Expected value of including uncertainty

In decision theory and quantitative policy analysis, the expected value of including uncertainty (EVIU) is the expected difference in the value of a decision based on a probabilistic analysis versus a decision based on an analysis that ignores uncertainty.

Decisions must be made every day in the ubiquitous presence of uncertainty. For most day-to-day decisions, various heuristics are used to act reasonably in the presence of uncertainty, often with little thought about its presence. However, for larger high-stakes decisions or decisions in highly public situations, decision makers may often benefit from a more systematic treatment of their decision problem, such as through quantitative analysis or decision analysis.

When building a quantitative decision model, a model builder identifies various relevant factors, and encodes these as input variables. From these inputs, other quantities, called result variables, can be computed; these provide information for the decision maker. For example, in the example detailed below, the decision maker must decide how soon before a flight's schedule departure he must leave for the airport (the decision). One input variable is how long it takes to drive to the airport parking garage. From this and other inputs, the model can compute how likely it is the decision maker will miss the flight and what the net cost (in minutes) will be for various decisions.

To reach a decision, a very common practice is to ignore uncertainty. Decisions are reached through quantitative analysis and model building by simply using a best guess (single value) for each input variable. Decisions are then made on computed point estimates. In many cases, however, ignoring uncertainty can lead to very poor decisions, with estimations for result variables often misleading the decision maker

An alternative to ignoring uncertainty in quantiative decision models is to explicitly encode uncertainty as part of the model. With this approach, a probability distribution is provided for each input variable, rather than a single best guess. The variance in that distribution reflects the degree of subjective uncertainty (or lack of knowledge) in the input quantity. The software tools then use methods such as Monte Carlo analysis to propagate the uncertainty to result variables, so that a decision maker obtains an explicit picture of the impact that uncertainty has on his decisions, and in many cases can make a much better decision as a result.

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