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In decision theory and game theory, Wald's maximin model is a non-probabilistic decision-making model according to which decisions are ranked on the basis of their worst-case outcomes. That is, the best (optimal) decision is one whose worst outcome is at least as good as the worst outcome of any other decisions. It is one of the most important models in robust decision making in general and robust optimization in particular. It is also known by a variety of other titles, such as Wald's maximin rule, Wald's maximin principle, Wald's maximin paradigm, and Wald's maximin criterion. Often 'minimax' is used instead of 'maximin'. ==Definition== Wald's generic maximin model is as follows: : where denotes the decision space; denotes the set of states associated with decision and denotes the payoff (outcome) associated with decision and state . This model represents a 2-person game in which the player plays first. In response, the second player selects the worst state in , namely a state in that minimizes the payoff over in . In many applications the second player represents uncertainty. However, there are maximin models that are completely deterministic. The above model is the ''classic'' format of Wald's maximin model. There is an equivalent mathematical programming (MP) format: : where denotes the real line. As in game theory, the worst payoff associated with decision , namely : is called ''the security level'' of decision . The minimax version of the model is obtained by exchanging the positions of the and operations in the classic format: : The equivalent MP format is as follows: : 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「Wald's maximin model」の詳細全文を読む スポンサード リンク
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