
'''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty to develop explanations, calculate, and make predictions. This professional knowledge converges on but never reaches certainty, producing a "legitimacy" from the fruitfulness of its use.''' <ref>Randomness Is Unpredictability, Antony Eagle, The British Journal for the Philosophy of Science, Vol. 56, No. 4 (Dec., 2005), pp. 749-790 </ref> 
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=== Bias and Uncertainty ===
Up to this point, the concept of error, as outlined in uncertainty, could easily be simulated by a truly random variable. There should be no detectable differences around the unseen or specified statistical mean. However, several instances in experience point to a tendency towards one extreme or a pattern of error, a collective that is in itself an error of errors, namely [https://en.wikipedia.org/wiki/Bias bias].

Arguably, this could be part of the choice uncertainty discussion below, but it makes more sense to be looked at on its own. Acting in the face of uncertainty is an exercise of personal knowledge or experience. Whether using professional knowledge or individual experience, the potential is there to make a series of choices that, in some instances, make errors more likely than they would be if considered in the context of a statistical analysis.

This tendency for an individual to make the same error in a predictable or repeatable manner is termed bias. Personal experience, beliefs, knowledge, data, and models all have inherent built-in factors, errors, or defects that create predictable error patterns when applied in decision-making. Walker (1998) argues implicitly that such bias can also be considered systemic error. This type of uncertainty is difficult to identify except on theoretical grounds when the magnitude and direction of the bias are known. Most of these biases, whether systemic or knowledge-based, individual or practice-based, require disciplined efforts to "validate" such methods, models, and judgments at all layers while bounding the uncertainty.
: This is the challenge to professional knowledge that must be overcome, namely, developing and validating models and methods for managing bounded uncertainty that minimize this tendency towards repeatable groups of error or bias. Therefore, from this point forward, the discussion will refer to 'uncertainty and its proxies, error, and bias.'
===Propagating Error and Uncertainty===
Partitioning uncertainty and its proxies, error, and bias into logically distinct subsets involves or is associated with multiple-step execution or choice frameworks. Combining those measurements or choices into single parameters or choices involves, in some cases, assembling the data/information in a unique or limited range of sequences. This implies that associated errors in observing, calculating, or choosing will have unequal impacts or consequences depending on where in the chain the error occurs. Also implied in this is the possibility that subsequent errors will be causally linked to some degree. (In statistical practice, this depends upon whether the errors are independent of each other or correlated, specifically, co-variant.)
:'''Professions develop knowledge of how uncertainty and its proxies, error, and bias propagate through an ordered and normative series of observations or choices. This allows the profession to prioritize error and bias reduction to achieve an optimized reduction of uncertainty.'''
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===Cognition, Metacognition and Uncertainty===
The concept of choice and the resultant error in uncertainty can be further delimited depending on the role of [https://en.wikipedia.org/wiki/Cognition cognition], [https://en.wikipedia.org/wiki/Metacognition meta-cognition], and independent external elements. The rationale for this argument is that such choice activity in the face of uncertainty is a conscious, cognitive act.

An argument could be made that such cognition is a prerequisite for exercising choice and introducing error, as presented and discussed above. The counterfactual to this argument is the example of uninformed choice. Choices introduce as much as errors or more as informed choices could be made. (???) Choice, in this sense, is indifferent to knowledge. Purposeful choice in an environment of objectives requires knowledge, method, and experience, but uninformed, speculative choice does not.
:"''' Cognition''' is the set of all mental abilities and processes related to knowledge, attention, memory and working memory, judgment and evaluation, reasoning and "computation," problem-solving and decision making, comprehension and production of language, etc. Human cognition is conscious and unconscious, concrete or abstract, as well as intuitive (like knowledge of a language) and conceptual (like a model of a language). Cognitive processes use existing knowledge and generate new knowledge." (Wikipedia)
:"'''Metacognition''' is "cognition about cognition", or "knowing about knowing" and can take many forms. It includes knowledge about when and how to use particular strategies for learning or problem-solving. There are generally two aspects of [https://en.wikipedia.org/wiki/Metacognition metacognition]: knowledge about cognition and regulation of cognition."
Some types of metacognition knowledge are:
:'''Person knowledge''' (declarative knowledge), which is understanding one's own capabilities.
:'''Task knowledge''' (procedural knowledge), which is how one perceives the difficulty of a task, which is the content, length, and type of assignment.
:'''Strategic knowledge''' (conditional knowledge) is one's capability to use strategies to learn information. (Accessed at Wikipedia)
Like metacognitive knowledge, metacognitive regulation or "cognitive control" contains three essential skills.
:'''Planning:''' refers to the appropriate selection of strategies and the correct allocation of resources that affect task performance.
:'''Monitoring:''' refers to one's awareness of comprehension and task performance.
:'''Evaluating:''' refers to appraising the final product of a task and the efficiency at which the task was performed. This can include re-evaluating strategies that were used. (Accessed at Wikipedia)
Nothing has been said up to this point that limits this structure to an individual, or a collective, or two entities making cognitive choices that are incompatible or inconsistent to varying degrees. Interpretations of this scheme include an individual professional exercising judgment in a decision that, in turn, relies upon the collective judgment and decision of the discipline as an underlying basis versus the decision of another individual to oppose/protest or otherwise contest that decision. Thus, the framework of cognitive/meta-cognitive applies to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, economics of regulatory practice, and game theory.
:By solving useful problems in the face of inherent uncertainty, discipline-specific frameworks of cognitive/meta-cognitive activity apply to individuals, collectives, and controversies. In economic theory, this could be recast to cognitive/meta-cognitive roles in the theory of rational choice, the economics of regulatory practice, and game theory.
