IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/30873.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w30873.pdf
    Download Restriction: no
    ---><---

    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

    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:nbr:nberwo:30873. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.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.