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Info-gap decision theory is a non-probabilistic decision theory that seeks to optimize robustness to failure – or opportuneness for windfall – under severe uncertainty,〔Yakov Ben-Haim, ''Information-Gap Theory: Decisions Under Severe Uncertainty,'' Academic Press, London, 2001.〕〔Yakov Ben-Haim, ''Info-Gap Theory: Decisions Under Severe Uncertainty,'' 2nd edition, Academic Press, London, 2006.〕 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 ),〔(How Did Info-Gap Theory Start? How Does it Grow? )〕 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. == Summary == Info-gap is a decision theory: it seeks to assist in decision-making under uncertainty. It does this by using 3 models, each of which builds on the last. One begins with a ''model'' for the situation, where some ''parameter'' or parameters are unknown. One then takes an ''estimate'' for the parameter, which is assumed to be ''substantially wrong,'' 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 distant other values of the parameter are from the estimate: as uncertainty increases, the set of possible values increase – if one is ''this'' uncertain in the estimate, what other parameters are possible? ;Robustness/opportuneness model: Given an uncertainty model and a minimum level of desired outcome, then for each decision, how uncertain can you be and be assured achieving this minimum level? (This is called the robustness of the decision.) Conversely, given a desired windfall outcome, how uncertain must you be for this desirable outcome to be possible? (This is called the opportuneness of the decision.) ;Decision-making model: To decide, one optimizes either the robustness or the opportuneness, on the basis of the robustness or opportuneness model. Given a desired minimum outcome, which decision is most robust (can stand the most uncertainty) and still give the desired outcome (the robust-satisficing action)? Alternatively, given a desired windfall outcome, which decision requires the ''least'' uncertainty for the outcome to be achievable (the opportune-windfalling action)? 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Info-gap decision theory」の詳細全文を読む スポンサード リンク
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