一句话：研究MDP中可达与平均收益目标下CVaR及其与期望、VaR约束组合的决策复杂度与策略结构。
论文将条件风险价值（CVaR）引入马尔可夫链和马尔可夫决策过程，目标涵盖可达性与平均收益。CVaR通过最差p分位的期望来量化风险，可用于设计风险规避系统。作者不仅考虑CVaR约束，还引入其与期望约束、分位数约束（风险价值VaR）的合取。针对相应决策问题，论文给出了计算复杂度的下界与上界，并从记忆和随机化两个维度刻画了最优策略的结构。该工作为概率系统中的风险感知验证与控制器综合提供了理论复杂度边界和策略可实现性认识。
We present the conditional value-at-risk (CVaR) in the context of Markov chains and Markov decision processes with reachability and mean-payoff objectives. CVaR quantifies risk by means of the expectation of the worst p-quantile. As such it can be used to design risk-averse systems. We consider not only CVaR constraints, but also introduce their conjunction with expectation constraints and quantile constraints (value-at-risk, VaR). We derive lower and upper bounds on the computational complexity of the respective decision problems and characterize the structure of the strategies in terms of memory and randomization.