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Equilibrium selection in the stag hunt game under generalized reinforcement learning

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  • Lahkar, Ratul

Abstract

We apply the generalized reinforcement (GR) learning protocol to the stag hunt game. GR learning combines positive and negative reinforcement. The GR learning rule generates the GR dynamic, which governs the evolution of the mixed strategy of agents in the population. We identify conditions under which the GR dynamic converges globally to one of the two pure strategy Nash equilibria of the game.

Suggested Citation

  • Lahkar, Ratul, 2017. "Equilibrium selection in the stag hunt game under generalized reinforcement learning," Journal of Economic Behavior & Organization, Elsevier, vol. 138(C), pages 63-68.
    • Handle: RePEc:eee:jeborg:v:138:y:2017:i:c:p:63-68
    DOI: 10.1016/j.jebo.2017.04.012
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    Cited by:

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    2. Edgar J. Sánchez Carrera & Elena Gubar & Andrey F. Oleynik, 2019. "Network Structures and Poverty Traps," Dynamic Games and Applications, Springer, vol. 9(1), pages 236-253, March.

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    More about this item

    Keywords

    Reinforcement learning; Generalized reinforcement dynamic; Stag hunt game;
    All these keywords.

    JEL classification:

    • 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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