发表机构
City University of Hong Kong（香港城市大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究如何设计信息引导决策者风险偏好及决策结果，构建说服框架，接收者依信息修正风险偏好，通过分析后验信念分布刻画发送者最优信息设计，确定有益条件并通过再保险设计展现框架在风险管理中的潜力。
AI 中文摘要
决策者的风险偏好本质上是不稳定的，可能会根据外部信息进行调整，从而影响后续选择和结果。本文构建了一个说服框架，研究如何设计信息来引导风险偏好和决策结果。在模型中，接收者以由连贯风险度量表示的初始风险偏好开始，在观察到发送者声称的信息规则生成的系统状态后进行修正，且修正必须保持状态实现前后风险评估的时间一致性。通过分析后验信念在状态上的诱导分布来刻画发送者的最优信息设计。我们确定了在多种情况下信息设计对发送者有益的条件，并通过再保险设计应用说明了该框架在风险管理中的潜力。
英文摘要
A decision-maker's risk preference is inherently unstable and may adjust in response to external information, shaping subsequent choices and their outcomes. This paper develops a persuasion framework to study how information can be designed to steer risk preferences and decision results. In our framework, a receiver starts with an initial risk preference represented by a coherent risk measure and revises it after observing a system state generated by an information rule claimed by a sender. The revision must preserve time consistency of risk evaluations before and after the state realization. We investigate two models under this framework: one focuses purely on how receiver's risk preference is influenced by information, the other also incorporates receiver's decision. For both models, we investigate the sender's optimal information design problem by analyzing the induced distribution of posterior beliefs over states. Each belief leads to specific preference revisions and corresponding conditional risk assessments. We identify conditions under which information design benefits the sender across several settings and illustrate the framework's potential in risk management through an application to reinsurance design.
