IDEAS home Printed from https://ideas.repec.org/a/spr/joecth/v80y2025i1d10.1007_s00199-024-01627-z.html
   My bibliography  Save this article

Rationalizable learning

Author

Listed:
  • Andrew Caplin

    (New York University)

  • Daniel Martin

    (University of California)

  • Philip Marx

    (Louisiana State University)

Abstract

The central question we address in this paper is: what can an analyst infer from choice data about what a decision maker has learned? The key constraint we impose, which is shared across models of Bayesian learning, is that any learning must be rationalizable. We use our framework to show how identification can be strengthened as one imposes the assumptions behind more restrictive forms of Bayesian learning.

Suggested Citation

  • Andrew Caplin & Daniel Martin & Philip Marx, 2025. "Rationalizable learning," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 80(1), pages 171-202, August.
    • Handle: RePEc:spr:joecth:v:80:y:2025:i:1:d:10.1007_s00199-024-01627-z
    DOI: 10.1007/s00199-024-01627-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00199-024-01627-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00199-024-01627-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or

    for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Andrew Caplin & Daniel Martin, 2015. "A Testable Theory of Imperfect Perception," Economic Journal, Royal Economic Society, vol. 125(582), pages 184-202, February.
    2. Varian, Hal R, 1982. "The Nonparametric Approach to Demand Analysis," Econometrica, Econometric Society, vol. 50(4), pages 945-973, July.
    3. Andrew Caplin & Mark Dean, 2015. "Revealed Preference, Rational Inattention, and Costly Information Acquisition," American Economic Review, American Economic Association, vol. 105(7), pages 2183-2203, July.
    4. Andrew Caplin & Mark Dean & Daniel Martin, 2011. "Search and Satisficing," American Economic Review, American Economic Association, vol. 101(7), pages 2899-2922, December.
    5. Chen, Xiaolu & Weng, Tongfeng & Li, Chunzi & Yang, Huijie, 2022. "Equivalence of machine learning models in modeling chaos," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).
    6. Andrew Caplin & Dániel CsabaQuantCo & John Leahy & Oded Nov, 2020. "Rational Inattention, Competitive Supply, and Psychometrics," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 135(3), pages 1681-1724.
    7. Filip Matêjka & Alisdair McKay, 2015. "Rational Inattention to Discrete Choices: A New Foundation for the Multinomial Logit Model," American Economic Review, American Economic Association, vol. 105(1), pages 272-298, January.
    8. Caplin, Andrew & Martin, Daniel & Marx, Philip, 2025. "Modeling machine learning: A cognitive economic approach," Journal of Economic Theory, Elsevier, vol. 224(C).
    9. Andrew Caplin & Daniel Martin, 2021. "Comparison of Decisions under Unknown Experiments," Journal of Political Economy, University of Chicago Press, vol. 129(11), pages 3185-3205.
    10. David Almog & Daniel Martin, 2024. "Rational inattention in games: experimental evidence," Experimental Economics, Springer;Economic Science Association, vol. 27(4), pages 715-742, September.
    11. Andrew Caplin & Daniel Martin & Philip Marx, 2022. "Calibrating for Class Weights by Modeling Machine Learning," Papers 2205.04613, arXiv.org, revised Jul 2022.
    12. Chambers, Christopher P. & Liu, Ce & Rehbeck, John, 2020. "Costly information acquisition," Journal of Economic Theory, Elsevier, vol. 186(C).
    13. Andrew Caplin & Mark Dean & John Leahy, 2022. "Rationally Inattentive Behavior: Characterizing and Generalizing Shannon Entropy," Journal of Political Economy, University of Chicago Press, vol. 130(6), pages 1676-1715.
    14. Vivek Bhattacharya & Greg Howard, 2022. "Rational Inattention in the Infield," American Economic Journal: Microeconomics, American Economic Association, vol. 14(4), pages 348-393, November.
    15. Tommaso Denti, 2022. "Posterior Separable Cost of Information," American Economic Review, American Economic Association, vol. 112(10), pages 3215-3259, October.
    16. Zach Y. Brown & Jihye Jeon, 2024. "Endogenous Information and Simplifying Insurance Choice," Econometrica, Econometric Society, vol. 92(3), pages 881-911, May.
    17. Mark Dean & Nathaniel Neligh, 2023. "Experimental Tests of Rational Inattention," Journal of Political Economy, University of Chicago Press, vol. 131(12), pages 3415-3461.
    18. repec:hal:pseose:halshs-01155313 is not listed on IDEAS
    19. Ashesh Rambachan, 2024. "Identifying Prediction Mistakes in Observational Data," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 139(3), pages 1665-1711.
    20. Rochet, Jean-Charles, 1987. "A necessary and sufficient condition for rationalizability in a quasi-linear context," Journal of Mathematical Economics, Elsevier, vol. 16(2), pages 191-200, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Caplin, Andrew & Martin, Daniel & Marx, Philip, 2025. "Modeling machine learning: A cognitive economic approach," Journal of Economic Theory, Elsevier, vol. 224(C).

