Info-gap decision theory

Info-gap decision theory seeks to optimize robustness to failure under severe uncertainty, in particular applying sensitivity analysis of the stability radius type to perturbations in the value of a given estimate of the parameter of interest. It has some connections with Wald's maximin model; some authors distinguish them, others consider them instances of the same principle. It has been developed by Yakov Ben-Haim, and has found many applications and described as a theory for decision-making under "severe uncertainty". It has been criticized as unsuited for this purpose, and alternatives proposed, including such classical approaches as robust optimization. Info-gap is a theory: it assists in decisions under uncertainty. It does this by using models, each built on the last. One begins with a model for the situation, where some parameter or parameters are unknown. Then takes an estimate for the parameter, and one analyzes how sensitive the outcomes under the model are to the error in this estimate. Uncertainty model Starting from the estimate, an uncertainty model measures how far away other values of the parameter are: as uncertainty increases, the set of values increase. Robustness/opportuneness model Given an uncertainty model, then for each decision, how uncertain can you be and be confident succeeding? (robustness) Also, given a windfall, how uncertain must you be for this result to be plausible? (opportuneness) Decision-making model One optimizes the robustness on the basis of the model. Given an outcome, which decision can stand the most uncertainty and give the outcome? Also, given a windfall, which decision requires the least uncertainty for the outcome? Info-gap theory models uncertainty as subsets around a point estimate : the estimate is accurate, and uncertainty increases, in general without bound.