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A Behavioral Learning Process in Games

Author

Listed:
  • Laslier, J.-F.
  • Topol, R.
  • Walliser, B.

Abstract

The paper studies a behavioral learning process where an agent plays, at each period, an action with a probability which is proportional to the cumulative utility he got in the past with that action. The so-called CPR learning rule and the dynamic process it induces are formally stated and compared to other reinforcement rules as well as to fictitious play or the replicator dynamics.

Suggested Citation

  • Laslier, J.-F. & Topol, R. & Walliser, B., 1999. "A Behavioral Learning Process in Games," Papers 99-03, Paris X - Nanterre, U.F.R. de Sc. Ec. Gest. Maths Infor..
  • Handle: RePEc:fth:pnegmi:99-03
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    References listed on IDEAS

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    1. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-881, September.
    2. Borgers, Tilman & Sarin, Rajiv, 2000. "Naive Reinforcement Learning with Endogenous Aspirations," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 41(4), pages 921-950, November.
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    5. Borgers, Tilman & Sarin, Rajiv, 1997. "Learning Through Reinforcement and Replicator Dynamics," Journal of Economic Theory, Elsevier, vol. 77(1), pages 1-14, November.
    6. Roth, Alvin E. & Erev, Ido, 1995. "Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term," Games and Economic Behavior, Elsevier, vol. 8(1), pages 164-212.
    7. Kaniovski Yuri M. & Young H. Peyton, 1995. "Learning Dynamics in Games with Stochastic Perturbations," Games and Economic Behavior, Elsevier, vol. 11(2), pages 330-363, November.
    8. John G. Cross, 1973. "A Stochastic Learning Model of Economic Behavior," The Quarterly Journal of Economics, Oxford University Press, vol. 87(2), pages 239-266.
    9. Nachbar, J H, 1990. ""Evolutionary" Selection Dynamics in Games: Convergence and Limit Properties," International Journal of Game Theory, Springer;Game Theory Society, vol. 19(1), pages 59-89.
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    Citations

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    Cited by:

    1. Maxwell Pak & Bing Xu, 2016. "Generalized reinforcement learning in perfect-information games," International Journal of Game Theory, Springer;Game Theory Society, vol. 45(4), pages 985-1011, November.
    2. Jean-François Laslier & Bernard Walliser, 2015. "Stubborn learning," Theory and Decision, Springer, vol. 79(1), pages 51-93, July.
    3. Mengel, Friederike, 2012. "Learning across games," Games and Economic Behavior, Elsevier, vol. 74(2), pages 601-619.
    4. Ianni, Antonella, 2014. "Learning strict Nash equilibria through reinforcement," Journal of Mathematical Economics, Elsevier, vol. 50(C), pages 148-155.
    5. 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.
    6. Naoki Funai, 2013. "An Adaptive Learning Model in Coordination Games," Games, MDPI, Open Access Journal, vol. 4(4), pages 1-22, November.
    7. Burkhard C. Schipper & Jorg Oechssler & Albert Kolb, 2005. "Rage Against the Machines: How Subjects Learn to Play Against Computers," Working Papers 516, University of California, Davis, Department of Economics.
    8. Jean-François Laslier & Bilge Ozturk Goktuna, 2016. "Opportunist politicians and the evolution of electoral competition," Journal of Evolutionary Economics, Springer, vol. 26(2), pages 381-406, May.
    9. Oyarzun, Carlos & Sarin, Rajiv, 2013. "Learning and risk aversion," Journal of Economic Theory, Elsevier, vol. 148(1), pages 196-225.
    10. Walter Gutjahr, 2006. "Interaction dynamics of two reinforcement learners," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 14(1), pages 59-86, February.
    11. Izquierdo, Luis R. & Izquierdo, Segismundo S. & Gotts, Nicholas M. & Polhill, J. Gary, 2007. "Transient and asymptotic dynamics of reinforcement learning in games," Games and Economic Behavior, Elsevier, vol. 61(2), pages 259-276, November.
    12. Jacques Durieu & Philippe Solal, 2012. "Models of Adaptive Learning in Game Theory," Chapters,in: Handbook of Knowledge and Economics, chapter 11 Edward Elgar Publishing.
    13. Hopkins, Ed & Posch, Martin, 2005. "Attainability of boundary points under reinforcement learning," Games and Economic Behavior, Elsevier, vol. 53(1), pages 110-125, October.
    14. Viktoriya Semeshenko & Alexis Garapin & Bernard Ruffieux & Mirta Gordon, 2010. "Information-driven coordination: experimental results with heterogeneous individuals," Theory and Decision, Springer, vol. 69(1), pages 119-142, July.
    15. Schuster, Stephan, 2010. "Network Formation with Adaptive Agents," MPRA Paper 27388, University Library of Munich, Germany.
    16. Beggs, A.W., 2005. "On the convergence of reinforcement learning," Journal of Economic Theory, Elsevier, vol. 122(1), pages 1-36, May.
    17. Cominetti, Roberto & Melo, Emerson & Sorin, Sylvain, 2010. "A payoff-based learning procedure and its application to traffic games," Games and Economic Behavior, Elsevier, vol. 70(1), pages 71-83, September.
    18. Alanyali, Murat, 2010. "A note on adjusted replicator dynamics in iterated games," Journal of Mathematical Economics, Elsevier, vol. 46(1), pages 86-98, January.
    19. Funai Naoki, 2014. "An Adaptive Learning Model with Foregone Payoff Information," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 14(1), pages 1-28, January.
    20. 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.
    21. Burkhard C. Schipper & Jorg Oechssler & Albert Kolb, 2005. "Rage Against the Machines: How Subjects Learn to Play Against Computers," Working Papers 516, University of California, Davis, Department of Economics.
    22. Carlos Oyarzun & Rajiv Sarin, 2012. "Learning and Risk Aversion," Levine's Working Paper Archive 786969000000000572, David K. Levine.
    23. Judith Avrahami & Werner Güth & Yaakov Kareev, 2005. "Games of Competition in a Stochastic Environment," Theory and Decision, Springer, vol. 59(4), pages 255-294, December.
    24. Schuster, Stephan, 2012. "Applications in Agent-Based Computational Economics," MPRA Paper 47201, University Library of Munich, Germany.
    25. Semeshenko, Viktoriya & Gordon, Mirta B. & Nadal, Jean-Pierre, 2008. "Collective states in social systems with interacting learning agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(19), pages 4903-4916.

    More about this item

    Keywords

    LEARNING ; GAME THEORY ; BEHAVIOUR;

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General

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