论文标题
从可操作的信号中学习
Learning from Manipulable Signals
论文作者
论文摘要
我们研究校长和代理商之间的动态停止游戏。代理商被私下告知他的类型。校长从嘈杂的性能度量中了解了代理的类型，代理可以通过昂贵和隐藏的动作来操纵。我们完全表征了这个游戏的独特马尔可夫均衡。我们发现终止/市场崩溃通常是在（预期）性能的激增之前。我们的模型还预测，由于内源性信号操纵，透明度过多会抑制学习。随着玩家任意患者的患者，本金没有从观察到的信号中引起有用的信息。
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 of this game. We 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.
