Bayesian Risk Preference Persuasion

A decision-maker's risk preference is inherently unstable and may adjust in response to external information, shaping subsequent choices and outcomes.

This paper develops a persuasion framework to study how information can be designed to steer risk preferences and decision results.

In our model, 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 characterize the sender's optimal information design 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.