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Learning about Learning in Games through Experimental Control of Strategic Interdependence

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Author Info

  • Jason Shachat

    (National University of Singapore)

  • J. Todd Swarthout

    (University of Arizona)

Abstract

We conduct experiments in which humans repeatedly play one of two games against a computer decision maker that follows either Roth and Erev's reinforcement learning algorithm or Camerer and Ho's EWA algorithm. The human/algorithm interaction provides results that can't be obtained from the analysis of pure human interactions or model simulations. The learning algorithms are more sensitive than humans in calculating exploitable opponent play. Learning algorithms respond to these calculated opportunities systematically; however, the magnitude of these responses are too weak to improve the algorithm's payoffs. Human play against various decision maker types does not significantly vary. These results demonstrate that humans and currently proposed models of their behavior differ in that humans do not adjust payoff assessments by smooth transition functions and that when humans detect exploitable play they are more likely to choose the best response to this belief.

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File URL: http://128.118.178.162/eps/exp/papers/0310/0310003.pdf
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Bibliographic Info

Paper provided by EconWPA in its series Experimental with number 0310003.

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Length: 39 pages
Date of creation: 13 Oct 2003
Date of revision:
Handle: RePEc:wpa:wuwpex:0310003

Note: Type of Document - pdf; pages: 39
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Web page: http://128.118.178.162

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References

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  1. Robert W. Rosenthal & Jason Shachat & Mark Walker, 2003. "Hide and Seek in Arizona," Experimental 0312001, EconWPA.
  2. Morgan, John & Sefton, Martin, 2002. "An Experimental Investigation of Unprofitable Games," Games and Economic Behavior, Elsevier, vol. 40(1), pages 123-146, July.
  3. McCabe, Kevin & Houser, Daniel & Ryan, Lee & Smith, Vernon & Trouard, Ted, 2001. "A Functional Imaging Study of Cooperation in Two-Person reciprocal Exchange," MPRA Paper 5172, University Library of Munich, Germany.
  4. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-81, September.
  5. Timothy C. Salmon, 2001. "An Evaluation of Econometric Models of Adaptive Learning," Econometrica, Econometric Society, vol. 69(6), pages 1597-1628, November.
  6. Jordan J. S., 1993. "Three Problems in Learning Mixed-Strategy Nash Equilibria," Games and Economic Behavior, Elsevier, vol. 5(3), pages 368-386, July.
  7. Fudenberg, Drew & Levine, David K., 1995. "Consistency and cautious fictitious play," Journal of Economic Dynamics and Control, Elsevier, vol. 19(5-7), pages 1065-1089.
  8. Eyal Winter & Shmuel Zamir, 2005. "An Experiment With Ultimatum Bargaining In A Changing Environment," The Japanese Economic Review, Japanese Economic Association, vol. 56(3), pages 363-385.
  9. Roth, Alvin E & Schoumaker, Francoise, 1983. "Expectations and Reputations in Bargaining: An Experimental Study," American Economic Review, American Economic Association, vol. 73(3), pages 362-72, June.
  10. Shachat, Jason M., 2002. "Mixed Strategy Play and the Minimax Hypothesis," Journal of Economic Theory, Elsevier, vol. 104(1), pages 189-226, May.
  11. Walker, James M. & Smith, Vernon L. & Cox, James C., 1987. "Bidding behavior in first price sealed bid auctions : Use of computerized Nash competitors," Economics Letters, Elsevier, vol. 23(3), pages 239-244.
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Cited by:
  1. Dürsch, Peter & Kolb, Albert & Oechssler, Jörg & Schipper, Burkhard C., 2005. "Rage Against the Machines: How Subjects Learn to Play Against Computers," Discussion Paper Series of SFB/TR 15 Governance and the Efficiency of Economic Systems 63, Free University of Berlin, Humboldt University of Berlin, University of Bonn, University of Mannheim, University of Munich.
  2. Spiliopoulos, Leonidas, 2008. "Humans versus computer algorithms in repeated mixed strategy games," MPRA Paper 6672, University Library of Munich, Germany.
  3. 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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