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Global Nash convergence of Foster and Young's regret testing

Fabrizio Germano () and Gabor Lugosi

Economics Working Papers from Department of Economics and Business, Universitat Pompeu Fabra

Abstract: 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.

Keywords: Regret testing; regret based learning; random search; stochastic dynamics; uncoupled dynamics; global convergence to Nash equilibria (search for similar items in EconPapers)
JEL-codes: C72 C73 D81 D83 (search for similar items in EconPapers)
Date: 2004-10
New Economics Papers: this item is included in nep-gth
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Journal Article: Global Nash convergence of Foster and Young's regret testing (2007) Downloads
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