Deep Uncertainty. Walker, W. E., Lempert, R. J., & Kwakkel, J. H. In Gass, S. I. & Fu, M. C., editors, Encyclopedia of Operations Research and Management Science, pages 395–402. Springer US.
Deep Uncertainty [link]Paper  doi  abstract   bibtex   
[Excerpt: Introduction] The notion of uncertainty has taken different meanings and emphases in various fields, including the physical sciences, engineering, statistics, economics, finance, insurance, philosophy, and psychology. Analyzing the notion in each discipline can provide a specific historical context and scope in terms of problem domain, relevant theory, methods, and tools for handling uncertainty. Such analyses are given by Agusdinata (2008), van Asselt (2000), Morgan and Henrion (1990), and Smithson (1989). [] In general, uncertainty can be defined as limited knowledge about future, past, or current events. With respect to policy making, the extent of uncertainty clearly involves subjectivity, since it is related to the satisfaction with existing knowledge, which is colored by the underlying values and perspectives of the policymaker and the various actors involved in the policy-making process, and the decision options available to them. [] Shannon (1948) formalized the relationship between the uncertainty about an event and information in ” A Mathematical Theory of Communication.” He defined a concept he called entropy as a measure of the average information content associated with a random outcome. Roughly speaking, the concept of entropy in information theory describes how much information there is in a signal or event and relates this to the degree of uncertainty about a given event having some probability distribution. [] Uncertainty is not simply the absence of knowledge. Funtowicz and Ravetz (1990) describe uncertainty as a situation of inadequate information, which can be of three sorts: inexactness, unreliability, and border with ignorance. However, uncertainty can prevail in situations in which ample information is available (Van Asselt and Rotmans 2002). Furthermore, new information can either decrease or increase uncertainty. New knowledge on complex processes may reveal the presence of uncertainties that were previously unknown or were understated. In this way, more knowledge illuminates that one's understanding is more limited or that the processes are more complex than previously thought (van der Sluijs 1997). [] Uncertainty as inadequacy of knowledge has a very long history, dating back to philosophical questions debated among the ancient Greeks about the certainty of knowledge, and perhaps even further. Its modern history begins around 1921, when Knight made a distinction between risk and uncertainty (Knight 1921). According to Knight, risk denotes the calculable and thus controllable part of all that is unknowable. The remainder is the uncertain - incalculable and uncontrollable. Luce and Raiffa (1957) adopted these labels to distinguish between decision making under risk and decision making under uncertainty. Similarly, Quade (1989) makes a distinction between stochastic uncertainty and real uncertainty. According to Quade, stochastic uncertainty includes frequency-based probabilities and subjective (Bayesian) probabilities. Real uncertainty covers the future state of the world and the uncertainty resulting from the strategic behavior of other actors. Often, attempts to express the degree of certainty and uncertainty have been linked to whether or not to use probabilities, as exemplified by Morgan and Henrion (1990), who make a distinction between uncertainties that can be treated through probabilities and uncertainties that cannot. Uncertainties that cannot be treated probabilistically include model structure uncertainty and situations in which experts cannot agree upon the probabilities. These are the more important and hardest to handle types of uncertainties (Morgan 2003). As Quade (1989, p. 160) wrote: ” Stochastic uncertainties are therefore among the least of our worries; their effects are swamped by uncertainties about the state of the world and human factors for which we know absolutely nothing about probability distributions and little more about the possible outcomes.” These kinds of uncertainties are now referred to as deep uncertainty (Lempert et al. 2003), or severe uncertainty (Ben-Haim 2006). [Levels of Uncertainty] Walker et al. (2003) define uncertainty to be ” any departure from the (unachievable) ideal of complete determinism.” [] For purposes of determining ways of dealing with uncertainty in developing public policies or business strategies, one can distinguish two extreme levels of uncertainty – complete certainty and total ignorance – and five intermediate levels (e.g. Courtney 2001; Walker et al. 2003; Makridakis et al. 2009; Kwakkel et al. 2010d). In Fig. 1, the five levels are defined with respect to the knowledge assumed about the various aspects of a policy problem: (a) the future world, (b) the model of the relevant system for that future world, (c) the outcomes from the system, and (d) the weights that the various stakeholders will put on the outcomes. The levels of