Learning from Manipulable Signals

• 成果类型: Article
• 署名作者: Ekmekci, Mehmet; Gorno, Leandro; Maestri, Lucas; Sun, Jian; Wei, Dong
• 署名单位: Boston College; Getulio Vargas Foundation; Singapore Management University; University of California System; University of California Santa Cruz
• 刊物名称: AMERICAN ECONOMIC REVIEW
• ISSN/ISSBN: 0002-8282
• DOI: 10.1257/aer.20211158
• 发表日期: 2022
• 页码: 3995-4040
• 关键词: Reputation

摘要

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 informa-tion from the observed signal. (JEL C73, D82, D83, G24, M13)