• 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}
}

• M. Ekmekci, Leandro Gorno, +2 authors Dong Wei
• Published 17 July 2020
• Economics
• arXiv: Theoretical Economics

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 transparency can inhibit learning. As the players get arbitrarily patient, the principal elicits no useful information from the observed signal.