用优化确定等价风险控制，实现更稳健的决策
Conformal Risk-Averse Decision Making with Optimized Certainty Equivalent Risk Control
- 基于预测集设计风险敏感决策策略
- 在已知分布下最优策略可转为预测集形式
- 适用于无线波束成形等不确定环境决策
我们研究风险规避型决策问题，即代理在系统状态未知的情况下选择行动。风险通过优化确定等价（OCE）度量来衡量，该度量推广了均值-方差风险和条件风险价值（CVaR）等常见准则。在已知分布情况下，我们刻画了最优策略，并发现其对CVaR退化为基于预测集的解，为类似符合预测的预测集提供了操作性解释。对于未知分布的情况，我们提出一种数据驱动的校准策略，基于合成似然模型与保留校准数据，实现了对OCE风险的高概率控制。该方法在两个无线波束成形场景中进行了评估。
原文摘要 · Abstract (English)
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.
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