这篇论文提出了评估策略的新方法，对于决策者信念正确的情况，能提供更紧密的性能保证，值得一读。
研究优化应用，考虑未知参数，提出评估策略的方法，若决策者的信念正确，则提供更紧密的性能保证。主要结果显示，若计算策略的最坏情况性能是凸规划，则人类专业知识的价值等于最大-最小问题的极小极大差距。以组合优化和最短路径问题为例说明发展。
The Value of Human Expertise
We consider optimization applications with unknown parameters where the decision maker believes that the optimal value of the nominal problem-the optimization problem they would have solved if the true parameters were known-is unlikely to be large. This belief derives from information that humans have that is not captured in datasets, obtained from domain knowledge and interacting with the physical world. We propose an approach to evaluating policies that provides tighter performance guarantees if the decision maker's belief happens to be correct. Our main result shows that if computing a policy's worst-case performance is a convex program, then the value of human expertise-the maximum improvement in performance guarantees that can be obtained from the belief about the nominal problem-is equal to the minimax gap of a max-min problem. We illustrate our developments in assortment optimization and shortest path problems.