论文标题
解决动态的主体代理问题，以理性的不专心校长
Solving Dynamic Principal-Agent Problems with a Rationally Inattentive Principal
论文作者
论文摘要
校长代理（PA）问题描述了一系列广泛的经济关系，其特征是激励措施和非对称信息。校长的问题是在鉴于可用信息的情况下找到最佳的激励措施，例如，经理为其员工设定最佳工资。尽管通常认为主体是理性的，但当主体有限理性时，尤其是在顺序环境中，具有多个代理和多个信息渠道时，有关解决方案的知之甚少。在这里，我们开发了Rirl，这是一个深厚的增强学习框架，该框架以理性的不集中校长解决了如此复杂的PA问题。这样的主体会产生关注信息的成本，这可以模拟有限理性的形式。我们使用RIRR来分析经理与雇员关系中的丰富经济现象。在单步环境中，1）RIRL产生与理论预测一致的工资； 2）非零的注意力成本导致更简单但盈利较低的工资结构，并增加了代理商福利。在与多个代理的顺序环境中，RIRL显示出校长对不同信息通道的不关心后果：1）基于能力差异，对代理的输出的注意力不集中，缩小了工资差距； 2）对代理商的努力不集中会引起社会困境动态，在该动态中，代理商更加努力地工作，但本质上是自由的。此外，Rirl揭示了校长的不集中和代理类型之间的非平凡关系，例如，如果代理人容易出现在最佳的努力选择，则付款时间表对委托人的注意力成本更为敏感。因此，Rirl可以揭示新的经济关系，并在理解动态环境中有限理性的影响方面取得了进步。
Principal-Agent (PA) problems describe a broad class of economic relationships characterized by misaligned incentives and asymmetric information. The Principal's problem is to find optimal incentives given the available information, e.g., a manager setting optimal wages for its employees. Whereas the Principal is often assumed rational, comparatively little is known about solutions when the Principal is boundedly rational, especially in the sequential setting, with multiple Agents, and with multiple information channels. Here, we develop RIRL, a deep reinforcement learning framework that solves such complex PA problems with a rationally inattentive Principal. Such a Principal incurs a cost for paying attention to information, which can model forms of bounded rationality. We use RIRL to analyze rich economic phenomena in manager-employee relationships. In the single-step setting, 1) RIRL yields wages that are consistent with theoretical predictions; and 2) non-zero attention costs lead to simpler but less profitable wage structures, and increased Agent welfare. In a sequential setting with multiple Agents, RIRL shows opposing consequences of the Principal's inattention to different information channels: 1) inattention to Agents' outputs closes wage gaps based on ability differences; and 2) inattention to Agents' efforts induces a social dilemma dynamic in which Agents work harder, but essentially for free. Moreover, RIRL reveals non-trivial relationships between the Principal's inattention and Agent types, e.g., if Agents are prone to sub-optimal effort choices, payment schedules are more sensitive to the Principal's attention cost. As such, RIRL can reveal novel economic relationships and enables progress towards understanding the effects of bounded rationality in dynamic settings.
