IDEAS home Printed from https://ideas.repec.org/p/pen/papers/26-010.html

The Generalized Euler Equation and the Bankruptcy-Sovereign Default Problem

Author

Listed:
  • J. Aislinn Bohren

    (University of Pennsylvania)

  • Daniel N. Hauser

    (University School of Business and Helsinki)

Abstract

We develop a flexible model of confirmation bias, in which individuals misinterpret signals as stronger evidence for the state favored by their prior than they actually are. The bias varies with the agent's current belief, nesting Rabin and Schrag (1999) as a special case. In an individual learning setting, incorrect learning arises with positive probability when the bias is severe, generalizing existing work. By contrast, in a social learning setting, correct learning obtains almost surely regardless of the severity of bias. Whereas social information typically introduces inefficiencies, here it mitigates them: social interaction overcomes bias that would otherwise generate incorrect learning.Length: 25 pages

Suggested Citation

  • J. Aislinn Bohren & Daniel N. Hauser, 2026. "The Generalized Euler Equation and the Bankruptcy-Sovereign Default Problem," PIER Working Paper Archive 26-010, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    • Handle: RePEc:pen:papers:26-010
    as

    Download full text from publisher

    File URL: https://economics.sas.upenn.edu/system/files/working-papers/26-010%20PIER%20Paper%20Submission.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ignacio Esponda & Demian Pouzo, 2026. "Learning and Equilibrium under Model Misspecification," Papers 2601.09891, arXiv.org.
    2. Daniel J. Benjamin & Matthew Rabin & Collin Raymond, 2016. "A Model of Nonbelief in the Law of Large Numbers," Journal of the European Economic Association, European Economic Association, vol. 14(2), pages 515-544.
    3. T Renee Bowen & Danil Dmitriev & Simone Galperti, 2023. "Learning from Shared News: When Abundant Information Leads to Belief Polarization," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 138(2), pages 955-1000.
    4. John W.E. Cremin, 2025. "Too Much Information & The Death of Consensus," Working Papers hal-05421416, HAL.
    5. De Filippis, Roberta & Guarino, Antonio & Jehiel, Philippe & Kitagawa, Toru, 2022. "Non-Bayesian updating in a social learning experiment," Journal of Economic Theory, Elsevier, vol. 199(C).
    6. Sushil Bikhchandani & David Hirshleifer & Omer Tamuz & Ivo Welch, 2024. "Information Cascades and Social Learning," Journal of Economic Literature, American Economic Association, vol. 62(3), pages 1040-1093, September.
    7. Arieli, Itai & Babichenko, Yakov & Müller, Stephan & Pourbabaee, Farzad & Tamuz, Omer, 2025. "The hazards and benefits of condescension in social learning," Theoretical Economics, Econometric Society, vol. 20(1), January.
    8. Bohren, Aislinn & Hauser, Daniel, 2017. "Learning with Heterogeneous Misspecified Models: Characterization and Robustness," CEPR Discussion Papers 12036, Centre for Economic Policy Research.
    9. David Eil & Justin M. Rao, 2011. "The Good News-Bad News Effect: Asymmetric Processing of Objective Information about Yourself," American Economic Journal: Microeconomics, American Economic Association, vol. 3(2), pages 114-138, May.
    10. Lones Smith & Peter Sorensen, 2000. "Pathological Outcomes of Observational Learning," Econometrica, Econometric Society, vol. 68(2), pages 371-398, March.
    11. 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.
    12. Jaden Yang Chen, 2026. "Sequential Learning under Informational Ambiguity," American Economic Review, American Economic Association, vol. 116(1), pages 209-245, January.
    13. Matthew Rabin & Joel L. Schrag, 1999. "First Impressions Matter: A Model of Confirmatory Bias," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 114(1), pages 37-82.
    14. J. Aislinn Bohren & Daniel N. Hauser, 2021. "Learning With Heterogeneous Misspecified Models: Characterization and Robustness," Econometrica, Econometric Society, vol. 89(6), pages 3025-3077, November.
    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. Florian Brandl & Wanying Huang & Atulya Jain, 2026. "On the Inefficiency of Social Learning," Papers 2602.08812, arXiv.org, revised Oct 2026.
    2. Ing-Haw Cheng & Alice Hsiaw, 2023. "Bayesian Doublespeak," Working Papers 135, Brandeis School of Business and Economics.
    3. Florian Brandl & Wanying Huang & Atulya Jain, 2026. "On the Inefficiency of Social Learning," ECONtribute Discussion Papers Series 433, University of Bonn and University of Cologne, Germany.
    4. 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.
    5. 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.
    6. Chen, Jaden Yang, 2022. "Biased learning under ambiguous information," Journal of Economic Theory, Elsevier, vol. 203(C).
    7. 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.
    8. Zikai Xu, 2022. "Observational Learning with Competitive Prices," Papers 2202.06425, arXiv.org, revised May 2022.
    9. Larry Samuelson & Jakub Steiner, 2024. "Robust latent data representations," ECON - Working Papers 460, Department of Economics - University of Zurich, revised Jul 2025.
    10. Zihan Zhao, 2026. "Social Learning with Selective Sampling," Papers 2608.16599, arXiv.org.
    11. Agranov, Marina & Lopez-Moctezuma, Gabriel & Strack, Philipp & Tamuz, Omer, 2026. "Learning through imitation: An experiment," Journal of Economic Theory, Elsevier, vol. 235(C).
    12. Daria Fedyaeva & Georgy Lukyanov & Hannah Tolli'e, 2025. "Learning to Unlearn: Education as a Remedy for Misspecified Beliefs," Papers 2510.24735, arXiv.org, revised Aug 2026.
    13. Le Yaouanq, Yves, 2023. "A model of voting with motivated beliefs," Journal of Economic Behavior & Organization, Elsevier, vol. 213(C), pages 394-408.
    14. Hestermann, Nina & Le Yaouanq, Yves, 2018. "It\'s not my Fault! Self-Confidence and Experimentation," Rationality and Competition Discussion Paper Series 124, CRC TRR 190 Rationality and Competition.
    15. Francesco Bilotta & Luca Braghieri & Collin Raymond & Mark Whitmeyer, 2026. "Agreement and Diversity in Interpretation," Papers 2607.05558, arXiv.org.
    16. Ignacio Esponda & Demian Pouzo, 2026. "Learning and Equilibrium under Model Misspecification," Papers 2601.09891, arXiv.org.
    17. He, Kevin & Libgober, Jonathan, 2025. "Misspecified learning and evolutionary stability," Journal of Economic Theory, Elsevier, vol. 230(C).
    18. Daniel J. Benjamin, 2018. "Errors in Probabilistic Reasoning and Judgment Biases," GRU Working Paper Series GRU_2018_023, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
    19. Braz Camargo & Laura Karpuska & Caio Lorecchio, 2023. "Political Accountability and Misinformation," UB School of Economics Working Papers 2023/460, University of Barcelona School of Economics.
    20. Jan Hanousek Jr. & Jan Hanousek & Konstantin Sokolov, 2023. "X Bots and Earnings Announcements," MENDELU Working Papers in Business and Economics 2023-92, Mendel University in Brno, Faculty of Business and Economics.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:pen:papers:26-010. 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: Administrator (email available below). General contact details of provider: https://edirc.repec.org/data/deupaus.html .

    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.