Learning from Manipulable Signals

Mehmet Ekmekci (), Leandro Gorno, Lucas Maestri, Jian Sun and Dong Wei

American Economic Review, 2022, vol. 112, issue 12, 3995-4040

Abstract: We study a dynamic stopping game between a principal and an agent. The principal gradually learns about the agent's private type from a noisy performance measure that can be manipulated by the agent via a costly and hidden action. We fully characterize the unique Markov equilibrium of this game. We find that terminations/market crashes are often preceded by a spike in manipulation intensity and (expected) performance. Moreover, due to endogenous signal manipulation, too much transparency can inhibit learning and harm the principal. As the players get arbitrarily patient, the principal elicits no useful information from the observed signal.

JEL-codes: C73 D82 D83 G24 M13 (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.aeaweb.org/doi/10.1257/aer.20211158 (application/pdf)
https://www.aeaweb.org/doi/10.1257/aer.20211158.appx (application/pdf)
https://www.aeaweb.org/doi/10.1257/aer.20211158.ds (application/zip)
Access to full text is restricted to AEA members and institutional subscribers.

Related works:
Working Paper: Learning from Manipulable Signals (2021)
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:aea:aecrev:v:112:y:2022:i:12:p:3995-4040

Ordering information: This journal article can be ordered from
https://www.aeaweb.org/journals/subscriptions

DOI: 10.1257/aer.20211158

Access Statistics for this article

American Economic Review is currently edited by Esther Duflo

More articles in American Economic Review from American Economic Association Contact information at EDIRC.
Bibliographic data for series maintained by Michael P. Albert ().