Learning from Manipulable Signals

Author:

Ekmekci Mehmet1,Gorno Leandro2,Maestri Lucas2,Sun Jian3,Wei Dong4

Affiliation:

1. Boston College )

2. FGV EPGE

3. Singapore Management University

4. University of California, Santa Cruz

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. (JEL C73, D82, D83, G24, M13)

Publisher

American Economic Association

Subject

Economics and Econometrics

Reference23 articles.

1. Inducing Leaders to Take Risky Decisions: Dismissal, Tenure, and Term Limits

2. Banerjee, Robin. 2022. Corporate Frauds: Business Crimes Now Bigger, Broader, Bolder. New Delhi, India: SAGE Publications.

3. Venture capital financing, moral hazard, and learning

4. Bergemann, Dirk, and Ulrich Hege. 2003. "The Value of Benchmarking." In Venture Capital Contracting and the Valuation of High Tech Firms, edited by Joseph McCahery and Luc Renneboog, 83-107. Oxford, UK: Oxford University Press.

5. Strategic Experimentation

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1. Signalling with Private Monitoring;Review of Economic Studies;2024-04-15

2. Hierarchical Bayesian Persuasion;SSRN Electronic Journal;2023

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