Motivation
It is common to make decisions under uncertainty. What can be done to make good (or at least the best possible) decisions under conditions of uncertainty? Info-gap robustness analysis evaluates each feasible decision by asking: how much deviation from an estimate of a parameter value, function, or set, is permitted and yet "guarantee" acceptable performance? In everyday terms, the "robustness" of a decision is set by the size of deviation from an estimate that still leads to performance within requirements when using that decision. It is sometimes difficult to judge how much robustness is needed or sufficient. However, according to info-gap theory, the ranking of feasible decisions in terms of their degree of robustness is independent of such judgments.
Info-gap theory also proposes an opportuneness function which evaluates the potential for windfall outcomes resulting from favorable uncertainty.
Read more about this topic: Info-gap Decision Theory
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