Learning from Manipulable Signals

Mehmet Ekmekci (), Leandro Gorno, Lucas Maestri, Jian Sun and Dong Wei

Papers from arXiv.org

Abstract: We study a dynamic stopping game between a principal and an agent. The agent is privately informed about his type. The principal learns about the agent's type from a noisy performance measure, which 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 (expected) performance. Our model also predicts that, due to endogenous signal manipulation, too much transparency can inhibit learning. As the players get arbitrarily patient, the principal elicits no useful information from the observed signal.

Date: 2020-07, Revised 2021-07
New Economics Papers: this item is included in nep-gth and nep-mic
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Citations: View citations in EconPapers (1)

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Journal Article: Learning from Manipulable Signals (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2007.08762

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