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Rationalizable Learning

Author

Listed:
  • Andrew Caplin
  • Daniel J. Martin
  • Philip Marx

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. To implement this constraint, we introduce two conditions, one of which refines the mean preserving spread of Blackwell (1953) to take account for optimality, and the other of which generalizes the NIAC condition (Caplin and Dean 2015) and the NIAS condition (Caplin and Martin 2015) to allow for arbitrary learning. We apply our framework to show how identification of what was learned can be strengthened with additional assumptions on the form of Bayesian learning.

Suggested Citation

  • Andrew Caplin & Daniel J. Martin & Philip Marx, 2023. "Rationalizable Learning," NBER Working Papers 30873, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:30873
    Note: TWP
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    Other versions of this item:

    • 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.

    References listed on IDEAS

    as
    1. 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.
    2. Chambers, Christopher P. & Liu, Ce & Rehbeck, John, 2020. "Costly information acquisition," Journal of Economic Theory, Elsevier, vol. 186(C).
    3. 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.
    4. Andrew Caplin & Daniel Martin, 2015. "A Testable Theory of Imperfect Perception," Economic Journal, Royal Economic Society, vol. 125(582), pages 184-202, February.
    5. 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.
    6. 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.
    7. 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.
    8. Andrew Caplin & Mark Dean & Daniel Martin, 2011. "Search and Satisficing," American Economic Review, American Economic Association, vol. 101(7), pages 2899-2922, December.
    9. Chen, Xiaolu & Weng, Tongfeng & Li, Chunzi & Yang, Huijie, 2022. "Equivalence of machine learning models in modeling chaos," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).
    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. Mark Dean & Nathaniel Neligh, 2023. "Experimental Tests of Rational Inattention," Journal of Political Economy, University of Chicago Press, vol. 131(12), pages 3415-3461.
    12. repec:hal:pseose:halshs-01155313 is not listed on IDEAS
    13. Varian, Hal R, 1982. "The Nonparametric Approach to Demand Analysis," Econometrica, Econometric Society, vol. 50(4), pages 945-973, July.
    14. Caplin, Andrew & Martin, Daniel & Marx, Philip, 2025. "Modeling machine learning: A cognitive economic approach," Journal of Economic Theory, Elsevier, vol. 224(C).
    15. 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.
    16. Vivek Bhattacharya & Greg Howard, 2022. "Rational Inattention in the Infield," American Economic Journal: Microeconomics, American Economic Association, vol. 14(4), pages 348-393, November.
    17. Zach Y. Brown & Jihye Jeon, 2024. "Endogenous Information and Simplifying Insurance Choice," Econometrica, Econometric Society, vol. 92(3), pages 881-911, May.
    18. Andrew Caplin & Daniel Martin & Philip Marx, 2022. "Calibrating for Class Weights by Modeling Machine Learning," Papers 2205.04613, arXiv.org, revised Jul 2022.
    19. Tommaso Denti, 2022. "Posterior Separable Cost of Information," American Economic Review, American Economic Association, vol. 112(10), pages 3215-3259, October.
    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)

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    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).

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    More about this item

    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

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