Bayesian Risk Preference Persuasion
Abstract
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.
Related
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BibTeX
@misc{liu2026bayesian,
title = {{Bayesian Risk Preference Persuasion}},
author = {Shutian Liu},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2607.13810},
eprint = {2607.13810},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.13810},
}
RIS
TY - UNPB
TI - Bayesian Risk Preference Persuasion
AU - Shutian Liu
PY - 2026
DA - 2026/09/21
PB - arXiv
DO - 10.48550/arxiv.2607.13810
UR - https://arxiv.org/abs/2607.13810
AB - 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.
ER -