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Bayesian Persuasion with a Risk-Conscious Receiver

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  • Yujing Chen

Abstract

We study Bayesian persuasion when the receiver evaluates actions by reward-side Conditional Value-at-Risk (CVaR) rather than expected utility. CVaR preferences break the standard action-based direct-recommendation reduction: merging signals that recommend the same action can change the receiver's tail-risk ranking and destroy incentive compatibility. We show that this failure does not imply intractability in the explicit finite-state model. Each CVaR action value is max-affine in the posterior, and refining recommendations by the active affine piece yields an active-facet revelation principle and an exact polynomial-size linear program. We further identify a representation boundary: listed polyhedral risks remain tractable by the same LP, whereas succinctly represented facet families make exact persuasion NP-hard. Finally, we give a finite-precision approximation scheme for risk preferences determined by finitely many stable posterior statistics.

Suggested Citation

  • Yujing Chen, 2026. "Bayesian Persuasion with a Risk-Conscious Receiver," Papers 2605.12094, arXiv.org.
    • Handle: RePEc:arx:papers:2605.12094
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    References listed on IDEAS

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