Rationalizable learning

Andrew Caplin, Daniel Martin () and Philip Marx
Additional contact information
Andrew Caplin: New York University
Daniel Martin: University of California
Philip Marx: Louisiana State University

Economic Theory, 2025, vol. 80, issue 1, No 5, 202 pages

Abstract: 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.

Keywords: Rational inattention; Revealed preference; Stochastic choice; Information acquisition; Learning; Identification; D11; D81; D83 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00199-024-01627-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joecth:v:80:y:2025:i:1:d:10.1007_s00199-024-01627-z

Ordering information: This journal article can be ordered from
http://www.springer. ... eory/journal/199/PS2

DOI: 10.1007/s00199-024-01627-z

Access Statistics for this article

Economic Theory is currently edited by Nichoals Yanneils

More articles in Economic Theory from Springer, Society for the Advancement of Economic Theory (SAET) Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().