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Learning about learning in games through experimental control of strategic interdependence

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  • 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)

Abstract

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
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Bibliographic Info

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

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Keywords: Learning; Repeated games; Experiments; Simulation;

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References

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Cited by:
  1. Peter Dürsch & Albert Kolb & Jörg Oechssler & Burkhard C. Schipper, 2005. "Rage Against the Machines: How Subjects Learn to Play Against Computers," Working Papers, University of Heidelberg, Department of Economics 0423, University of Heidelberg, Department of Economics, revised Oct 2005.
  2. Spiliopoulos, Leonidas, 2008. "Humans versus computer algorithms in repeated mixed strategy games," MPRA Paper 6672, University Library of Munich, Germany.
  3. repec:wyi:wpaper:002021 is not listed on IDEAS
  4. Peter Duersch & Albert Kolb & Jörg Oechssler & Burkhard Schipper, 2010. "Rage against the machines: how subjects play against learning algorithms," Economic Theory, Springer, Springer, vol. 43(3), pages 407-430, June.
  5. Jason Shachat & J. Todd Swarthout & Lijia Wei, 2011. "Man versus Nash An experiment on the self-enforcing nature of mixed strategy equilibrium," Working Papers 1101, Xiamen Unversity, The Wang Yanan Institute for Studies in Economics, Finance and Economics Experimental Laboratory, revised 21 Feb 2011.

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