Learning from Manipulable Signals
AbstractWe 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.
CitationEkmekci, Mehmet, Leandro Gorno, Lucas Maestri, Jian Sun, and Dong Wei. 2022. "Learning from Manipulable Signals." American Economic Review, 112 (12): 3995-4040. DOI: 10.1257/aer.20211158
- C73 Stochastic and Dynamic Games; Evolutionary Games; Repeated Games
- D82 Asymmetric and Private Information; Mechanism Design
- D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
- G24 Investment Banking; Venture Capital; Brokerage; Ratings and Ratings Agencies
- M13 New Firms; Startups