uncertainty are briefly discussed below. Complete certainty is the situation in which everything is known precisely. It is not attainable, but acts as a limiting characteristic at one end of the spectrum. [::] Level 1 uncertainty (A clear enough future) represents the situation in which one admits that one is not absolutely certain, but one is not willing or able to measure the degree of uncertainty in any explicit way (Hillier and Lieberman 2001, p. 43). Level 1 uncertainty is often treated through a simple sensitivity analysis of model parameters, where the impacts of small perturbations of model input parameters on the outcomes of a model are assessed. [::] Level 2 uncertainty (Alternate futures with probabilities) is any uncertainty that can be described adequately in statistical terms. In the case of uncertainty about the future, Level 2 uncertainty is often captured in the form of either a (single) forecast (usually trend based) with a confidence interval or multiple forecasts (scenarios) with associated probabilities. [::] Level 3 uncertainty (Alternate futures with ranking) represents the situation in which one is able to enumerate multiple alternatives and is able to rank the alternatives in terms of perceived likelihood. That is, in light of the available knowledge and information there are several different parameterizations of the system model, alternative sets of outcomes, and/or different conceivable sets of weights. These possibilities can be ranked according to their perceived likelihood (e.g. virtually certain, very likely, likely, etc.). In the case of uncertainty about the future, Level 3 uncertainty about the future world is often captured in the form of a few trend-based scenarios based on alternative assumptions about the driving forces (e.g., three trend-based scenarios for air transport demand, based on three different assumptions about GDP growth). The scenarios are then ranked according to their perceived likelihood, but no probabilities are assigned, see Patt and Schrag (2003) and Patt and Dessai (2004). [::] Level 4 uncertainty (Multiplicity of futures) represents the situation in which one is able to enumerate multiple plausible alternatives without being able to rank the alternatives in terms of perceived likelihood. This inability can be due to a lack of knowledge or data about the mechanism or functional relationships being studied; but this inability can also arise due to the fact that the decision makers cannot agree on the rankings. As a result, analysts struggle to specify the appropriate models to describe interactions among the system's variables, to select the probability distributions to represent uncertainty about key parameters in the models, and/or how to value the desirability of alternative outcomes (Lempert et al. 2003). [::] Level 5 uncertainty (Unknown future) represents the deepest level of recognized uncertainty; in this case, what is known is only that we do not know. This ignorance is recognized. Recognized ignorance is increasingly becoming a common feature of life, because catastrophic, unpredicted, surprising, but painful events seem to be occurring more often. Taleb (2007) calls these events ” Black Swans.” He defines a Black Swan event as one that lies outside the realm of regular expectations (i.e., ” nothing in the past can convincingly point to its possibility”), carries an extreme impact, and is explainable only after the fact (i.e., through retrospective, not prospective, predictability). One of the most dramatic recent Black Swans is the concatenation of events following the 2007 subprime mortgage crisis in the U.S. The mortgage crisis (which some had forecast) led to a credit crunch, which led to bank failures, which led to a deep global recession in 2009, which was outside the realm of most expectations. Another recent Black Swan was the level 9.0 earthquake in Japan in 2011, which led to a tsunami and a nuclear catastrophe, which led to supply chain disruptions (e.g., for automobile parts) around the world. [::] Total ignorance is the other extreme on the scale of uncertainty. As with complete certainty, total ignorance acts as a limiting case. [] [...]
@incollection{walkerDeepUncertainty2013,
  title = {Deep Uncertainty},
  booktitle = {Encyclopedia of {{Operations Research}} and {{Management Science}}},
  author = {Walker, Warren E. and Lempert, Robert J. and Kwakkel, Jan H.},
  editor = {Gass, Saul I. and Fu, Michael C.},
  date = {2013},
  pages = {395--402},
  publisher = {{Springer US}},
  doi = {10.1007/978-1-4419-1153-7\\_1140},
  url = {http://mfkp.org/INRMM/article/13687645},
  abstract = {[Excerpt: Introduction] The notion of uncertainty has taken different meanings and emphases in various fields, including the physical sciences, engineering, statistics, economics, finance, insurance, philosophy, and psychology. Analyzing the notion in each discipline can provide a specific historical context and scope in terms of problem domain, relevant theory, methods, and tools for handling uncertainty. Such analyses are given by Agusdinata (2008), van Asselt (2000), Morgan and Henrion (1990), and Smithson (1989).

