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

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 ... " cite note-2 1/23

(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. [Note 1]
As outlined below, professions such as civil engineering have been successful in acting in the face of uncertainty lies in the profession's ability to reduce a broad variety of uncertainties into a series of 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. Note 2

Semantics framework

Certainty and Uncertainty

There is no such thing as absolute certainty, but there is assurance sufficient for the purposes of human life. (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. (Wittgenstein # 115 from On 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 ( descriptive or declarative or propositional knowledge)
• Knowledge how ( or "know-how"), and
• knowledge by acquaintance.

Certainty

Certainty has been defined as "an epistemic property" of knowledge in all its forms and the state of our beliefs about that knowledge. [3] Certainty about any belief about knowledge in any form 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. [4] 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. There is an underlying requirement for an empirical framework of test-ability and falsification to recognize this to limit dogmatism. One could argue that they would change their minds if God asked them to do so. 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 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 for any such system of knowledge, there will be statements that are unprovable within the system. Secondly, the knowledge system cannot demonstrate its own consistency. (Godel'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) (Physicist Carlo Rovelli, Source: Wikipedia)

Uncertainty

Uncertainty, on the other hand, arises immediately in the presence of the slightest amount of doubt or criticism. Note 3 Similarly, uncertainty could be defined as " ... any departure from the unachievable goal of complete determinism." [5) Reasoning under uncertainty differs from 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. Note 4 Knowledge professionals reason in a state of incomplete, inconsistent, and limited knowledge, with doubts that cloud statements or assertions and taint 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 contingent nature of uncertainty logically implies the contingent nature of error.
Knowledge Professions use error as a proxy for uncertainty to manage uncertainty within discipline knowledge frameworks. 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. They are explainable and predictable to such a degree that they interest 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, 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 '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 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. [Cite?]

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:

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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:

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Limitations of the definition

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Working definition

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Beneficial outcomes

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See also

Wiki articles on the project.

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Notes

1. 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.
2. Open item
3. 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.)

4. 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)