IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2505.20708.html
   My bibliography  Save this paper

Berk-Nash Rationalizability

Author

Listed:
  • Ignacio Esponda
  • Demian Pouzo

Abstract

Misspecified learning -- where agents rely on simplified or biased models -- offers a unifying framework for analyzing behavioral biases, cognitive constraints, and systematic misperceptions. We introduce Berk--Nash rationalizability, a new solution concept for such settings that parallels rationalizability in games. Our main result shows that, with probability one, every limit action -- any action played or approached infinitely often -- is Berk--Nash rationalizable. This holds regardless of whether behavior converges and offers a tractable way to bound long-run behavior without solving complex learning dynamics. We illustrate this advantage with a known example and identify general classes of environments where the rationalizable set can be easily characterized.

Suggested Citation

  • Ignacio Esponda & Demian Pouzo, 2025. "Berk-Nash Rationalizability," Papers 2505.20708, arXiv.org, revised Jun 2025.
    • Handle: RePEc:arx:papers:2505.20708
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2505.20708
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fudenberg, Drew & Romanyuk, Gleb & Strack, Philipp, 2017. "Active learning with a misspecified prior," Theoretical Economics, Econometric Society, vol. 12(3), September.
    2. Jehiel, Philippe, 2005. "Analogy-based expectation equilibrium," Journal of Economic Theory, Elsevier, vol. 123(2), pages 81-104, August.
    3. He, Kevin, 2022. "Mislearning from censored data: The gambler's fallacy and other correlational mistakes in optimal-stopping problems," Theoretical Economics, Econometric Society, vol. 17(3), July.
    4. Kfir Eliaz & Ran Spiegler, 2020. "A Model of Competing Narratives," American Economic Review, American Economic Association, vol. 110(12), pages 3786-3816, December.
    5. Esponda, Ignacio & Pouzo, Demian & Yamamoto, Yuichi, 2021. "Asymptotic behavior of Bayesian learners with misspecified models," Journal of Economic Theory, Elsevier, vol. 195(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kevin He & Jonathan Libgober, 2020. "Evolutionarily Stable (Mis)specifications: Theory and Applications," Papers 2012.15007, arXiv.org, revised Feb 2023.
    2. J. Aislinn Bohren & Daniel N. Hauser, 2023. "Behavioral Foundations of Model Misspecification," PIER Working Paper Archive 23-007, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    3. Jehiel, Philippe & Mohlin, Erik, 2021. "Cycling and Categorical Learning in Decentralized Adverse Selection Economies," Working Papers 2021:11, Lund University, Department of Economics.
    4. José Luis Montiel Olea & Pietro Ortoleva & Mallesh Pai & Andrea Prat, 2021. "Competing Models," Working Papers 2021-89, Princeton University. Economics Department..
    5. Mira Frick & Ryota Iijima & Yuhta Ishii, 2020. "Misinterpreting Others and the Fragility of Social Learning," Econometrica, Econometric Society, vol. 88(6), pages 2281-2328, November.
    6. Takeshi Murooka & Yuichi Yamamoto, 2021. "Misspecified Bayesian Learning by Strategic Players: First-Order Misspecification and Higher-Order Misspecification," OSIPP Discussion Paper 21E008, Osaka School of International Public Policy, Osaka University.
    7. Philippe Jehiel & Erik Mohlin, 2023. "Categorization in Games: A Bias-Variance Perspective," Working Papers halshs-04154272, HAL.
    8. Mira Frick & Ryota Iijima & Yuhta Ishii, 2020. "Belief Convergence under Misspecified Learning: A Martingale Approach," Cowles Foundation Discussion Papers 2235R, Cowles Foundation for Research in Economics, Yale University, revised Mar 2021.
    9. Esponda, Ignacio & Pouzo, Demian & Yamamoto, Yuichi, 2021. "Asymptotic behavior of Bayesian learners with misspecified models," Journal of Economic Theory, Elsevier, vol. 195(C).
    10. Mira Frick & Ryota Iijima & Yuhta Ishii, 2020. "Belief Convergence under Misspecified Learning: A Martingale Approach," Cowles Foundation Discussion Papers 2235R3, Cowles Foundation for Research in Economics, Yale University, revised Apr 2022.
    11. Mira Frick & Ryota Iijima & Yuhta Ishii, 2020. "Stability and Robustness in Misspecified Learning Models," Cowles Foundation Discussion Papers 2235, Cowles Foundation for Research in Economics, Yale University.
    12. Ba, Cuimin & Gindin, Alice, 2023. "A multi-agent model of misspecified learning with overconfidence," Games and Economic Behavior, Elsevier, vol. 142(C), pages 315-338.
    13. Yingkai Li & Harry Pei, 2020. "Misspecified Beliefs about Time Lags," Papers 2012.07238, arXiv.org.
    14. Mira Frick & Ryota Iijima & Yuhta Ishii, 2023. "Belief Convergence under Misspecified Learning: A Martingale Approach," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(2), pages 781-814.
    15. Fudenberg, Drew & Lanzani, Giacomo & Strack, Philipp, 2023. "Pathwise concentration bounds for Bayesian beliefs," Theoretical Economics, Econometric Society, vol. 18(4), November.
    16. Philippe Jehiel, 2022. "Analogy-Based Expectation Equilibrium and Related Concepts:Theory, Applications, and Beyond," Working Papers halshs-03735680, HAL.
    17. Bowen, T. Renee & Galperti, Simone & Dmitriev, Danil, 2021. "Learning from Shared News: When Abundant Information Leads to Belief Polarization," CEPR Discussion Papers 15789, C.E.P.R. Discussion Papers.
    18. Gagnon-Bartsch, Tristan & Bushong, Benjamin, 2022. "Learning with misattribution of reference dependence," Journal of Economic Theory, Elsevier, vol. 203(C).
    19. Mira Frick & Ryota Iijima & Yuhta Ishii, 2021. "Welfare Comparisons for Biased Learning," Cowles Foundation Discussion Papers 2274, Cowles Foundation for Research in Economics, Yale University.
    20. Mira Frick & Ryota Iijima & Yuhta Ishii, 2018. "Dispersed Behavior and Perceptions in Assortative Societies," Cowles Foundation Discussion Papers 2128, Cowles Foundation for Research in Economics, Yale University.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2505.20708. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.