Learning from Manipulable Signals
研究委托人与代理人之间的动态停止博弈，代理人可通过隐蔽且高成本的行为操纵噪声绩效指标，刻画唯一马尔可夫均衡，发现终止前操纵强度与预期绩效飙升，且过度透明会抑制学习。
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 information from the observed signal.