发表机构
Northwestern University; Peking University（西北大学; 北京大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究设计者对代理人私人信息部分有贝叶斯信念、部分存在模糊性时的鲁棒机制，给出“基于知识”机制鲁棒最优的条件，统一了早期研究并揭示新应用。
AI 中文摘要
我们研究当设计者对代理人私人信息的某些组成部分拥有贝叶斯信念，但对其他部分存在模糊性时的鲁棒机制。设计者通过机制在所有与她对贝叶斯组成部分的信念一致的联合分布上的最坏情况表现来评估机制。该框架涵盖多种情境，例如：委托人知晓状态分布但不了解代理人偏好（如代理人在各维度间的权衡）的多维授权问题；卖家对买家偏好的估计存在误设的筛选问题；以及代理人间信念对设计者而言存在模糊性的拍卖与投票设计问题。我们给出了“基于知识”机制（即仅以贝叶斯组成部分为条件、不考虑模糊组成部分的机制）鲁棒最优的条件。我们的结果统一了不同经济环境下的早期研究，并揭示了新的应用场景。
英文摘要
We study robust mechanisms when the designer possesses a Bayesian belief over some components of agents' private information but faces ambiguity over others. The designer evaluates mechanisms by their worst-case performance over all joint distributions consistent with her belief over the Bayesian components. The framework encompasses settings such as multidimensional delegation in which a principal knows the distribution of the state but not the agent's preferences (e.g., his tradeoffs across dimensions), screening in which a seller only has misspecified estimates of buyer preferences, and auction and voting design when agents' beliefs about each other are ambiguous to the designer. We provide conditions under which a \emph{knowledge-based} mechanism---one that conditions only on the Bayesian components but not the ambiguous ones---is robustly optimal. Our results unify earlier work across distinct economic environments and uncover new applications.
