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Stationary Bayesian–Markov Equilibria in Bayesian Stochastic Games with Periodic Revelation

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  • Eunmi Ko

    (Department of Economics, Rochester Institute of Technology, 92 Lomb Memorial Dr, Rochester, NY 14623, USA)

Abstract

I consider a class of dynamic Bayesian games in which types evolve stochastically according to a first-order Markov process on a continuous type space. Types are privately informed, but they become public together with actions when payoffs are obtained, resulting in a delayed information revelation. In this environment, I show that there exists a stationary Bayesian–Markov equilibrium in which a player’s strategy maps a tuple of the previous type and action profiles and the player’s current type to a mixed action. The existence can be extended to K -periodic revelation. I also offer a computational algorithm to find an equilibrium.

Suggested Citation

  • Eunmi Ko, 2024. "Stationary Bayesian–Markov Equilibria in Bayesian Stochastic Games with Periodic Revelation," Games, MDPI, vol. 15(5), pages 1-17, September.
    • Handle: RePEc:gam:jgames:v:15:y:2024:i:5:p:31-:d:1476156
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    References listed on IDEAS

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