Uncertainty and Risk in Civil Engineering Practice

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.

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:

1. distinction between certainty and uncertainty;
2. refinement of uncertainty into epistemic, aleatory, and behavioral components;
3. use of error and bias as operational proxies for uncertainty;
4. 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.

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 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 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

• • 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 bias or "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 finite element method or finite difference method [may be used to approximate a solution 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 -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 - 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 - 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 - 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 statistical process control and replication of experiments or observations.
• Inherent randomness - randomness or aleatory uncertainty exists because the system is"...in principle, irreducible to a deterministic one (the most well-known case is described by 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 - Model uncertainty occurs in the process of generating a model as a conceptual representation of a particular phenomenon to create a 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 Aleatoric uncertainty as well as the subject of 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 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 accuracy in 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.
• 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 (Knight, 1921, 1948, 1957) attempted to define "uncertainty" as the presence of "defects of managerial knowledge" (or 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 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 "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.

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. 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

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 alternatively, its main component, meta-knowledge) ... -a knowing about knowing." (Han, 2011, op. cit., Emphasis added)

5. Cox wanted his system to satisfy the following conditions:

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

6. 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.
7. 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)
8. 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)