Two-Sided Learning and the Ratchet Principle
Name
2SLRP.pdf
Description
Accepted version
Size
637.18 KB
Format
Adobe PDF
Checksum (MD5)
3698a204dbeaa3c9d4a0ef441d30f66d
Author(s)
Cisternas Leyton, Gonzalo Sebastian
Date Issued
January 2018
Journal
Review of Economic Studies
Publisher
Oxford University Press (OUP)
Citation
Cisternas, Gonzalo et al. "Two-Sided Learning and the Ratchet Principle." Review of Economic Studies 85, 1 (January 2018): 307-351
Version
Author's final manuscript
Abstract
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.
MIT Department
Sloan School of Management
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1093/RESTUD/RDX019
