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Best-response dynamics in zero-sum stochastic games

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  • Leslie, David S.
  • Perkins, Steven
  • Xu, Zibo

Abstract

We define and analyse three learning dynamics for two-player zero-sum discounted-payoff stochastic games. A continuous-time best-response dynamic in mixed strategies is proved to converge to the set of Nash equilibrium stationary strategies. Extending this, we introduce a fictitious-play-like process in a continuous-time embedding of a stochastic zero-sum game, which is again shown to converge to the set of Nash equilibrium strategies. Finally, we present a modified δ-converging best-response dynamic, in which the discount rate converges to 1, and the learned value converges to the asymptotic value of the zero-sum stochastic game. The critical feature of all the dynamic processes is a separation of adaption rates: beliefs about the value of states adapt more slowly than the strategies adapt, and in the case of the δ-converging dynamic the discount rate adapts more slowly than everything else.

Suggested Citation

  • Leslie, David S. & Perkins, Steven & Xu, Zibo, 2020. "Best-response dynamics in zero-sum stochastic games," Journal of Economic Theory, Elsevier, vol. 189(C).
    • Handle: RePEc:eee:jetheo:v:189:y:2020:i:c:s0022053120300892
    DOI: 10.1016/j.jet.2020.105095
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    Cited by:

    1. Lucas Baudin & Rida Laraki, 2022. "Fictitious Play and Best-Response Dynamics in Identical Interest and Zero Sum Stochastic Games," Post-Print hal-03767937, HAL.

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    More about this item

    Keywords

    Stochastic games; Best-response dynamics; Zero-sum games; Convergence;
    All these keywords.

    JEL classification:

    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games

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