Global Nash convergence of Foster and Young's regret testing
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- dc.contributor.author Germano, Fabrizio
- dc.contributor.author Lugosi, Gábor
- dc.contributor.other Universitat Pompeu Fabra. Departament d'Economia i Empresa
- dc.date.accessioned 2020-05-25T09:27:06Z
- dc.date.available 2020-05-25T09:27:06Z
- dc.date.issued 2004-10-01
- dc.date.modified 2020-05-25T09:20:25Z
- 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.format.mimetype application/pdf*
- dc.identifier https://econ-papers.upf.edu/ca/paper.php?id=788
- dc.identifier.citation Games and Economic Behavior (2007) 60: 135-154
- dc.identifier.uri http://hdl.handle.net/10230/1237
- dc.language.iso eng
- dc.relation.ispartofseries Economics and Business Working Papers Series; 788
- dc.rights L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
- dc.subject.keyword regret testing
- dc.subject.keyword regret based learning
- dc.subject.keyword random search
- dc.subject.keyword stochastic dynamics
- dc.subject.keyword uncoupled dynamics
- dc.subject.keyword global convergence to nash equilibria
- dc.subject.keyword Microeconomics
- dc.title Global Nash convergence of Foster and Young's regret testing
- dc.type info:eu-repo/semantics/workingPaper