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Learning about learning in games through experimental control of strategic interdependence

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  • Shachat, Jason
  • Swarthout, J. Todd

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 does not 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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Bibliographic Info

Article provided by Elsevier in its journal Journal of Economic Dynamics and Control.

Volume (Year): 36 (2012)
Issue (Month): 3 ()
Pages: 383-402

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Handle: RePEc:eee:dyncon:v:36:y:2012:i:3:p:383-402

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Web page: http://www.elsevier.com/locate/jedc

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Keywords: Learning; Repeated games; Experiments; Simulation;

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Citations

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Cited by:
  1. Spiliopoulos, Leonidas, 2008. "Humans versus computer algorithms in repeated mixed strategy games," MPRA Paper 6672, University Library of Munich, Germany.
  2. Dürsch, Peter & Kolb, Albert & Oechssler, Jörg & Schipper, Burkhard, 2005. "Rage Against the Machines - How Subjects Learn to Play Against Computers," Sonderforschungsbereich 504 Publications 05-36, Sonderforschungsbereich 504, Universität Mannheim & Sonderforschungsbereich 504, University of Mannheim.
  3. repec:fee:wpaper:1101 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, vol. 43(3), pages 407-430, June.

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