• Corpus ID: 220633054

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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