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===Probability and Uncertainty===
Probability is a [https://en.wikipedia.org/wiki/Coherence_theory_of_truth coherent] approach to uncertainty in the physical and mathematics or "classical" domains such as engineering. [8] and is the "...is certainly the best-known and most widely used formalism for quantifying uncertainty.[9] [https://en.wikipedia.org/wiki/Cox%27s_theorem Cox’s theorem] ("Any measure of belief is [https://en.wikipedia.org/wiki/Isomorphism isomorphic] (but not necessarily equal) to a probability measure") is a well-known argument for the validity of that argument. [Note 5]

Another writer, [https://en.wikipedia.org/wiki/Frank_Knight Knight] (1921,1956), presented the following taxonomy of probabilities: [10]
: '''Classical 'a priori' probability:''' As an idealized model, numerical probabilities are computed based on generalized principles of assigning equal likelihoods and mutually exhaustive possible outcomes (Runde, 1998) to all set members. Such probabilities are assigned to "...outcomes based on a judgment of indifference between those outcomes, that is, based on the absence of any evidence of real influences in play that may render any one outcome more or less probable than any other." (Runde, op. cit.) This builds upon Knights's concept of an 'absolutely homogeneous classification of instances that are completely identical except for [https://en.wikipedia.org/wiki/Indeterminate_(variable) indeterminate] factors. This judgment of probability or logical probability is on the same logical plane as the propositions of mathematics and ultimately [https://en.wikipedia.org/wiki/Inductivism inductions] from experience.'(Knight, 1956, pg.225)
: '''Bayesian 'a priori' probability:''' In contrast to interpreting probability as the "frequency" or "propensity" of some phenomenon, [https://en.wikipedia.org/wiki/Bayesian_probability Bayesian probability] is a quantity that we assign to represent a state of knowledge or a state of belief. In this view, probability is assigned to a hypothesis, often using the basis of past or prior experience. In contrast, under the frequentist view, a hypothesis is typically tested without being assigned a probability based on prior experience. The Bayesian interpretation of probability can be seen as an extension of propositional logic that enables reasoning with hypotheses, i.e., propositions whose truth or falsity is uncertain. Bayesian probability belongs to the category of evidential probabilities; to evaluate the probability of a hypothesis, the Bayesian probabilist specifies some prior probability, which is then updated in the light of new, relevant data (evidence). The Bayesian interpretation provides a standard set of procedures and formulae to perform this calculation. [11]
: '''Statistical or 'a posterior probability:''' Empirical evaluation of the frequency of association between predicates, not analyzable into varying combinations of equally probable or logical, 'a priori' alternatives. Knight argued that any "high degree of confidence" that the judgment that experience will remain valid for future predictions is "...still based on an 'a priori" judgment of indeterminateness." (Knight, Op. Cit.) Knight's argument to support this is what will be discussed further below that first, "...the impossibility of eliminating all factors not really indeterminate; and, second, the impossibility of enumerating the equally probable alternatives involved and determining their mode of combination so as to evaluate the probability by a priori calculation." (Knight, Op. Cit.) Knight noted that the main difference between this form and that of logical or inductive probability was the presence of empirical information, which allowed the analyst to identify propensity and direction in the observed phenomena. In this case, 'a priori' probability values may be derived from first principles and "statistical probabilities are determined a posteriori by the empirical method of counting instances." (Runde [1998] quoting Knight, op. cit.)
:'''Estimates:''' The distinction here is that there is no valid basis ('a priori' or 'a posteriori') for any classifying phenomena. For Knight, this form of probability presented the greatest logical difficulties of all ... but its distinction from the other types must be emphasized, and some of its complicated relations indicated..." (pp. 224-5, emphasis in the original)

Arguably, Knight's concept of probability can be viewed as "..a continuum of probability situations, depending on the degree of homogeneity of the 'instances' in question" [12] ; i.e. going from a logically distinct but equally likely set of elements to a unique set with one member with statistical frequency in the middle. Knight's main motivation for distinguishing between a priori probability and statistical probability underscores his opinion that "(i) the 'mathematical or a priori type of probability is practically never met with in business, while the second is extremely common'; and (ii) 'the statistical treatment never gives closely accurate quantitative results' (Runde quoting Knight pp. 215-16). Runde argues that Knight couldn't conceive of (empirically tabulated) occurrences that are used in daily commerce classes of instances that we have to make do within the course of everyday economic life could be divided into subclasses of instances that are sufficiently homogeneous to permit the determination of what he calls 'real' probability (p. 217).
:'''In modeling degrees of uncertainty, any measure of belief is isomorphic but not necessarily equal to a probability or statistics measure'''.
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===Explanation, Prediction and Uncertainty===
It should be clear that the general notion of uncertainty synonymous with complete ignorance must be bounded as part of a scheme to produce a useful concept of uncertainty in professional practice such as civil engineering.
:The first principle that must be introduced is that such uncertainty must be bounded by reliable and valuable knowledge gained from past experience combined with analytical and computational capabilities to address an immediate and real problem of interest. This could be viewed as the economic interest argument. Only uncertainties associated with physical phenomena and economic scarcity will be addressed.
:The second principle is that the knowledge model is capable of producing statements or assertions in the form of hypotheses that themselves are testable. [https://en.wikipedia.org/wiki/Testability Testability], in this sense, is defined as the property applying to an empirical hypothesis and involves two components:
::The logical property that is variously described as contingency, defeasibility, or falsifiability, which means that counterexamples to the hypothesis are logically possible. Contrast this to a [https://en.wikipedia.org/wiki/Tautology_(logic) logical tautology], which is always true that is true in every possible interpretation. The practical feasibility of observing a reproducible series of such counterexamples if they do exist. In short, a hypothesis is testable if there is some real, non-zero expectation of deciding whether it is true or false of verifiable, reproducible experience. Upon this property of its constituent hypotheses rests the ability to decide whether a theory can be supported or falsified by actual experience data. (Source Wikipedia.)
'''Knowledge models must be capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria described above in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error and bias.'''
===Variability and Aleatory Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge referred to as epistemic uncertainty. Acting in the face of epistemic uncertainty introduces choice uncertainty (discussed below) and results in the production of explanations and predictions. It is implied that there will be testable hypotheses in the form of outcomes of interest to the knowledge professions. Inherent in nature is a variation of results, which may appear to uncertain but isn't. Observations of phenomena will not be the same, and part of the experimental process is to narrow those results down to within an acceptable level of tolerance. Statistical measures seek to identify underlying and unobserved measures of central tendency. These parameters are not directly observed but are derived to a degree of confidence.
In some cases, variation around a mean or within a statistical range may be perfectly acceptable to the knowledge profession. Examples of this are in the physical sciences and medicine. In Engineering and, to a lesser extent, economics, the process or phenomenon may be required to be more controlled to a narrower range than what a natural or "unmanaged" amount of variation would allow. Several courses of action present themselves.
:The first would be to ignore the aleatory phenomenon and choice, which is rejected outright.
