Learning from Manipulable Signals

doi:10.1257/aer.20211158

Learning from Manipulable Signals

2022-12-01

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DOI：10.1257/aer.20211158

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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)

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Learning from Manipulable Signals

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