论文标题
无重格代理的机制：超越普通先验
Mechanisms for a No-Regret Agent: Beyond the Common Prior
论文作者
论文摘要
丰富的机制设计问题可以理解为委托人对一项政策的校长和反应的代理商之间的不完整信息，并由未知状态确定的回报。传统上，这些模型需要关于信念的强烈且经常实行的假设（在国家的普遍之前）。在本文中，我们借鉴了共同的先验。取而代之的是，我们考虑了一种重复的互动，委托人和代理商都可以随着时间的推移从状态历史中学习。我们将机制设计为强化学习问题，并开发出获得自然基准的机制，而没有对国家生成过程的任何假设。我们的结果利用了对代理的新型行为假设（以反事实内部的遗憾为中心），这些假设在不依赖信念的情况下捕捉了理性精神。
A rich class of mechanism design problems can be understood as incomplete-information games between a principal who commits to a policy and an agent who responds, with payoffs determined by an unknown state of the world. Traditionally, these models require strong and often-impractical assumptions about beliefs (a common prior over the state). In this paper, we dispense with the common prior. Instead, we consider a repeated interaction where both the principal and the agent may learn over time from the state history. We reformulate mechanism design as a reinforcement learning problem and develop mechanisms that attain natural benchmarks without any assumptions on the state-generating process. Our results make use of novel behavioral assumptions for the agent -- centered around counterfactual internal regret -- that capture the spirit of rationality without relying on beliefs.
