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Evidence based rules and learning in symmetric normal-form games

Author

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  • Dale O. Stahl

    (Department of Economics, University of Texas, Austin, TX 78712, USA)

Abstract

We put forth a general theory of boundedly rational behavior and learning for symmetric normal-form games with unique symmetric Nash equilibria. A class of evidence-based behavioral rules is specified, which includes best-responding to a prior and Nash play. A player begins with initial propsenities towards the rules, and given experience over time adjusts his/her propensities in proportion to the past performance of the rules. We focus on scenarios in which the past distribution of play is revealed to all players. Confronting this theory with experimental data, we find significant support for rule learning and heterogeneity among participants.

Suggested Citation

  • Dale O. Stahl, 1999. "Evidence based rules and learning in symmetric normal-form games," International Journal of Game Theory, Springer;Game Theory Society, vol. 28(1), pages 111-130.
    • Handle: RePEc:spr:jogath:v:28:y:1999:i:1:p:111-130
    Note: Received June 1996/Final version April 1997
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    Citations

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    Cited by:

    1. Andreas Ortmann & Leonidas Spiliopoulos, 2017. "The beauty of simplicity? (Simple) heuristics and the opportunities yet to be realized," Chapters, in: Morris Altman (ed.), Handbook of Behavioural Economics and Smart Decision-Making, chapter 7, pages 119-136, Edward Elgar Publishing.
    2. Nax, Heinrich Harald & Newton, Jonathan, 2022. "Deep and shallow thinking in the long run," Theoretical Economics, Econometric Society, vol. 17(4), November.
    3. Costa-Gomes, Miguel & Crawford, Vincent P & Broseta, Bruno, 2001. "Cognition and Behavior in Normal-Form Games: An Experimental Study," Econometrica, Econometric Society, vol. 69(5), pages 1193-1235, September.
    4. Asim Ansari & Ricardo Montoya & Oded Netzer, 2012. "Dynamic learning in behavioral games: A hidden Markov mixture of experts approach," Quantitative Marketing and Economics (QME), Springer, vol. 10(4), pages 475-503, December.
    5. Lensberg, Terje & Schenk-Hoppé, Klaus Reiner, 2021. "Cold play: Learning across bimatrix games," Journal of Economic Behavior & Organization, Elsevier, vol. 185(C), pages 419-441.
    6. Yoella Bereby-Meyer & Alvin E. Roth, 2006. "The Speed of Learning in Noisy Games: Partial Reinforcement and the Sustainability of Cooperation," American Economic Review, American Economic Association, vol. 96(4), pages 1029-1042, September.
    7. Gunnthorsdottir, Anna & Rapoport, Amnon, 2006. "Embedding social dilemmas in intergroup competition reduces free-riding," Organizational Behavior and Human Decision Processes, Elsevier, vol. 101(2), pages 184-199, November.
    8. Teck-Hua Ho & So-Eun Park & Xuanming Su, 2021. "A Bayesian Level- k Model in n -Person Games," Management Science, INFORMS, vol. 67(3), pages 1622-1638, March.
    9. Arifovic, Jasmina & McKelvey, Richard D. & Pevnitskaya, Svetlana, 2006. "An initial implementation of the Turing tournament to learning in repeated two-person games," Games and Economic Behavior, Elsevier, vol. 57(1), pages 93-122, October.
    10. Ioannou, Christos A. & Romero, Julian, 2014. "A generalized approach to belief learning in repeated games," Games and Economic Behavior, Elsevier, vol. 87(C), pages 178-203.
    11. Mohlin, Erik, 2012. "Evolution of theories of mind," Games and Economic Behavior, Elsevier, vol. 75(1), pages 299-318.
    12. Stahl, Dale O., 2000. "Rule Learning in Symmetric Normal-Form Games: Theory and Evidence," Games and Economic Behavior, Elsevier, vol. 32(1), pages 105-138, July.
    13. Hanaki, Nobuyuki & Sethi, Rajiv & Erev, Ido & Peterhansl, Alexander, 2005. "Learning strategies," Journal of Economic Behavior & Organization, Elsevier, vol. 56(4), pages 523-542, April.
    14. Haruvy, Ernan & Stahl, Dale O., 2012. "Between-game rule learning in dissimilar symmetric normal-form games," Games and Economic Behavior, Elsevier, vol. 74(1), pages 208-221.
    15. Battalio,R. & Samuelson,L. & Huyck,J. van, 1998. "Risk dominance, payoff dominance and probabilistic choice learning," Working papers 2, Wisconsin Madison - Social Systems.
    16. Fabrizio Germano, 2007. "Stochastic Evolution of Rules for Playing Finite Normal Form Games," Theory and Decision, Springer, vol. 62(4), pages 311-333, May.
    17. Majumdar, Dipjyoti, 2004. "An axiomatic characterization of Bayes' Rule," Mathematical Social Sciences, Elsevier, vol. 47(3), pages 261-273, May.
    18. Anna Gunnthorsdottir & Amnon Rapoport, 2003. "The effect of sharing rules on group competition," Experimental 0307003, University Library of Munich, Germany.
    19. Weizsacker, Georg, 2003. "Ignoring the rationality of others: evidence from experimental normal-form games," Games and Economic Behavior, Elsevier, vol. 44(1), pages 145-171, July.
    20. Haruvy, Ernan & Stahl, Dale O. & Wilson, Paul W., 1999. "Evidence for optimistic and pessimistic behavior in normal-form games," Economics Letters, Elsevier, vol. 63(3), pages 255-259, June.
    21. García-Pola, Bernardo, 2020. "Do people minimize regret in strategic situations? A level-k comparison," Games and Economic Behavior, Elsevier, vol. 124(C), pages 82-104.
    22. Rapoport, Amnon & Stein, William E. & Parco, James E. & Nicholas, Thomas E., 2003. "Equilibrium play and adaptive learning in a three-person centipede game," Games and Economic Behavior, Elsevier, vol. 43(2), pages 239-265, May.
    23. Lai, Ernest K. & Lim, Wooyoung, 2012. "Authority and communication in the laboratory," Games and Economic Behavior, Elsevier, vol. 74(2), pages 541-560.
    24. Sanz Nogales, Jose M. & Zazo, S., 2020. "Replicator based on imitation for finite and arbitrary networked communities," Applied Mathematics and Computation, Elsevier, vol. 378(C).
    25. Timothy Salmon, 2004. "Evidence for Learning to Learn Behavior in Normal Form Games," Theory and Decision, Springer, vol. 56(4), pages 367-404, April.

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    Keywords

    Learning · rules · experimental;

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