AbstractWe select among rules for learning which of two actions in a stationary decision problem achieves a higher expected payo¤when payoffs realized by both actions are known in previous instances. Only a bounded set containing all possible payoffs is known. Rules are evaluated using maximum risk with maximin utility, minimax regret, competitive ratio and selection procedures being special cases. A randomized variant of fictitious play attains minimax risk for all risk functions with ex-ante expected payoffs increasing in the number of observations. Fictitious play itself has neither of these two properties. Tight bounds on maximal regret and probability of selecting the best action are included.
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Bibliographic InfoPaper provided by European University Institute in its series Economics Working Papers with number ECO2007/01.
Date of creation: 2007
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fictitious play; nonparametric; finite sample; matched pairs; foregone payoffs; minimax risk; ex-ante improving; selection procedure;
Find related papers by JEL classification:
- D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
- D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
- C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-04-28 (All new papers)
- NEP-CBA-2007-04-28 (Central Banking)
- NEP-CBE-2007-04-28 (Cognitive & Behavioural Economics)
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