Learning from Manipulable Signals
@article{Ekmekci2020LearningFM, title={Learning from Manipulable Signals}, author={Mehmet Ekmekci and Leandro Gorno and Lucas Maestri and Jian Sun and Dong Wei}, journal={arXiv: Theoretical Economics}, year={2020} }
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, and 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…
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