Uncertainty and Risk in Civil Engineering Practice - Revision history

Pooyan: Restore native revision 219 (24 Nov 2024); add provenance note and link to 2026 taxonomy draft

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Restore native revision 219 (24 Nov 2024); add provenance note and link to 2026 taxonomy draft

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 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==
+==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.
-==Working definition==
-...
+Several limitations are inherent:
 ==Beneficial outcomes==
-...
+* 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.
 ==See also==
-Wiki articles on the project.
+Project definition
 [[#top|Top of current page]]
 ==Notes==
 <references group="note" />
-==References==
+# Uncertainty as meta-knowledge
-<references />
+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.

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

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	=== Economics ~~Uncertainty Taxonomies~~ ===		===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~~.		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 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~~.		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.
	~~:"''(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)~~		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.
	~~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.		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.
	* 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]]~~		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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WikiSysop: 2026 01 19 /* Scientific Risk Taxonomies */ GPT edit

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	=== Scientific ~~Risk Taxonomies~~ ===		===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]~~		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.
	====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:~~		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.
	***'''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~~.		====Environment-specific, phenomenon-oriented, model-centric taxonomy of 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]]'''~~		A widely used scientific taxonomy distinguishes uncertainty according to its relationship to models and data:
	*** 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~~.		* '''Parameter uncertainty'''
	*** '''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.		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.
	~~[[#top\|Top of current page]]~~		** '''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

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

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Logical Framework for Specific Risk Taxonomies

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	<ref>Vern citing Ruckelshaus, Science. Risk, and Public Policy, 221 SCIENCE 1026, 1027 (1983)</ref>,		<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"/>		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 ===		=== Scientific Risk Taxonomies ===

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

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Logical Framework for Specific Risk Taxonomies

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	''' Given the necessity of acting in the face of enormous uncertainties		''' 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>,		<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=		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 ===		=== Scientific Risk Taxonomies ===
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	*** 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.		*** 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:====		====Commentary:====
	...		...

	==Limitations of the definition==		==Limitations of the definition==
	...		...