摘要翻译：
研究了委托人和代理人之间的动态停止对策。代理人被私下告知他的类型。主体从一个有噪声的性能度量中学习代理的类型，该性能度量可以由代理通过一个代价高昂和隐藏的动作来操纵。我们充分刻划了这个对策的唯一马尔可夫均衡。我们发现，终止/市场崩溃通常是在（预期）业绩飙升之前。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。当玩家变得任意耐心时，主从观察到的信号中提取不出有用的信息。
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英文标题：
《Learning from Manipulable Signals》
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作者：
Mehmet Ekmekci, Leandro Gorno, Lucas Maestri, Jian Sun, Dong Wei
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最新提交年份：
2021
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分类信息：

一级分类：Economics        经济学
二级分类：Theoretical Economics        理论经济学
分类描述：Includes theoretical contributions to Contract Theory, Decision Theory, Game Theory, General Equilibrium, Growth, Learning and Evolution, Macroeconomics, Market and Mechanism Design, and Social Choice.
包括对契约理论、决策理论、博弈论、一般均衡、增长、学习与进化、宏观经济学、市场与机制设计、社会选择的理论贡献。
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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.
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PDF链接：
https://arxiv.org/pdf/2007.08762