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dc.contributor.authorAlemdar, N. M.en_US
dc.contributor.authorSirakaya, S.en_US
dc.date.accessioned2018-04-12T13:45:36Z
dc.date.available2018-04-12T13:45:36Z
dc.date.issued2003en_US
dc.identifier.issn0165-1889
dc.identifier.urihttp://hdl.handle.net/11693/38142
dc.description.abstractThis paper develops a method to compute the Stackelberg equilibria in sequential games. We construct a normal form game which is interactively played by an artificially intelligent leader, GAL, and a follower, GAF. The leader is a genetic algorithm breeding a population of potential actions to better anticipate the follower's reaction. The follower is also a genetic algorithm training on-line a suitable neural network to evolve a population of rules to respond to any move in the leader's action space. When GAs repeatedly play this game updating each other synchronously, populations converge to the Stackelberg equilibrium of the sequential game. We provide numerical examples attesting to the efficiency of the algorithm. © 2002 Elsevier Science B.V. All rights reserved.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(02)00069-6en_US
dc.subjectFeed-forward neural networksen_US
dc.subjectParallel genetic algorithmsen_US
dc.subjectStackelberg equilibriumen_US
dc.titleOn-line computation of Stackelberg equilibria with synchronous parallel genetic algorithmsen_US
dc.typeArticleen_US
dc.departmentDepartment of Economics
dc.citation.spage1503en_US
dc.citation.epage1515en_US
dc.citation.volumeNumber27en_US
dc.citation.issueNumber8en_US
dc.identifier.doi10.1016/S0165-1889(02)00069-6en_US
dc.publisherElsevier BVen_US


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