We construct an uncoupled randomized strategy of repeated play such that, if every player follows such a strategy, then the joint mixed strategy profiles converge, almost surely, to a Nash equilibrium of the one-shot game. The procedure requires very little in terms of players' information about the game. In fact, players' actions are based only on their own past payoffs and, in a variant of the strategy, players need not even know that their payoffs are determined through other players' actions. The procedure works for general finite games and is based on appropriate modifications of a simple stochastic learning rule introduced by Foster and Young.
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Paper provided by Department of Economics and Business, Universitat Pompeu Fabra in its series Economics Working Papers with number
788.
Find related papers by JEL classification: C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
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Eddie Dekel & Drew Fudenberg & David K. Levine, 2000.
"Learning to Play Bayesian Games,"
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[Downloadable!]
Sergiu Hart, 2004.
"Adaptive Heuristics,"
Discussion Paper Series
dp372, Center for Rationality and Interactive Decision Theory, Hebrew University, Jerusalem.
[Downloadable!]
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