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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.

Suggested Citation

  • Jason Shachat & J. Todd Swarthout, 2011. "Learning about learning in games through experimental control of strategic interdependence," Working Papers 1103, Xiamen Unversity, The Wang Yanan Institute for Studies in Economics, Finance and Economics Experimental Laboratory, revised 28 Apr 2011.
    • Handle: RePEc:fee:wpaper:1103
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    Cited by:

    1. Dürsch, Peter & Kolb, Albert & Oechssler, Jörg & Schipper, Burkhard, 2005. "Rage against the machines : how subjects learn to play against computers," Papers 05-36, Sonderforschungsbreich 504.
    2. Duffy, Sean & Naddeo, JJ & Owens, David & Smith, John, 2016. "Cognitive load and mixed strategies: On brains and minimax," MPRA Paper 89720, University Library of Munich, Germany.
    3. Dürsch, Peter & Kolb, Albert & Oechssler, Jörg & Schipper, Burkhard, 2005. "Rage against the machines : how subjects learn to play against computers," Papers 05-36, Sonderforschungsbreich 504.
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    5. Feng, Jun & Qin, Xiangdong & Wang, Xiaoyuan, 2021. "A Bayesian cognitive hierarchy model with fixed reasoning levels," Journal of Economic Behavior & Organization, Elsevier, vol. 192(C), pages 704-723.
    6. March, Christoph, 2021. "Strategic interactions between humans and artificial intelligence: Lessons from experiments with computer players," Journal of Economic Psychology, Elsevier, vol. 87(C).
    7. Frederic Moisan & Cleotilde Gonzalez, 2017. "Security under Uncertainty : Adaptive Attackers Are More Challenging to Human Defenders than Random Attackers," Post-Print hal-03188217, HAL.
    8. 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.
    9. Spiliopoulos, Leonidas, 2008. "Humans versus computer algorithms in repeated mixed strategy games," MPRA Paper 6672, University Library of Munich, Germany.
    10. Peter Duersch & Albert Kolb & Jörg Oechssler & Burkhard Schipper, 2010. "Rage against the machines: how subjects play against learning algorithms," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 43(3), pages 407-430, June.

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    More about this item

    Keywords

    Learning; Repeated games; Experiments; Simulation;
    All these keywords.

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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