:The second would be to assume that, in effect, such variation is itself a form of minimally managed uncertainty and treat it the same as epistemic uncertainty. This is not a desirable outcome as reducing Aleatory error and bias requires
:A third approach would be to acknowledge that process variation is, in some forms, manageable. This means making choices and acting in a way that presumes that the natural occurrence of error can be reduced, or more importantly, bias can be reduced. There is ample precedent for the third approach in civil engineering design and project management. In this sense, the argument is made that the aleatory response to managed efforts results in an elastic variation range. Presumably, an effective management effort either "shifts" the central tendency of the process results or reduces the variation range of occurrence or some other population parameter. Choosing to act and accept that narrower range rather than the broader natural variation may result in an outcome that, while well within the bounds of the expected variation, is outside the arbitrary control limits established by the choice and action. While this phenomenon mimics the broader uncertainty and its proxy error, it is not an error. The information on the variation was known, and the ability to produce narrower results was an error, albeit a pseudo-error, when compared to the broader classes of uncertainty discussed above, such as total ignorance.
::Looking at uncertainty using this third approach means that the error in choosing a new target parameter for this process may contain epistemic errors (our knowledge of the phenomenon may be erroneous, or the model flawed), aleatory uncertainties as discussed, or, more importantly, choice error or bias in thinking that we can move the process results to meet the requirements.
:'''In civil engineering practice, this may mean applying professional judgment in developing partitioning schemes or allocating uncertainty between the three dimensions (Epistemic, Aleatory, and Behavioral) of uncertainty.'''
Lastly, raising the topic like this brings the questions of effectiveness and efficiency to the fore. Advancing knowledge models that reduce error and bias at first are largely choice models resulting in justifications and favorable experiences. At some point in the process, the knowledge profession advances explanations and predictions that require reliable mathematical or logical concepts that are reduced to models and parameters. The question of efficiency becomes important; the broader uncertainty may have been reduced, but the process is economically inefficient. The profession is urged to become more frugal or, for example, reduce the variability of the outcomes. Knowledge professions often operate under conditions that require them to acquire knowledge and reduce uncertainty effectively and efficiently. Doing so requires understanding both epistemic uncertainty, as discussed above, and statistical process variation or aleatory variation.
:'''Knowledge professions manage and reduce uncertainty in the form of error and bias in an economically effective and efficient manner. This effort requires understanding both epistemic and aleatory uncertainty.'''
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===Choice and Behavioral Uncertainty===
The discussion up to this point has focused on uncertainty associated with various forms of knowledge, referred to as epistemic uncertainty, as well as the inherent variations in results, or what was termed aleatory uncertainty. Acting in the face of that epistemic and aleatory uncertainty introduces yet another uncertainty into the process, namely choice uncertainty. This uncertainty reflects uncertainties associated with first-person and third-person choice. This is particularly relevant for engineering with project stakeholders.
===Summary of Semantics Framework for Uncertainty===
Professional disciplines have gradually re-framed their understanding of uncertainty and, thru experience and analysis, have arrived at the following meta-knowledge concepts of uncertainty:
* '''Uncertainty and its proxy, error, have three components: [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic epistemic uncertainty], [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic aleatory uncertainty], and behavioral uncertainty.'''
* '''Driven by practical objectives and benefiting from past experience, knowledge professions such as engineering start by reasoning from uncertainty using non-dogmatic beliefs, which recognize the existence of specific criteria and information that would make the believer change their beliefs using coherent approaches such as probability.'''
* '''Knowledge professions develop knowledge of how uncertainty and its proxy, error, propagate through an ordered and normative series of observations or choices that meet testability criteria in 'reasoning under uncertainty' or 'in the face of uncertainty' with the 'potential for' or 'presence of' error. An error can then be described, explained, and reduced in ways that uncertainty cannot. This allows the professions to prioritize error reduction to achieve an optimized reduction of uncertainty and produce a "legitimacy" from the fruitfulness of its use.'''
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===Uncertainty versus Risk/Hazard===
Up to this point, nothing has been said about risk or hazard. Now, risk can be defined. Simply put risk or hazard are finite, logically distinct subsets of the broader uncertainty. Risk and hazard are currently defined interchangeably. First and foremost, risk is a specific subset of general uncertainty. Any arbitrary subset of general uncertainty can be defined as a risk if it meets the following criteria:
* Uncertainty is associated with the potential for material error when acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena.
* Uncertainty and its proxy, error, can be partitioned into logically distinct subsets associated with multiple-step execution or choice frameworks in physical environments linked to a unique or limited range of sequences.
**Subcriteria 1:Resulting errors will have varying impacts or consequences depending on where the error occurs and the degree of causal linkage or influence in the chain.
**Subcriteria 2:Potential errors can be sequenced and prioritized to achieve an optimized reduction of error or bias and, by proxy, uncertainty over time.
* Uncertainty and its proxies, error bias, and environments are isomorphic to coherent approaches such as probability.
* Uncertainty and its proxies, error, and bias are isomorphic to coherent approaches such as knowledge models capable of producing expressions that can explain phenomena (physical, economic, and social) that meet the testability criteria in 'reasoning under uncertainty.'
This is a definition of general risk. There is no differentiation for engineering or economics, medicine or insurance. There is no difference between a 'good' risk consequence and a 'negative' one. Redefining general risk into simpler terms gives the following:
:'''General Risk is a subset of general uncertainty that is associated with the potential for material errors and biases when sequentially acting or choosing in the face of such uncertainty in an environment of physical, economic, and social phenomena; isomorphic to probability measures and methods and capable of testability.'''
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==Logical Framework for Specific Risk Taxonomies==
As noted above, at the most fundamental level, uncertainty is ".....the subjective perception of ignorance." [13] [Note 6] At this point in the discussion, the focus has moved from discussing uncertainty to its framed subset, risk.
:'''Further, the discussion moves from general risk to a professional or discipline-specific risk [https://en.wikipedia.org/wiki/Taxonomy taxonomy]. Beyond this point, risk will be used in place of uncertainty.'''
Han et al. note that, in general, "(t) taxonomies are valuable not only in their comprehensiveness but in their coherent reduction of uncertainty to conceptually discrete elements."
