基于优化确定性等价风险控制的保角风险厌恶决策
Conformal Risk-Averse Decision Making with Optimized Certainty Equivalent Risk Control
浏览论文内容
中文总结 AI 辅助
该研究针对智能体在系统状态不确定时的风险厌恶决策问题，提出基于优化确定性等价（OCE）的风险控制策略，将已知分布下的最优策略简化为CVaR的预测集解，并通过数据驱动校准策略在无线波束成形场景中验证了其OCE风险高概率控制效果。
中文摘要 AI 辅助
我们研究风险厌恶型决策问题，其中智能体在对真实系统状态不确定的情况下选择行动。风险通过优化确定性等价（OCE）度量来衡量，该度量概括了均值-方差风险和条件风险价值（CVaR）等流行准则。我们刻画了已知分布下的最优策略，表明其可简化为基于预测集的CVaR解决方案，这为保角预测型预测集提供了可操作的解释。对于未知分布，我们开发了一种基于数据的校准策略，该策略基于似然的合成模型和预留校准数据，可实现OCE风险的高概率控制。该方法在两种无线波束成形场景中进行了评估。
英文摘要
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.
