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dc.contributor.authorAuletta, Vincenzo
dc.contributor.authorFerraioli, Diodato
dc.contributor.authorPasquale, Francesco
dc.contributor.authorPersiano, Giuseppe
dc.date.accessioned2013-06-24T07:50:02Z
dc.date.available2013-06-24T07:50:02Z
dc.date.issued2013
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/11478
dc.language.isoenen
dc.subjectLogit dynamicsen
dc.subjectMixing timeen
dc.subjectSocial welfareen
dc.subjectEquilibriaen
dc.subject.ddc004en
dc.titleMixing Time and Stationary Expected Social Welfare of Logit Dynamicsen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenWe study logit dynamics (Blume in Games Econ. Behav. 5:387–424, 1993) for strategic games. This dynamics works as follows: at every stage of the game a player is selected uniformly at random and she plays according to a noisy best-response where the noise level is tuned by a parameter β. Such a dynamics defines a family of ergodic Markov chains, indexed by β, over the set of strategy profiles. We believe that the stationary distribution of these Markov chains gives a meaningful description of the long-term behavior for systems whose agents are not completely rational. Our aim is twofold: On the one hand, we are interested in evaluating the performance of the game at equilibrium, i.e. the expected social welfare when the strategy profiles are random according to the stationary distribution. On the other hand, we want to estimate how long it takes, for a system starting at an arbitrary profile and running the logit dynamics, to get close to its stationary distribution; i.e., the mixing time of the chain. In this paper we study the stationary expected social welfare for the 3-player CK game (Christodoulou and Koutsoupias in Proc. of the 37th Annual ACM Symposium on Theory of Computing (STOC’05), pp. 67–73, ACM, New York, 2005), for 2-player coordination games, and for two simple n-player games. For all these games, we also give almost tight upper and lower bounds on the mixing time of logit dynamics. Our results show two different behaviors: in some games the mixing time depends exponentially on β, while for other games it can be upper bounded by a function independent of β.en
dc.relation.isversionofjnlnameTheory of Computing Systems
dc.relation.isversionofjnlvol53en
dc.relation.isversionofjnlissue1en
dc.relation.isversionofjnldate2013
dc.relation.isversionofjnlpages3-40en
dc.relation.isversionofdoihttp://dx.doi.org/10.1007/s00224-013-9458-zen
dc.identifier.urlsitehttp://arxiv.org/abs/1002.3474v4en
dc.relation.isversionofjnlpublisherSpringeren
dc.subject.ddclabelInformatique généraleen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen


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