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Wald's maximin model : ウィキペディア英語版
Wald's maximin model
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:
:v^:= \max_\min_f(d,s)
where D denotes the decision space; S(d) denotes the set of states associated with decision d and f(d,s) denotes the payoff (outcome) associated with decision d and state s.
This model represents a 2-person game in which the \max player plays first. In response, the second player selects the worst state in S(d), namely a state in S(d) that minimizes the payoff f(d,s) over s in S(d). 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:
:v^:= \max_
where \mathbb denotes the real line.
As in game theory, the worst payoff associated with decision d, namely
: v(d):= \min_ f(d,s)\ , \ d \in D
is called ''the security level'' of decision d.
The minimax version of the model is obtained by exchanging the positions of the \max and \min operations in the classic format:
:v^:= \min_\max_f(d,s).
The equivalent MP format is as follows:
:v^:= \min_

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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