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Learning in experimental 2×2 games

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  • Chmura, Thorsten
  • Goerg, Sebastian J.
  • Selten, Reinhard

Abstract

In this paper, we introduce two new learning models: action-sampling learning and impulse-matching learning. These two models, together with the models of self-tuning EWA and reinforcement learning, are applied to 12 different 2×2 games and their results are compared with the results from experimental data. We test whether the models are capable of replicating the aggregate distribution of behavior, as well as correctly predicting individualsʼ round-by-round behavior. Our results are two-fold: while the simulations with impulse-matching and action-sampling learning successfully replicate the experimental data on the aggregate level, individual behavior is best described by self-tuning EWA. Nevertheless, impulse-matching learning has the second-highest score for the individual data. In addition, only self-tuning EWA and impulse-matching learning lead to better round-by-round predictions than the aggregate frequencies, which means they adjust their predictions correctly over time.

Suggested Citation

  • Chmura, Thorsten & Goerg, Sebastian J. & Selten, Reinhard, 2012. "Learning in experimental 2×2 games," Games and Economic Behavior, Elsevier, vol. 76(1), pages 44-73.
  • Handle: RePEc:eee:gamebe:v:76:y:2012:i:1:p:44-73
    DOI: 10.1016/j.geb.2012.06.007
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    2. Ralph-C. Bayer & Hang Wu, 2013. "Do We Learn from Our Own Experience or from Observing Others?," School of Economics and Public Policy Working Papers 2013-21, University of Adelaide, School of Economics and Public Policy.
    3. Ding, Jieyao & Nicklisch, Andreas, 2013. "On the impulse in impulse learning," Economics Letters, Elsevier, vol. 121(2), pages 294-297.
    4. Nicklisch, Andreas & Köke, Sonja & Lange, Andreas, 2016. "Is Adversity a School of Wisdom? Experimental Evidence on Cooperative Protection Against Stochastic Losses," VfS Annual Conference 2016 (Augsburg): Demographic Change 145716, Verein für Socialpolitik / German Economic Association.
    5. Mohlin, Erik & Östling, Robert & Wang, Joseph Tao-yi, 2020. "Learning by similarity-weighted imitation in winner-takes-all games," Games and Economic Behavior, Elsevier, vol. 120(C), pages 225-245.
    6. Erhao Xie, 2019. "Monetary Payoff and Utility Function in Adaptive Learning Models," Staff Working Papers 19-50, Bank of Canada.
    7. Edward Cartwright & Anna Stepanova & Lian Xue, 2019. "Impulse balance and framing effects in threshold public good games," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 21(5), pages 903-922, October.
    8. Sebastian J. Goerg & Tibor Neugebauer & Abdolkarim Sadrieh, 2016. "Impulse Response Dynamics in Weakest Link Games," German Economic Review, Verein für Socialpolitik, vol. 17(3), pages 284-297, August.
    9. Xie, Erhao, 2021. "Empirical properties and identification of adaptive learning models in behavioral game theory," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 798-821.
    10. Sanjit Dhami & Ali al-Nowaihi & Cass R. Sunstein, 2019. "Heuristics and Public Policy: Decision-making Under Bounded Rationality," Studies in Microeconomics, , vol. 7(1), pages 7-58, June.
    11. Marco LiCalzi & Roland Mühlenbernd, 2022. "Feature-weighted categorized play across symmetric games," Experimental Economics, Springer;Economic Science Association, vol. 25(3), pages 1052-1078, June.
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    14. Linde, Jona & Sonnemans, Joep & Tuinstra, Jan, 2014. "Strategies and evolution in the minority game: A multi-round strategy experiment," Games and Economic Behavior, Elsevier, vol. 86(C), pages 77-95.
    15. Christos A. Ioannou & Julian Romero, 2012. "Strategic Learning With Finite Automata Via The EWA-Lite Model," Purdue University Economics Working Papers 1269, Purdue University, Department of Economics.
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    17. Edward Cartwright & Anna Stepanova, 2017. "Efficiency in a forced contribution threshold public good game," International Journal of Game Theory, Springer;Game Theory Society, vol. 46(4), pages 1163-1191, November.

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    More about this item

    Keywords

    2×2 games; Experimental data; Learning; Impulse-matching; Self-tuning EWA; Reinforcement; Action-sampling;
    All these keywords.

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior

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