Uncertainty (and, by implication, risk or hazard) taxonomies offer an approach or tool to more precisely identify and define ontological uncertainty so that it can be quantified, analyzed, and communicated. Viewed this way, uncertainty is not a single, monolithic phenomenon but "...multi-dimensional with theoretically distinct domains and constructs that are potentially measurable and related to different outcomes, mechanisms of action, and management strategies." [14] Taxonomic schemes for classifying the different kinds of scientific and, by extension, engineering uncertainty require identifying the various kinds of potential error associated with descriptive scientific or engineering information and knowledge. An example of this is the following quote from a legal authority:
:"I would especially stress the need for an agency to disclose the uncertainty that surrounds its determinations. '''And by uncertainty, I mean the agency's ignorance as well as its quantitative estimates of error."''' (Emphasis added) [15]
'''Given the necessity of acting in the face of enormous uncertainties [16], discipline taxonomies must offer a clearly coherent and probabilistic approach to uncertainty that is sufficiently general in nature, logically distinct and exhaustive in scope and "...provide decision-makers with a foundation for understanding the nature of discipline information..."''' [17]
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=== Scientific Risk Taxonomies ===
The [https://en.wikipedia.org/wiki/Scientific_method scientific method] is a body of techniques for investigating phenomena in its environment, acquiring new knowledge, or correcting and integrating previous knowledge. Inherent in that process are uncertainties of many forms. Any one taxonomy of [https://en.wikipedia.org/wiki/Uncertainty scientific uncertainty] has largely been focused on statistical models used to assess and quantify sampling, errors, and parameters, although several different taxonomies are conceivable: note-24 [18]
====Environment-specific, Phenomena oriented, model-centric taxonomy of uncertainties====
** [https://en.wikipedia.org/wiki/Uncertainty_quantification#Sources_of_uncertainty '''Parameter uncertainty'''] the source of which is model parameters that are inputs to the computer model (mathematical model) but whose exact values are unknown and cannot be controlled, or whose values cannot be exactly inferred by statistical methods. Subsets of this type of uncertainty include but are not limited to:
***'''Experimental uncertainty''' is also known as observation error, which comes from the variability of experimental measurements. An example is repeating an experiment measurement several times using exactly the same settings for all inputs/variables and recording the variability. It is a subset of the larger uncertainty component, parameter uncertainty.
*** '''Parametric variability''' comes from the variability of the input variables of the phenomena model and is also a subset of the larger uncertainty component, parameter uncertainty.
** '''Structural uncertainty''', or model inadequacy, model [https://en.wikipedia.org/wiki/bias bias] or "[https://en.wikipedia.org/wiki/Systemic_bias systemic bias]", or model discrepancy, which comes from the lack of knowledge of the underlying true state of the phenomenon or its environment. It depends on how accurately a mathematical model describes the true state of the phenomenon or what is termed "model fitness". Due to the inherent nature of uncertainty in any knowledge body (scientific or engineering), models are only an approximation to reality. Therefore, any conclusions drawn from the model can be very misleading when the underlying basis is not plausible or lacks validity. '''note-25 [19]]'''
*** What is missing in this context is any concept of output variability due to the model itself. Part of this is due to the lack of recognition of "process" in scientific investigation. The concept of the scientific method started with a single investigator, such as Galileo or Michael Faraday, working in their laboratories. It has grown into planet and solar system scale experiments such as data gathered on planetary flybys such as Mars, Pluto, and Ceres to the hunt for the Higgs Boson. Ultimately, science has become more like engineering in the scale and complexity of its investigations and theory aggregates, such as the "Unified theory" of particle physics. Subsets of this type of uncertainty include but are not limited to:
*** '''Algorithmic uncertainty''', or numerical uncertainty, comes from numerical errors and numerical approximations per implementation of the computer model. Most models are too complicated to solve exactly. For example, the [https://en.wikipedia.org/wiki/Finite_element_method finite element method] or [https://en.wikipedia.org/wiki/Finite_difference_method finite difference method] [may be used to approximate a solution [https://en.wikipedia.org/wiki/Partial_differential_equation partial differential equation], which introduces numerical errors. Other examples are numerical integration and infinite sum truncation, which are necessary approximations in numerical implementation.
*** '''Interpolation uncertainty''' comes from a lack of available data collected from computer model simulations or experimental measurements. For other input settings that don't have simulation data or experimental measurements, one must interpolate or extrapolate to predict the corresponding responses.
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====Commentary====
A more comprehensive taxonomy of uncertainties that acknowledges knowledge, data, and linguistic uncertainties is:
*'''Epistemic uncertainty''' -[https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Epistemic uncertainty] or systematic uncertainty, in contrast, reflects limitations in the current “state of knowledge” underlying models themselves, originates from competing theories or models, is not readily quantifiable, and is manifest by subjective confusion or indecision. It includes uncertainty due to measurement limitations, insufficient data, extrapolations and interpolations, and variability over time and space. Epistemic uncertainty is uncertainty about "...some determinate fact ... because of a lack of complete information. '''cite_note-26 [20]]''' Epistemic uncertainty can be classified into six main types:'''cite_note-27 [21]]'''
*''' Measurement error''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Observational_error Measurement error] is uncertainty that manifests itself as (apparently) random variation in the measurement of a quantity. Repeated measurements will vary and demonstrate classic statistical behavior.
* '''Systematic error''' - [https://en.wikipedia.org/wiki/Observational_error Systematic error] occurs due to bias in the measuring equipment, model, analysis, observation, or sampling procedure. It is formally defined as the difference between the true value of the quantity of interest and the value to which the mean of the results converges as sample or data sizes increase. Unlike measurement error, it is not (apparently) random, and therefore, results subject to systematic error alone do not vary about a true value. Systematic error can result from deliberate judgment to exclude (or include) data, parameters, or models that ought not to be excluded (or included).
** "The only way to deal with systematic error is to recognize a bias in the process, model, or procedure and remove it. Systematic error, however, is notoriously difficult to recognize except on theoretical grounds. Corrections may only be applied when the magnitude and direction of the bias are known. Such corrections underlie the application of double-sampling methods in environmental science." (Regan, et. al., op. cit.)
*'''Natural variation''' - [https://en.wikipedia.org/wiki/Natural_process_variation Natural variation] or underlying process variation occurs in systems that "...change (concerning time, space, or other variables) in ways that are difficult to predict...across the full range of temporal and spatial values (or other related variables)." '''note-28 [22]'''
** This form of uncertainty is reduceable utilizing tools such as [https://en.wikipedia.org/wiki/Statistical_process_control statistical process control] and replication of experiments or observations.
