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Learning about Learning in Games through Experimental Control of Strategic Interdependence

Author

Listed:
  • Jason Shachat
  • J. Todd Swarthout

Abstract

We conduct experiments in which humans repeatedly play one of two games against a computer decision maker that follows either a reinforcement learning or an Experience Weighted Attraction algorithm. Our experiments show these learning algorithms more sensitively detect exploitable opportunities 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 significantly vary. These factors lead to a strong linear relationship between the humans and algorithms action choice proportions that is suggestive of the algorithm's best response correspondence.

Suggested Citation

  • Jason Shachat & J. Todd Swarthout, 2002. "Learning about Learning in Games through Experimental Control of Strategic Interdependence," Experimental Economics Center Working Paper Series 2006-17, Experimental Economics Center, Andrew Young School of Policy Studies, Georgia State University, revised Aug 2008.
    • Handle: RePEc:exc:wpaper:2006-17
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    File URL: http://excen.gsu.edu/workingpapers/GSU_EXCEN_WP_2006-17.pdf
    File Function: First version, 2006
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    File URL: http://excen.gsu.edu/workingpapers/GSU_EXCEN_WP_2008-06.pdf
    File Function: Revised version, 2008
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    Citations

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    Cited by:

    1. Sylvain Mignot & Annick Vignes, 2020. "The Many Faces of Agent-Based Computational Economics: Ecology of Agents, Bottom-Up Approaches and Paradigm Shift [Les modèles multi-agents en économie, entre agents hétérogènes, approches bottom-u," Post-Print hal-02956172, HAL.
    2. 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.
    3. Gaunersdorfer, Andrea & Hofbauer, Josef, 2025. "Learning in unprofitable games," Games and Economic Behavior, Elsevier, vol. 151(C), pages 108-126.
    4. Sean Duffy & J. J. Naddeo & David Owens & John Smith, 2024. "Cognitive Load and Mixed Strategies: On Brains and Minimax," International Game Theory Review (IGTR), World Scientific Publishing Co. Pte. Ltd., vol. 26(03), pages 1-34, September.
    5. 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.
    6. repec:wyi:journl:002151 is not listed on IDEAS
    7. 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.
    8. March, Christoph, 2021. "Strategic interactions between humans and artificial intelligence: Lessons from experiments with computer players," Journal of Economic Psychology, Elsevier, vol. 87(C).
    9. Frederic Moisan & Cleotilde Gonzalez, 2017. "Security under Uncertainty : Adaptive Attackers Are More Challenging to Human Defenders than Random Attackers," Post-Print hal-03188217, HAL.
    10. 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.
    11. Spiliopoulos, Leonidas, 2008. "Humans versus computer algorithms in repeated mixed strategy games," MPRA Paper 6672, University Library of Munich, Germany.
    12. 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.

    More about this item

    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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