[] In general, uncertainty can be defined as limited knowledge about future, past, or current events. With respect to policy making, the extent of uncertainty clearly involves subjectivity, since it is related to the satisfaction with existing knowledge, which is colored by the underlying values and perspectives of the policymaker and the various actors involved in the policy-making process, and the decision options available to them. 

[] Shannon (1948) formalized the relationship between the uncertainty about an event and information in ” A Mathematical Theory of Communication.” He defined a concept he called entropy as a measure of the average information content associated with a random outcome. Roughly speaking, the concept of entropy in information theory describes how much information there is in a signal or event and relates this to the degree of uncertainty about a given event having some probability distribution.

[] Uncertainty is not simply the absence of knowledge. Funtowicz and Ravetz (1990) describe uncertainty as a situation of inadequate information, which can be of three sorts: inexactness, unreliability, and border with ignorance. However, uncertainty can prevail in situations in which ample information is available (Van Asselt and Rotmans 2002). Furthermore, new information can either decrease or increase uncertainty. New knowledge on complex processes may reveal the presence of uncertainties that were previously unknown or were understated. In this way, more knowledge illuminates that one's understanding is more limited or that the processes are more complex than previously thought (van der Sluijs 1997).

[] Uncertainty as inadequacy of knowledge has a very long history, dating back to philosophical questions debated among the ancient Greeks about the certainty of knowledge, and perhaps even further. Its modern history begins around 1921, when Knight made a distinction between risk and uncertainty (Knight 1921). According to Knight, risk denotes the calculable and thus controllable part of all that is unknowable. The remainder is the uncertain - incalculable and uncontrollable. Luce and Raiffa (1957) adopted these labels to distinguish between decision making under risk and decision making under uncertainty. Similarly, Quade (1989) makes a distinction between stochastic uncertainty and real uncertainty. According to Quade, stochastic uncertainty includes frequency-based probabilities and subjective (Bayesian) probabilities. Real uncertainty covers the future state of the world and the uncertainty resulting from the strategic behavior of other actors. Often, attempts to express the degree of certainty and uncertainty have been linked to whether or not to use probabilities, as exemplified by Morgan and Henrion (1990), who make a distinction between uncertainties that can be treated through probabilities and uncertainties that cannot. Uncertainties that cannot be treated probabilistically include model structure uncertainty and situations in which experts cannot agree upon the probabilities. These are the more important and hardest to handle types of uncertainties (Morgan 2003). As Quade (1989, p. 160) wrote: ” Stochastic uncertainties are therefore among the least of our worries; their effects are swamped by uncertainties about the state of the world and human factors for which we know absolutely nothing about probability distributions and little more about the possible outcomes.” These kinds of uncertainties are now referred to as deep uncertainty (Lempert et al. 2003), or severe uncertainty (Ben-Haim 2006).

[Levels of Uncertainty] Walker et al. (2003) define uncertainty to be ” any departure from the (unachievable) ideal of complete determinism.”

