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Learning about learning in games through experimental control of strategic interdependence

  • Jason Shachat

    ()

    (Wang Yanan Institute for Studies in Economics (WISE), and the MOE Key Laboratory in Econometerics, Xiamen University, China)

  • J. Todd Swarthout

    ()

    (Department of Economics, Georgia State University, Atlanta, GA, 30303, USA)

We 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 algorithms detect exploitable opportunities more sensitively than humans. Also, learning algorithms respond to detected payoff-increasing opportunities systematically; however, the responses are too weak to improve the algorithms’ payoffs. Human play against various decision maker types doesn't vary significantly. These factors lead to a strong linear relationship between the humans’ and algorithms’ action choice proportions that is suggestive of the algorithms’ best response correspondences.

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File URL: http://feel.xmu.edu.cn/RePEc/wpaper/Learning_about_learning_in_games_through_experimental_control_of_strategic_interdependence.pdf
File Function: 2011
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Paper provided by Xiamen Unversity, The Wang Yanan Institute for Studies in Economics, Finance and Economics Experimental Laboratory in its series Working Papers with number 1103.

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Length: 53 pages
Date of creation: 28 Apr 2011
Date of revision: 28 Apr 2011
Handle: RePEc:fee:wpaper:1103
Contact details of provider: Phone: 86-592-2180855
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Web page: http://feel.xmu.edu.cn
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