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Learning from Manipulable Signals (with M. Ekmekci, L. Gorno, and J.S.D. Wei)

Microeconomics Seminar 2020-2021
joint with the Département de sciences économiques, Université de Montréal

Organizer : Sean Horan (U. of Montreal)

* Invitation only. Please contact the organizer if you would like access.

 


 
Résumé
: We study a dynamic stopping game between a principal and an agent. The agent is privately informed about his type. The principal learns about the agent’s type from a noisy performance measure, which 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 (expected) performance. Our model also predicts that, due to endogenous signal manipulation, too much transparency can inhibit learning. As the players get arbitrarily patient, the principal elicits no useful information from the observed signal.

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