*'''Inherent randomness''' - [https://en.wikipedia.org/wiki/Randomness randomness] or [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty aleatory uncertainty] exists because the system is"...in principle, irreducible to a deterministic one (the most well-known case is described by [https://en.wikipedia.org/wiki/Uncertainty_principle Heisenberg’s uncertainty principle] in quantum mechanics)." '''note-29 [23], note-30 [Note 7]'''
** Even if the argument is accepted that it is unlikely that any given physical system at some level is inherently random, it is important for any taxonomy to distinguish between systems that appear random due to incomplete or inconsistent information and those that are intrinsically random. (Again, see Regan (2002)).
*'''Model uncertainty''' - [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling Model uncertainty] occurs in the process of generating a model as a conceptual representation of a particular phenomenon to create a [https://web.archive.org/web/20170227055023/http://en.wikipedia.org/wiki/Scientific_modelling#Overview simplified reflection of reality]. This selectivity of factors and variables creates uncertainty in at least three ways. (Regan (2002))
** '''Variables and processes''' that are regarded as relevant often represent a trade-off between system knowledge and assumed states and model objectives;
** '''Models''' depict observed processes using logical or mathematical constructs based on underlying theories about system states or dynamics using continuous equations to describe discrete processes.
** '''Curve fitting''' (including interpolation and extrapolation) with mathematical expressions using model variables where inputs are discrete data points.
** Regan (2002) argues that '''model uncertainty''' is "...notoriously difficult to quantify and impossible to eliminate ...(and)...(t)he only reliable way of determining how appropriate a model is for prediction is to perform validation studies."
** Subjective judgment occurs due to the interpretation of scarce or error-prone data.
*** The only way to address this type of uncertainty is to "...assign a degree of belief about an event in the form of a subjective probability." (Regan (2002))
*Linguistic uncertainty - Linguistic uncertainty, or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. '''note-31 [24]], note-32 [25]]'''
** '''Context dependence''' is uncertainty concerning the context in which a statement is to be understood;
** '''Ambiguity''' occurs when words have multiple meanings, and further reduction to a single meaning is not logically possible in a given context.
** '''Underspecificity''' occurs in the presence of "unwanted generality" or multiple interpretations, and reduction to a smaller set or even a single interpretation is not logically possible.
*Stochastic or statistical uncertainty - Stochastic or statistical uncertainty, which is sometimes known as [https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty Aleatoric uncertainty] as well as the subject of [https://en.wikipedia.org/wiki/Stochastic_control stochastic control] pertains to the parameters of a risk model, originates from sampling or measurement error, and can be quantified and mathematically expressed (e.g., using confidence intervals).
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== Uncertainty Taxonomies in a Legal Environment ==
From a legal perspective, a definition of taxonomy is dictionary-based, such as "the systematic distinguishing, ordering, and naming of type groups within a subject field." given by Walker (op. cit.). Such legal concepts of scientific or engineering taxonomies are not required to be epistemically complete. Still, they must cover "the most significant aspects of ...(current, good discipline practices) ... and of the descriptive information commonly encountered by decision-makers" in a logically distinct manner. (Walker, 1991, p. 571) These taxonomies must be sufficient to catalog the types of uncertainty associated with descriptive assertion, including the critical subset of cause and effect assertions. This legal/decision-maker framework has presented descriptive uncertainty as having six components (Walker, 1991). Although not part of the Walker taxonomy, underlying the scheme is linguistic uncertainty, or "vagueness," as a source of uncertainty, including uncertainties due to context dependence, ambiguity, and under-specificity. '''note-33 [26]]''' Arguably, these uncertainties underlie the most basic element in the taxonomy.
* '''Linguistic '''- or "vagueness", is a source of uncertainty and includes uncertainties due to context dependence, ambiguity, and under-specificity. (Regan, 2002)
* '''Conceptual'''- the definition by choice and design of descriptive concepts or variables to be used as [https://en.wikipedia.org/wiki/Predicate_(grammar) predicates] where the subject of an assertion identifies what is being discussed and the predicate provides information about the topic or characterizations such as what the subject is, what the subject is doing, or what the subject is like (Wikipedia).
* "Conceptual uncertainty, or the potential for conceptual error, arises whenever predication occurs. Whenever a concept is used to describe something, using certain concepts instead of others begins to structure how we understand the object, event, or instance under discussion. Predication or conceptualization generates useful information about things, but it also can inhibit our ability to think about those things with concepts other than those selected. The concepts used may not be the most fruitful or the best designed- either for scientific purposes or for making wise, fair, effective, and efficient decisions." (Edited for reading, Walker, 1991)
* Similarly, choice and design of descriptive variables enhance and restrict data set membership, creating potentially incomplete, incompatible, or inconsistent data sets, "...the classification categories employed, and the relationships among those categories (nominal, ordinal or scalar)." '''note-34 [27]]'''
* Concept uncertainty '''note-35 [28]''' like systematic error discussed above is notoriously difficult to recognize, except on theoretical grounds, and the magnitude and direction of the bias are known.
Both terms could be analogized to that of [https://en.wikipedia.org/wiki/Accuracy_and_precision#Terminology_of_ISO_5725 accuracy] in [https://www.bipm.org/en/publications/guides/ ISO 5725] which looks at the ability of the concept or system's ability to produce results that are proximate to the "true" value or state of the phenomenon. Conceptual uncertainty and systemic bias are related to another measure of uncertainty: validity. Walker has defined validity as the ability of a concept to quantitatively describe and explain what the phenomenon objective "truly" is. Walker also ties the concepts together when he states, "Validity concerns the "accuracy" of the measurement data, not its precision." (Walker, 1998) 
Additionally, conceptual uncertainty and associated validity issues could be propagated throughout this taxonomy. Every component of action and decision in the face of uncertainty raises validity issues. Walker recognized this when the author defined epistemic choice for concepts used throughout the other uncertainty components or layers.
* '''Measurement''' uncertainty or Misclassification error- the application of the underlying concepts or variables to specific, individual cases and the uncertainty of the reliability (Walker, 1998) of the resultant data. (It is important to remember that assessment observation and measurement are used interchangeably in this analysis.) Thus, an assessment method or procedure is considered "reliable" or "precise" in the scientific terms of ISO 5715 if it repeatedly produces consistent results.
