Payoff information and learning in signaling games
Name
1709.01024.pdf
Description
Accepted version
Size
842.43 KB
Format
Adobe PDF
Checksum (MD5)
29e749407ebe058fe8ff1fe147dc4afc
Author(s)
Fudenberg, Drew
Date Issued
March 2020
Journal
Games and Economic Behavior
Publisher
Elsevier BV
Citation
Fudenberg, Drew and Kevin He. “Payoff information and learning in signaling games.” Games and Economic Behavior, 120, (March 2019): 96-120 © 2019 The Author(s)
Version
Author's final manuscript
Abstract
We add the assumption that players know their opponents' payoff functions and rationality to a model of non-equilibrium learning in signaling games. Agents are born into player roles and play against random opponents every period. Inexperienced agents are uncertain about the prevailing distribution of opponents' play, but believe that opponents never choose conditionally dominated strategies. Agents engage in active learning and update beliefs based on personal observations. Payoff information can refine or expand learning predictions, since patient young senders' experimentation incentives depend on which receiver responses they deem plausible. We show that with payoff knowledge, the limiting set of long-run learning outcomes is bounded above by rationality-compatible equilibria (RCE), and bounded below by uniform RCE. RCE refine the Intuitive Criterion (Cho and Kreps, 1987) and include all divine equilibria (Banks and Sobel, 1987). Uniform RCE sometimes but not always exists, and implies universally divine equilibrium.
MIT Department
Massachusetts Institute of Technology. Department of Economics
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.GEB.2019.11.011
