Conformal Risk-Averse Decision Making with Optimized Certainty Equivalent Risk Control

2026-08-28

AI Summary

研究在不确定系统状态下如何通过优化确定性等价风险控制进行风险规避决策，提出了一种基于合成模型和校准数据的数据驱动策略来控制OCE风险。

Abstract

We study risk-averse decision making, in which an agent selects actions while being uncertain about the true system state. The risk is measured via optimized certainty equivalent (OCE) metrics, which generalize popular criteria such as mean-variance risk and conditional value-at-risk (CVaR). We characterize the optimal policy under known distributions, and show that it reduces to a prediction set-based solution for the CVaR. This provides an operational interpretation of conformal prediction-type prediction sets. For unknown distributions, we develop a data-driven calibration strategy, based on a synthetic model for the likelihood and held-out calibration data, yielding high-probability control of the OCE risk. The approach is evaluated on two wireless beamforming settings.

Problem

• risk-averse decision making
• optimized certainty equivalent
• conformal prediction

Innovation

• Optimized Certainty Equivalent (OCE)
• Risk-Averse Decision Making
• Conformal Prediction
• Data-Driven Calibration
• High-Probability Control