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Learning in experimental 2 x 2 games

Author

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  • Thorsten Chmura

    (Department of Economics, Ludwig-Maximilians-Universitat Munich)

  • Sebastian Goerg

    (Max Planck Institute for Research on Collective Goods, Bonn)

  • Reinhard Selten

    (cLaboratory for Experimental Economics (BonnEconLab), University of Bonn)

Abstract

In this paper, we introduce two new learning models: impulse-matching learning and action-sampling learning. These two models together with the models of self-tuning EWA and reinforcement learning are applied to 12 different 2 x 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 individuals' 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

  • Thorsten Chmura & Sebastian Goerg & Reinhard Selten, 2011. "Learning in experimental 2 x 2 games," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2011_26, Max Planck Institute for Research on Collective Goods.
  • Handle: RePEc:mpg:wpaper:2011_26
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    References listed on IDEAS

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    Cited by:

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    2. Castañeda, Gonzalo & Chávez-Juárez, Florian & Guerrero, Omar A., 2018. "How do governments determine policy priorities? Studying development strategies through spillover networks," Journal of Economic Behavior & Organization, Elsevier, vol. 154(C), pages 335-361.
    3. Goerg Sebastian J. & Sadrieh Abdolkarim & Neugebauer Tibor, 2016. "Impulse Response Dynamics in Weakest Link Games," German Economic Review, De Gruyter, vol. 17(3), pages 284-297, August.
    4. Ralph-C. Bayer & Hang Wu, 2013. "Do We Learn from Our Own Experience or from Observing Others?," School of Economics Working Papers 2013-21, University of Adelaide, School of Economics.
    5. 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.
    6. Ding, Jieyao & Nicklisch, Andreas, 2013. "On the impulse in impulse learning," Economics Letters, Elsevier, vol. 121(2), pages 294-297.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Erik Mohlin & Robert Ostling & Joseph Tao-yi Wang, 2014. "Learning by Imitation in Games: Theory, Field, and Laboratory," Economics Series Working Papers 734, University of Oxford, Department of Economics.
    12. 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

    learning; 2 x 2 games; Experimental data;
    All these keywords.

    JEL classification:

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

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