[] For purposes of determining ways of dealing with uncertainty in developing public policies or business strategies, one can distinguish two extreme levels of uncertainty -- complete certainty and total ignorance -- and five intermediate levels (e.g. Courtney 2001; Walker et al. 2003; Makridakis et al. 2009; Kwakkel et al. 2010d). In Fig. 1, the five levels are defined with respect to the knowledge assumed about the various aspects of a policy problem: (a) the future world, (b) the model of the relevant system for that future world, (c) the outcomes from the system, and (d) the weights that the various stakeholders will put on the outcomes. The levels of uncertainty are briefly discussed below. Complete certainty is the situation in which everything is known precisely. It is not attainable, but acts as a limiting characteristic at one end of the spectrum.

[::] Level 1 uncertainty (A clear enough future) represents the situation in which one admits that one is not absolutely certain, but one is not willing or able to measure the degree of uncertainty in any explicit way (Hillier and Lieberman 2001, p. 43). Level 1 uncertainty is often treated through a simple sensitivity analysis of model parameters, where the impacts of small perturbations of model input parameters on the outcomes of a model are assessed.

[::] Level 2 uncertainty (Alternate futures with probabilities) is any uncertainty that can be described adequately in statistical terms. In the case of uncertainty about the future, Level 2 uncertainty is often captured in the form of either a (single) forecast (usually trend based) with a confidence interval or multiple forecasts (scenarios) with associated probabilities.

[::] Level 3 uncertainty (Alternate futures with ranking) represents the situation in which one is able to enumerate multiple alternatives and is able to rank the alternatives in terms of perceived likelihood. That is, in light of the available knowledge and information there are several different parameterizations of the system model, alternative sets of outcomes, and/or different conceivable sets of weights. These possibilities can be ranked according to their perceived likelihood (e.g. virtually certain, very likely, likely, etc.). In the case of uncertainty about the future, Level 3 uncertainty about the future world is often captured in the form of a few trend-based scenarios based on alternative assumptions about the driving forces (e.g., three trend-based scenarios for air transport demand, based on three different assumptions about GDP growth). The scenarios are then ranked according to their perceived likelihood, but no probabilities are assigned, see Patt and Schrag (2003) and Patt and Dessai (2004).

[::] Level 4 uncertainty (Multiplicity of futures) represents the situation in which one is able to enumerate multiple plausible alternatives without being able to rank the alternatives in terms of perceived likelihood. This inability can be due to a lack of knowledge or data about the mechanism or functional relationships being studied; but this inability can also arise due to the fact that the decision makers cannot agree on the rankings. As a result, analysts struggle to specify the appropriate models to describe interactions among the system's variables, to select the probability distributions to represent uncertainty about key parameters in the models, and/or how to value the desirability of alternative outcomes (Lempert et al. 2003).

[::] Level 5 uncertainty (Unknown future) represents the deepest level of recognized uncertainty; in this case, what is known is only that we do not know. This ignorance is recognized. Recognized ignorance is increasingly becoming a common feature of life, because catastrophic, unpredicted, surprising, but painful events seem to be occurring more often. Taleb (2007) calls these events ” Black Swans.” He defines a Black Swan event as one that lies outside the realm of regular expectations (i.e., ” nothing in the past can convincingly point to its possibility”), carries an extreme impact, and is explainable only after the fact (i.e., through retrospective, not prospective, predictability). One of the most dramatic recent Black Swans is the concatenation of events following the 2007 subprime mortgage crisis in the U.S. The mortgage crisis (which some had forecast) led to a credit crunch, which led to bank failures, which led to a deep global recession in 2009, which was outside the realm of most expectations. Another recent Black Swan was the level 9.0 earthquake in Japan in 2011, which led to a tsunami and a nuclear catastrophe, which led to supply chain disruptions (e.g., for automobile parts) around the world.

[::] Total ignorance is the other extreme on the scale of uncertainty. As with complete certainty, total ignorance acts as a limiting case.

[] [...]},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-13687645,~to-add-doi-URL,communicating-uncertainty,decision-making-procedure,deep-uncertainty,featured-publication,min-max,policy-strategies-for-scientific-uncertainty,resilience,robust-modelling,science-policy-interface,scientific-communication,uncertainty}
}
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