    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. Caplin, Andrew & Martin, Daniel & Marx, Philip, 2025. "Modeling machine learning: A cognitive economic approach," Journal of Economic Theory, Elsevier, vol. 224(C).
    2. Andrew Caplin & Mark Dean & John Leahy, 2022. "Rationally Inattentive Behavior: Characterizing and Generalizing Shannon Entropy," Journal of Political Economy, University of Chicago Press, vol. 130(6), pages 1676-1715.
    3. Duffy, Sean & Gussman, Steven & Smith, John, 2019. "Judgments of length in the economics laboratory: Are there brains in choice?," MPRA Paper 93126, University Library of Munich, Germany.
    4. Sean Duffy & John Smith, 2025. "An economist and a psychologist form a line: What can imperfect perception of length tell us about stochastic choice?," Theory and Decision, Springer, vol. 99(3), pages 701-734, November.
    5. Brocas, Isabelle & Carrillo, Juan D., 2021. "Value computation and modulation: A neuroeconomic theory of self-control as constrained optimization," Journal of Economic Theory, Elsevier, vol. 198(C).
    6. Bartosz Maćkowiak & Filip Matějka & Mirko Wiederholt, 2023. "Rational Inattention: A Review," Journal of Economic Literature, American Economic Association, vol. 61(1), pages 226-273, March.
    7. Tommaso Denti & Doron Ravid, 2023. "Robust Predictions in Games with Rational Inattention," Papers 2306.09964, arXiv.org.
    8. Mogens Fosgerau & Emerson Melo & André de Palma & Matthew Shum, 2020. "Discrete Choice And Rational Inattention: A General Equivalence Result," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 61(4), pages 1569-1589, November.
    9. Jianjun Miao & Hao Xing, 2024. "Dynamic discrete choice under rational inattention," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 77(3), pages 597-652, May.
    10. David Almog & Daniel Martin, 2024. "Rational inattention in games: experimental evidence," Experimental Economics, Springer;Economic Science Association, vol. 27(4), pages 715-742, September.
    11. Spyros Galanis & Sergei Mikhalishchev, 2024. "Information Aggregation with Costly Information Acquisition," Papers 2406.07186, arXiv.org, revised Jun 2025.
    12. Xavier Gabaix, 2017. "Behavioral Inattention," NBER Working Papers 24096, National Bureau of Economic Research, Inc.
    13. Duffy, Sean & Gussman, Steven & Smith, John, 2021. "Visual judgments of length in the economics laboratory: Are there brains in stochastic choice?," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 93(C).
    14. Egor Bronnikov & Elias Tsakas, 2025. "A robust measure of complexity," Papers 2501.09139, arXiv.org.
    15. Flynn, Joel P. & Sastry, Karthik A., 2023. "Strategic mistakes," Journal of Economic Theory, Elsevier, vol. 212(C).
    16. Chambers, Christopher P. & Liu, Ce & Rehbeck, John, 2020. "Costly information acquisition," Journal of Economic Theory, Elsevier, vol. 186(C).
    17. Andrew Caplin & Dániel CsabaQuantCo & John Leahy & Oded Nov, 2020. "Rational Inattention, Competitive Supply, and Psychometrics," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 135(3), pages 1681-1724.
    18. Walker-Jones, David, 2025. "Foundation and identification of multi-attribute Shannon entropy," Games and Economic Behavior, Elsevier, vol. 150(C), pages 334-355.
    19. Rehbeck, John, 2023. "Revealed Bayesian expected utility with limited data," Journal of Economic Behavior & Organization, Elsevier, vol. 207(C), pages 81-95.
    20. Duffy, Sean & Smith, John, 2025. "Stochastic choice and imperfect judgments of line lengths: What is hiding in the noise?," Journal of Economic Psychology, Elsevier, vol. 106(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making

    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:spr:joecth:v:80:y:2025:i:1:d:10.1007_s00199-024-01627-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.