Learning from Manipulable Signals
Mehmet Ekmekci, Leandro Gorno, Lucas Maestri, Jian Sun, Dong Wei
American Economic Review · 2022 · Volume 112 · Issue 12
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.
Keywords
Stochastic and Dynamic Games, Evolutionary Games, Repeated Games, Asymmetric and Private Information, Mechanism Design, Search, Learning, Information and Knowledge, Communication, Belief, Unawareness, Investment Banking, Venture Capital, Brokerage, Ratings and Ratings Agencies, New Firms, Startups
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