从可操纵信号中学习
Learning from Manipulable Signals
摘要：
我们研究了委托人和代理人之间的动态停止博弈。代理人被私下告知他的类型。主体从嘈杂的性能测量中了解代理的类型，该性能测量可以由代理通过代价高昂的隐藏动作来操纵。我们充分刻画了这个博弈的唯一马尔可夫均衡。我们发现，终止/市场崩溃之前，往往是一个尖峰（预期）的性能。我们的模型还预测，由于内源性信号操纵，太多的透明度会抑制学习。当参与者变得任意耐心时，校长从观察到的信号中提取不出有用的信息。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
