Info-gap Decision Theory

Info-gap decision theory is a non-probabilistic decision theory that seeks to optimize robustness to failure – or opportuneness for windfall – 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 since the 1980s 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.

Read more about Info-gap Decision TheorySummary, Basic Example: Budget, Motivation, Example: Resource Allocation, Uncertainty Models, Robustness and Opportuneness, Decision Rules, Applications, Limitations, Criticism, Alternatives, Classical Decision Theory Perspective

Other articles related to "decision, theory":

Info-gap Decision Theory - Classical Decision Theory Perspective - Notes On The Art of Math Modeling - Summary
152) Info-gap's robustness model is a simple instance of this paradigm that is characterized by a specific decision space, state spaces and objective function, as discussed above ... Much can be gained by viewing info-gap's theory in this light ...

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