Two-Sided Learning and the Ratchet Principle

• 成果类型: Article
• 署名作者: Cisternas, Gonzalo
• 署名单位: Massachusetts Institute of Technology (MIT)
• 刊物名称: REVIEW OF ECONOMIC STUDIES
• ISSN/ISSBN: 0034-6527
• DOI: 10.1093/restud/rdx019
• 发表日期: 2018
• 页码: 307-351
• 关键词: earnings management; continuous-time; career concerns; INFORMATION; incentives; reputation; inflation; DISCRETION; CONTRACTS; agents

摘要

I study a class of continuous-time games of learning and imperfect monitoring. A long-run player and a market share a common prior about the initial value of a Gaussian hidden state, and learn about its subsequent values by observing a noisy public signal. The long-run player can nevertheless control the evolution of this signal, and thus affect the market's belief. The public signal has an additive structure, and noise is Brownian. I derive conditions for an ordinary differential equation to characterize equilibrium behavior in which the long-run player's actions depend on the history of the game only through the market's correct belief. Using these conditions, I demonstrate the existence of pure-strategy equilibria in Markov strategies for settings in which the long-run player's flow utility is nonlinear. The central finding is a learning-driven ratchet principle affecting incentives. I illustrate the economic implications of this principle in applications to monetary policy, earnings management, and career concerns.