dc.contributor.author Germano, Fabrizio
dc.contributor.author Lugosi, Gábor
dc.contributor.other Universitat Pompeu Fabra. Departament d'Economia i Empresa
dc.date.accessioned 2012-07-11T02:08:11Z
dc.date.available 2012-07-11T02:08:11Z
dc.date.issued 2005-09-15T23:44:22Z
dc.identifier.uri http://hdl.handle.net/10230/1237
dc.description.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.
dc.language.iso eng
dc.rights.uri Aquest document està subjecte a una llicència d'ús de Creative Commons, amb la qual es permet copiar, distribuir i comunicar públicament l'obra sempre que se'n citin l'autor original, la universitat i el departament i no se'n faci cap ús comercial ni obra derivada, tal com queda estipulat en la llicència d'ús (http://creativecommons.org/licenses/by-nc-nd/2.5/es/)
dc.subject.other Regret testing, regret based learning, random search, stochastic dynamics, uncoupled dynamics, global convergence to Nash equilibria
dc.title Global Nash Convergence of Foster and Young's Regret Testing
dc.type info:eu-repo/semantics/workingPaper
dc.date.modified 2012-07-10T07:27:17Z

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