* Measurement uncertainty is logically distinct from conceptual uncertainty, which can only occur after any conceptual uncertainty has been established. Still, misclassification can occur even when the conceptual uncertainty has been minimized. Conversely, underlying errors or errors made in measurement or classification propagate thru the reasoning chain and reduce the quality or value of the final result.
* While uncertainties associated with quantitative measurements or assessments can be statistically evaluated and described, uncertainties related to qualitative assessments are more problematic.
**Working through the taxonomic chain from base concept to causal explanation requires amassing and integrating qualitative information and quantitative data into input parameters for mathematical models. Developing procedures for producing consistent, repeatable sets of information from assessments that minimize conceptual error or systemic bias requires professional efforts to "validate" such methods, models, and judgments at all layers in the process. Validation as a process is something that civil engineering as a practice needs to embrace more frequently.
* '''[https://en.wikipedia.org/wiki/Sampling Sampling]'''- in the classical statistics sense, is the selection of a limited subset of a larger population to use as a basis for making estimates about the population in the form of attributes, proprieties, or parameters. The resulting information would be determinative in making forecasts about future population sampling or possessing a desired amount of predictive power. The uncertainty about the ability of this information to correctly predict future samples is termed sampling error.
* '''Modeling'''- Modeling uncertainty arises whenever a claim is made that out of a class of candidate relationships and variable pairings, one variable pairing inclusive of constants is chosen that possesses a particular or persistent mathematical relationship to another variable X. Modeling errors arise in selecting the wrong variable pair and constants or by incorrectly specifying its constants. (Walker, 1991, pg. 599)
** '''Causal'''- In causal modeling and analysis, the relationship between causal variables is not strictly a mathematical function. Still, it makes testable predictions and explains "...how a system of variables works, why a system works the way it does, or why it makes sense to think of certain variables as a system" at all." (Walker, 1991, pg. 609)
** '''Epistemic'''- the choice of interpretations for fundamental, logical concepts used throughout the other components or layers.
The components of this uncertainty taxonomy can combine in different ways to "...produce different aggregate uncertainties." '''note-36 [29]'''
:''' Only the scientific and engineering professions possess the capacity to develop and validate procedures and models that combine the different kinds of qualitative '''note-37 [30]''' and quantitative information and their associated potential for error into an aggregate measure or "scalar variables" of uncertainty.'''note-38 [31]]
Scalar variables are quantitative variables whose categories are related by some measure of the relevant property's incremental frequency, degree, or amount. (Walker, 1991) Examples are Modulus of Elasticity, Compression stress in elastic materials, etc.

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=== Economics Uncertainty Taxonomies ===
Like scientific concepts of uncertainty, economics is environment-specific (in this case, read sector of economy or markets), phenomenon-oriented (in this case, read human activity and manufactured phenomena such as firms and markets guided by human judgment and decision), and model-centric.
:''' Economics is not interested in all forms of uncertainty, only specific, meaningful subsets possessing casual connections with material impacts, often labeled as 'risks'.'''
One of its earlier writers ([https://en.wikipedia.org/wiki/Frank_Knight#Life_and_career Knight], 1921, 1948, 1957) attempted to define "uncertainty" as the presence of "defects of managerial knowledge" (or [https://en.wikipedia.org/wiki/Knightian_uncertainty knightian uncertainty] and thus risk centric) as risk that is immeasurable, not possible to calculate.(Wikipedia) Risk in this sense was defined as "the ordinary risks of business activity which can ... be reduced... by applying the insurance principle." Knight does acknowledge that risk is a specific and unambiguous subset of uncertainty. (Risk, Uncertainty and Profit, 1957, pg. 19) For Knight, measuring uncertainty meant measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences, which Knight referred to as "the imperfection of knowledge". (Risk, Uncertainty and Profit, 1957, pg. 197) One of Knight's many contributions to this analysis was recognizing information and knowledge's role in economic activity.
:"''(W)e are concerned only to emphasize the fact that '''knowledge is in a sense variable in degree and that the practical problem may relate to the degree of knowledge rather than to its presence or absence in toto.''' We live only by knowing something about the future, while the problems of life, or conduct at least, arise from knowing so little. This is as true of business as of other spheres of activity. The situation's essence is action according to opinion, of greater or less foundation and value, neither entire ignorance nor complete and perfect information, but partial knowledge. If we are to understand the workings of the economic system, we must examine the meaning and significance of uncertainty; and to this end, some inquiry into the nature and function of knowledge itself is necessary.''" (Knight, pg. 199) Emphasis added
Knight also reinforced the difficulties in classifying uncertainties when he analyzed life insurance. In this case, accidental death was an uncertain phenomenon compared to sickness and accident, where an "...objective description and classification of cases was impossible..." (Knight, pg. 248) Another factor that made uncertainties ineligible as risks and, therefore, uninsurable because they were unclassifiable was the exercise of judgment in making decisions by the businessman. (Knight, pg. 251)
This is a simple classification of economic uncertainty and, as an idealization, is philosophically controversial. '''note-39 [32]'''
* Uncertainty and information about the economic environment are distinct from uncertainty about others’ behavior or choices.
* Risk as a specific subset of uncertainty implies that "...all possible acts are known, all possible outcomes arising from each act are known, and it is possible to assign probabilities to each act." '''note-40 [33]'''
Like the economic [https://en.wikipedia.org/wiki/Theory_of_the_firm theory of the firm], an economic theory of uncertainty seeks to explain and predict the nature of a specific subset of uncertainty (in this case, risk relating to physical phenomena and human activity), including its behavior, structure, and relationship to the universal uncertainty of physical phenomena. In this sense, uncertainty, like firms and markets, can establish price equilibriums under the right circumstances different from those that might have occurred in either of the other two. Such a theory of uncertainty offers a partitioning scheme that decomposes uncertainty into distinct and meaningful subsets, of which risk is a significant one. It also explains how the presence and variability of knowledge cause or " drive" economic consequences. Unlike physical phenomena, economic activity creates artificial objects such as products, firms, mediums of exchange, markets, economies, and knowledge about those activities. The economics of uncertainty exists as an alternative to "certainty" models that assume away imperfect knowledge and unknown choice/judgment preferences when it is more efficient for economic decision-making. This also allows the introduction of the logical concept of "hazard" versus "risk". Frank used hazard extensively in Risk, Uncertainty, and Profit, but not to the extent that one could say it was interchangeable. An example of this is in the term "[https://en.wikipedia.org/wiki/Moral_hazard Moral hazard]," where a firm could engage in "riskier" and potentially "uninsurable" behavior if the information were transparent, and, therefore, insurance coverage of losses is limited. note-41 [34]], note-42 [35]]

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=== Uncertainty and Risk in Policy and Regulatory Environment ===
:''"Hume's 'just reasoner', when faced with a difficult public policy decision in current times, would probably commission a risk analysis. But how could they incorporate 'a degree of doubt, caution, and modesty?"'' '''note-43 [36]'''
The problem of "decision-making in the face of uncertainty" is how to regulate based on incomplete information that has the potential to be materially inaccurate. A contextual assumption is that we need to evaluate decision rules for dealing with uncertainty. As a social enterprise, risk regulation, whatever its substantive objectives, should be as '''effective, efficient, and equitable''' as practically possible. These "three E's" form a set of "process objectives" or "meta-goals." From the uncertainty standpoint, causal information can be usefully divided into two major categories: information about groups and information about individuals.

