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dc.contributor.authorBaşçı, E.en_US
dc.date.accessioned2015-07-28T11:56:20Z
dc.date.available2015-07-28T11:56:20Z
dc.date.issued1999en_US
dc.identifier.issn0165-1889
dc.identifier.urihttp://hdl.handle.net/11693/10926
dc.description.abstractThis paper introduces a learning algorithm that allows for imitation in recursive dynamic games. The Kiyotaki-Wright model of money is a well-known example of such decision environments. In this context, learning by experience has been studied before. Here, we introduce imitation as an additional channel for learning. In numerical simulations, we observe that the presence of imitation either speeds up social convergence to the theoretical Markov-Nash equilibrium or leads every agent of the same type to the same mode of suboptimal behavior. We observe an increase in the probability of convergence to equilibrium, as the incentives for optimal play become more pronounced.en_US
dc.language.isoEnglishen_US
dc.source.titleJournal of Economic Dynamics and Controlen_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/S0165-1889(98)00084-0en_US
dc.subjectDynamic optimizationen_US
dc.subjectImitationen_US
dc.subjectKiyotaki-Wright model of moneyen_US
dc.subjectLearningen_US
dc.titleLearning by imitationen_US
dc.typeArticleen_US
dc.departmentDepartment of Economicsen_US
dc.citation.spage1569en_US
dc.citation.epage1585en_US
dc.citation.volumeNumber23en_US
dc.citation.issueNumber9-10en_US
dc.identifier.doi10.1016/S0165-1889(98)00084-0en_US
dc.publisherElsevier BVen_US


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