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 Theory:  Summary, Basic Example: Budget, Motivation, Example: Resource Allocation, Uncertainty Models, Robustness and Opportuneness, Decision Rules, Applications, Limitations, Criticism, Alternatives, Classical Decision Theory Perspective

Famous quotes containing the words decision and/or theory:

    Drug misuse is not a disease, it is a decision, like the decision to step out in front of a moving car. You would call that not a disease but an error of judgment.
    Philip K. Dick (1928–1982)

    Won’t this whole instinct matter bear revision?
    Won’t almost any theory bear revision?
    To err is human, not to, animal.
    Robert Frost (1874–1963)