Each category, group, and individual has its distinctive types of inherent uncertainty. These are logically distinct, generally independent, and cumulative, contributing to '''(?????)'''

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=== Engineering Uncertainty Taxonomies ===
Like scientific and economic concepts of uncertainty, engineering is environment-specific, phenomena-oriented, model-centric, external choice, and driven by decision-making in the face of uncertainty. Like economics and unlike science, engineering is not interested in all forms of uncertainty, only those with determinable and material consequences. Like economics, engineering conceives risk as a specific and unambiguous subset of uncertainty determined by measuring the probability of its occurrence and the severity of its consequences or economic impact, e.g., measurable risk of loss versus unmeasurable uncertainty consequences. Also, engineering recognizes the role information and, by extension, knowledge plays in constructing the built environment.
'''note-44 [37]]''' Like economics, engineering differs from economics and scientific uncertainty in its interest in behavioral uncertainty.
*"Behavioral or interaction uncertainty is how individuals or organizations act or interact. Behavioral uncertainty arises from four sources: design uncertainty, requirement uncertainty, volitional uncertainty, and human errors." '''note-45 [38]]'''
** A design uncertainty is a choice among alternatives over which an individual or group of individuals exercises direct control but has not yet decided upon.
** Requirement uncertainty includes parameters of interest to and determined by the stakeholder, independent of the engineer or designer.
** Volitional uncertainty is uncertainty about what the subject him/herself will decide. Other people’s future actions and conduct are not entirely predictable, particularly when dealing with other organizations.
** Human errors occur during the development of a system or project due to blunders or mistakes by an individual or individuals.

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==Practice Frameworks for Uncertainty and Risk in Civil Engineering Practice==
===PMIBoK===
PMI does not define uncertainty, but the PMI Body of Knowledge uses the term in 45 places (uncertainty) and as describing properties in 8 places (uncertainties). [See Note 8] PMI does define "risk" and uses the term over 1,300 times in the same document. In its glossary, PMI also defines terms such as "threat" and "opportunity" but not events.[391 The BoK contextually defines uncertainty when it states that project risk has its origins in the uncertainty present in all projects. (Op. Cit., pg 309) 
'''Uncertainty''' is contextually defined and taken as having the property of affecting project execution and the ability to meet stakeholder expectations. Uncertainty is more than the sum of all individual risks (known and unknown). The nature of this uncertainty is not defined, only its capacity to affect something else or its properties. 
'''Risk''' is defined as " ... an uncertain event or condition that, if it occurs, has a positive or negative effect on one or more project objectives." (Ibid.) Beyond this formal definition, PMI adds context when, in its risk management section (Sec. 11 ), the BoK states that risk is a caused event or condition with multiple causes and impacts. The BoK even offers specific techniques for mapping cause-and-effect relationships and influence diagrams. (Sec. 11.2.2.5) Not as evident but equally important is the recognition that not only is risk caused but the impact is "triggered". The risk trigger is an event or situation that signals that it is about to occur (Glossary, pg. 566). Risk conditions are factors that affect or otherwise contribute to risk, such as project stakeholders. Risk implicitly retains some residual element of probability in it, or a value of less than 1.0 with risk that approaches a level of near certainty is termed "issues" or "realized risk" .(Op. Cit., pg 309). PMI also links risk to underlying variations in project outcomes. (Ibid.) Risk is based upon data and information which can be assessed as to its quality (Sec. 11.3.2.3). This analysis looks to determine the value of the information and examines the degree to which the risk information is understood as well as its " ... accuracy, quality, reliability and integrity ... " (Ibid.) Part of that is understanding the relationship between occurrence and impact as outlined in PMl's Figure 11-10 Risk Impact Matrix and developing a model for prioritizing risk products and setting the threshold for action.
====Commentary:====
For defining and identifying risk, PMI notes in Sec. 11.2.2.4 that " ... Every project and its plan is conceived and developed based on a set of hypotheses, scenarios, or assumptions." Part of the risk analysis process is identifying risks to the project, such as inaccuracy, instability, inconsistency, or incompleteness of assumptions. Similarly, the quality of management plans, as well as their consistency with others and the project objectives and assumptions are 11 •• .indicators of risk in the project." (Sec. 11.2.2.1) 
Similarly, the role of engineering economics in risk is embedded or implied in the PMI definition of risk when the BoK discusses risk identification as a process of assessing which risks out of the total risk exposure for the project "may affect" the project and distilling their characteristics. Embedded in this statement is the concept that not all risks can cause economic impacts (positive and negative) to the project. Also embedded is the requirement to understand risk characteristics to identify project risk (Cost and schedule). Lastly, risk identification is an iterative process of incrementing project risk documentation, much like project scope, cost, and schedule documentation. 
Lastly, PMI acknowledges the complex constitution of risk aggregates or the "sum" of all risks in its material, but is it a sum? Risk information has descriptive and explanatory content. These are knowledge and meta-knowledge components. Understanding project assumptions is project-specific knowledge, but identifying risks from assumption inaccuracy, instability, inconsistency, or incompleteness requires professional or program meta-knowledge. 
'''Uncertainty exists in all projects, but risk, a subset of uncertainty with material economic impacts, is a key meta-knowledge element for civil engineering practice.'''
===Software Development Process- Risk Focused===
The Unified Process requires the project team to focus on addressing the most critical risks early in the project life
cycle. The deliverables of each iteration, especially in the Elaboration phase, must be selected to ensure that the greatest risks are addressed first. <ref> Booch, Grady, Ivar Jacobson, and James Rumbaugh. "The unified software development process."
