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Learning Across Games

Author

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  • Friederike Mengel

    () (Universidad de Alicante)

Abstract

In this paper (reinforcement) learning of decision makers that face many different games is studied. As learning separately for all games can be too costly (require too much reasoning resources) agents are assumed to partition the set of all games into analogy classes. Partitions of higher cardinality are more costly. A process of simultaneous learning of actions and partitions is presented and equilibrium partitions and action choices characterized. The model is able to explain deviations from subgame perfection that are sometimes observed in experiments even for vanishingly small reasoning costs. Furthermore it is shown that learning across games can stabilize mixed equilibria in 2×2 Coordination and Anti-Coordination games and destabilize strict Nash equilibria under certain conditions.

Suggested Citation

  • Friederike Mengel, 2007. "Learning Across Games," Working Papers. Serie AD 2007-05, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
  • Handle: RePEc:ivi:wpasad:2007-05
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Edward W. Piotrowski & Jan Sladkowski & Anna Szczypinska, "undated". "Reinforcement Learning in Market Games," Departmental Working Papers 30, University of Bialtystok, Department of Theoretical Physics.
    2. Gabor Lugosi & Omiros Papaspiliopoulos & Gilles Stoltz, 2009. "Online Multi-task Learning with Hard Constraints," Working Papers hal-00362643, HAL.
    3. Gauer, Florian & Kuzmics, Christoph, 2016. "Cognitive empathy in conflict situations," Center for Mathematical Economics Working Papers 551, Center for Mathematical Economics, Bielefeld University.
    4. Mohlin, Erik, 2012. "Evolution of theories of mind," Games and Economic Behavior, Elsevier, vol. 75(1), pages 299-318.
    5. Friederike Mengel & Emanuela Sciubba, 2010. "Extrapolation in Games of Coordination and Dominance Solvable Games," Working Papers 2010.148, Fondazione Eni Enrico Mattei.
    6. Vessela Daskalova & Nicolaas J. Vriend, 2014. "Categorization and Coordination," Cambridge Working Papers in Economics 1460, Faculty of Economics, University of Cambridge.
    7. Steiner, Jakub & Stewart, Colin, 2008. "Contagion through learning," Theoretical Economics, Econometric Society, vol. 3(4), December.
    8. Mengel, Friederike & Sciubba, Emanuela, 2014. "Extrapolation and structural similarity in games," Economics Letters, Elsevier, vol. 125(3), pages 381-385.
    9. K.J.M. De Jaegher & B. Hoyer, 2012. "Cooperation and the common enemy effect," Working Papers 12-24, Utrecht School of Economics.
    10. Grimm, Veronika & Mengel, Friederike, 2012. "An experiment on learning in a multiple games environment," Journal of Economic Theory, Elsevier, vol. 147(6), pages 2220-2259.
    11. Christoph March, 2011. "Adaptive social learning," PSE Working Papers halshs-00572528, HAL.
    12. Mohlin, Erik, 2014. "Optimal categorization," Journal of Economic Theory, Elsevier, vol. 152(C), pages 356-381.
    13. Arina Nikandrova, 2013. "Repeated Play of Families of Games by Resource-Constrained Players," Games, MDPI, Open Access Journal, vol. 4(3), pages 1-8, July.
    14. Yuval Heller & Eyal Winter, 2016. "Rule Rationality," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 57, pages 997-1026, August.
    15. Benndorf, Volker & Martínez-Martínez, Ismael & Normann, Hans-Theo, 2016. "Equilibrium selection with coupled populations in hawk–dove games: Theory and experiment in continuous time," Journal of Economic Theory, Elsevier, vol. 165(C), pages 472-486.
    16. Christoph March, 2011. "Adaptive social learning," Working Papers halshs-00572528, HAL.
    17. Edward W. Piotrowski & Jan Sladkowski & Anna Szczypinska, 2007. "Reinforcement learning in market games," Papers 0710.0114, arXiv.org.

    More about this item

    Keywords

    Game Theory; Bounded Rationality; Reinforcement Learning; Analogies.;

    JEL classification:

    • C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games

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