Editing Minimax
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=== Non-probabilistic decision theory === |
=== Non-probabilistic decision theory === |
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A key feature of minimax decision making is being non-probabilistic: in contrast to decisions using [[expected value]] or [[expected utility]], it makes no assumptions about the probabilities of various outcomes, just [[scenario analysis]] of what the possible outcomes are. It is thus [[:wikt:robust|robust]] to changes in the assumptions, |
A key feature of minimax decision making is being non-probabilistic: in contrast to decisions using [[expected value]] or [[expected utility]], it makes no assumptions about the probabilities of various outcomes, just [[scenario analysis]] of what the possible outcomes are. It is thus [[:wikt:robust|robust]] to changes in the assumptions, as these other decision techniques are not. Various extensions of this non-probabilistic approach exist, notably [[minimax regret]] and [[Info-gap decision theory]]. |
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Further, minimax only requires [[ordinal measurement]] (that outcomes be compared and ranked), not ''interval'' measurements (that outcomes include "how much better or worse"), and returns ordinal data, using only the modeled outcomes: the conclusion of a minimax analysis is: "this strategy is minimax, as the worst case is (outcome), which is less bad than any other strategy". Compare to expected value analysis, whose conclusion is of the form: "This strategy yields {{nobr| {{math|ℰ}}({{mvar|X}}) {{=}} {{mvar|n}} ."}} Minimax thus can be used on ordinal data, and can be more transparent. |
Further, minimax only requires [[ordinal measurement]] (that outcomes be compared and ranked), not ''interval'' measurements (that outcomes include "how much better or worse"), and returns ordinal data, using only the modeled outcomes: the conclusion of a minimax analysis is: "this strategy is minimax, as the worst case is (outcome), which is less bad than any other strategy". Compare to expected value analysis, whose conclusion is of the form: "This strategy yields {{nobr| {{math|ℰ}}({{mvar|X}}) {{=}} {{mvar|n}} ."}} Minimax thus can be used on ordinal data, and can be more transparent. |