Reading: Addison Wesley (1999).</ref>
===American Society of Civil Engineers (ASCE)===
ASCE does not define uncertainty, but the ASCE CE Body of Knowledge (CEBoK) uses the term in 22 places (uncertainty) and describes properties in 13 places (uncertainties). 
The CEBoK does not use the phrase "uncertainty and risk" as PMI and AACE do in their publication. ASCE reverses the two (Risk and uncertainty) in twelve places regarding technical outcomes (Outcome 12). A fundamental difference between PMI and ISO is that risk is considered a subset of uncertainty. ASCE does not define "risk" but uses the term 25 times in the same document. Almost all are in the context of variation of design parameters and not in the classical context of cost, schedule uncertainty, and risk.
====Commentary:====
...
=== American Association of Cost Engineers (AACE) ===
AACE defined uncertainty, risk, and other related terms in its Risk Management Dictionary. <ref>Risk Management Committee uRisk Management Dictionary, Cost Engineering Vol. 37/No. 10, OCTOBER 1995. </ref>
AACE's overall strategy was to define risk and other terms that stem from base uncertainty. AACE first defined uncertainty as .... "(t)he total range of events that may happen and produce risks (including both threats and opportunities) affecting a project (see opportunities, events, conditions, risk, and threats" where the following sub-definitions apply:
** Biases--A lack of objectivity based on the individual's position or perspective. Systematic and predictable relationships between a person's opinion or statement and his/her underlying knowledge or circumstances. Note: there may be "system biases" as well as "individual biases."
** Condition (Uncertain Condition)-Any specific identifiable circumstance (such as the rate of inflation or the quality of labor available) that might affect the outcome of the project
** Event (Uncertain Condition)-is a specific identifiable action (such as a large government project being started in the same labor area as your project) or an act of nature that might happen and that (if it does happen) could affect the outcome of the project.
** Opportunities are Uncertain events that could improve the results or improve the probability that the desired outcome will happen
** Threats are Uncertain events that are potentially negative or reduce the probability that the desired outcome will happen.
*** AACE defines Risk as an "ambiguous term" (sic) that is synonymous with uncertainty or a negative subset such as "threats", or an overall negative impact of all possible uncertainties.
====Commentary:====
...
==Limitations of the definition==
...
==Working definition==
...
==Beneficial outcomes==
...
==See also==
Wiki articles on the project.

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==Notes==
<references group="note" />

# See McDaniels et al. 2004 for an extensive discussion and bibliography on the historical development of Risk Analysis and some key milestones in the 20th century.
# Open item
# The Merriam-Webster dictionary defines uncertainty:
::"the quality or state of being uncertain," which is a circular definition. Likewise, one could define uncertainty as the state of not being certain. Synonyms are distrust, doubt, misgiving, mistrust, reservation, skepticism, and suspicion. Another writer added indefinite, indeterminate, not certain to occur, problematical, unreliable, untrustworthy, unknown beyond doubt, dubious, doubtful, not clearly identified or defined, not constant, variable, and fitful to the list.
::Han, Paul KJ, William MP Klein, and Neeraj K. Arora. "Varieties of Uncertainty in Health Care: A Conceptual Taxonomy." Medical Decision Making 31.6, (2011 ): 828-838.
::: Han, ct. al. noted that any definition of uncertainty encompasses " ... numerous types, sources, and manifestations of uncertainty, and ... (any) ... useful working definition of uncertainty needs to specify the concept underlying these varied meanings of the term." (Ibid.)
<ol start="4">
<li> Implicit in this definition of uncertainty as a "state of' is ... </li>
: " ... a conceptualization of uncertainty as a subjective, cognitive experience of people--a state of mind rather than a feature of the objective world. Furthermore, the defining feature of this state appears to be a lack of knowledge about some aspect of reality. Importantly, however, the concept of uncertainty also implies a subjective consciousness or awareness of one's lack of knowledge, without which one could not feel uncertain; '''uncertainty is a form of "meta-cognition" ... ( or alternatively, its main component, meta-knowledge) ... -a knowing about knowing.'''" (Han, 2011, op. cit., Emphasis added)
</ol>
<ol start="5">
<li> Cox wanted his system to satisfy the following conditions:</li>
: Divisibility and comparability- The plausibility of a statement is a real number and depends on the information we have related to the statement.
: Common sense - Plausibilities should vary sensibly with the assessment of plausibilities in the model.
: Consistency - If the plausibility of a statement can be derived in many ways, all the results must be equal.
Cox's theorem has come to be used as one of the justifications for the use of Bayesian probability theory. (For example, in Jaynes Jayne, Probability Theory: The Logic of Science, Cambridge University Press (2003). -preprint version (1996) at http://omega.albany.edu:8008/JaynesBook.html; Chapters 1 to 3 of published version at http://bayes.wustl.edu/etj/prob/book.pdf
</ol>
<ol start="6">
<li> The utility of existing taxonomies of uncertainty in civil engineering and, by extension, "risk" has been unsatisfactory for this very reason. This is particularly true in cases where the source of the uncertainty is conflicting versus incomplete information. See Regan, et. al.,"A taxonomy and treatment of uncertainty for ecology and conservation biology" (2002)
for a survey of scientific taxonomies for uncertainty.</li>
<li> Regan et al. argue that " ... genuine examples of this kind of uncertainty are hard to find. Even classic cases of random experiments like coin tosses and the throwing of dice are deterministic; it is just that we do not have enough information about the dynamic processes and initial conditions to make any sensible estimates about the outcomes. Such processes are, for all intents and purposes, inherently random, but they are not genuinely inherently random. For similar reasons, complex systems such as ecosystems and weather patterns are unlikely to be inherently random. Similarly, chaotic systems are entirely deterministic. They are unpredictable because the deterministic processes generating them and the relevant initial conditions are hard
to fully specify (see Stewart 1989, Sugihara et al. 1990)." (Regan (2002) </li>
<li> The CEBoK uses the phrase "uncertainty and risk" twice in reference to scheduling (Section 6.5,2) and cost estimating (Section 7.2,2) as well as reversing the two (Risk and Uncertainty) also in two places in discussing project life cycle (both in Section 2.4.1) </li>
</ol>

==References==
<references />