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Equilibrium in misspecified Markov decision processes

Author

Listed:
  • Esponda, Ignacio

    (Department of Economics, University of California, Santa Barbara)

  • Pouzo, Demian

    (Department of Economics, UC Berkeley)

Abstract

We provide an equilibrium framework for modeling the behavior of an agent who holds a simplified view of a dynamic optimization problem. The agent faces a Markov Decision Process, where a transition probability function determines the evolution of a state variable as a function of the previous state and the agent’s action. The agent is uncertain about the true transition function and has a prior over a set of possible transition functions; this set reflects the agent’s (possibly simplified) view of her environment and may not contain the true function. We define an equilibrium concept and provide conditions under which it characterizes steady-state behavior when the agent updates her beliefs using Bayes’ rule.

Suggested Citation

  • Esponda, Ignacio & Pouzo, Demian, 2021. "Equilibrium in misspecified Markov decision processes," Theoretical Economics, Econometric Society, vol. 16(2), May.
    • Handle: RePEc:the:publsh:3843
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    Citations

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    Cited by:

    1. Esponda, Ignacio & Pouzo, Demian & Yamamoto, Yuichi, 2021. "Asymptotic behavior of Bayesian learners with misspecified models," Journal of Economic Theory, Elsevier, vol. 195(C).
    2. Yingkai Li & Aleksandrs Slivkins, 2022. "Exploration and Incentivizing Participation in Randomized Trials," Papers 2202.06191, arXiv.org, revised Mar 2025.
    3. Anderson, Robert M. & Duanmu, Haosui & Ghosh, Aniruddha & Khan, M. Ali, 2024. "On existence of Berk-Nash equilibria in misspecified Markov decision processes with infinite spaces," Journal of Economic Theory, Elsevier, vol. 217(C).
    4. Thomas J. Sargent & John Stachurski, 2024. "Dynamic Programming: Finite States," Papers 2401.10473, arXiv.org.

    More about this item

    Keywords

    Misspecified model; Markov decision process; equilibrium;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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