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dc.contributor.authorJason Shachatzh_CN
dc.contributor.authorJ. Todd Swarthoutzh_CN
dc.date.accessioned2013-11-08T08:21:36Z
dc.date.available2013-11-08T08:21:36Z
dc.date.issued2013-11-08
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/56962
dc.description.abstractWe report results from an experiment in which humans repeatedly play one of two games against a computer program that follows either a reinforcement or an experience weighted attraction learning algorithm. Our experiment shows these learning algo- rithms detect exploitable opportunities more sensitively than humans. Also, learning algorithms respond to detected payoff-increasing opportunities systematically; how- ever, the responses are too weak to improve the algorithms’ payoffs. Human play against various decision maker types does not vary significantly. These factors lead to a strong linear relationship between the humans’ and algorithms’ action choice propor- tions that is suggestive of the algorithms’ best response correspondences.  zh_CN
dc.language.isozhzh_CN
dc.source.urihttp://www.wise.xmu.edu.cn/paperInfor.asp?id=232zh_CN
dc.subjectLearningzh_CN
dc.subject Repeated gameszh_CN
dc.subject Experimentszh_CN
dc.subject Simulation  zh_CN
dc.titleLearning about Learning in Games through Experimental Control of Strategic Interdependencezh_CN
dc.typeArticlezh_CN
dc.description.noteThis paper is forthcoming in Journal of Economics Dynamics & Control.zh_CN


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