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.

中文翻译：

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无悔代理人的机制：超越共同的先验

一类丰富的机制设计问题可以理解为承诺政策的委托人和做出响应的代理人之间的不完全信息博弈，其收益由世界的未知状态决定。传统上，这些模型需要对信念（状态的常见先验）进行强有力且通常不切实际的假设。在本文中，我们省去了常见的先验。相反，我们考虑重复交互，其中委托人和代理都可以随着时间的推移从状态历史中学习。我们将机制设计重新表述为强化学习问题，并开发了无需对状态生成过程进行任何假设即可获得自然基准的机制。