人类专业知识的价值
The Value of Human Expertise
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中文总结 AI 辅助
该研究针对带未知参数的优化问题，提出利用人类领域知识的策略评估方法，证明人类专业知识的价值等于极大极小间隙，并在 assortment 优化和最短路径问题中验证了方法。
中文摘要 AI 辅助
我们考虑带有未知参数的优化应用，其中决策者认为名义问题（若已知真实参数时本应求解的优化问题）的最优值不太可能很大。这一信念源于人类拥有的、未被数据集捕获的信息，这些信息来自领域知识及与物理世界的交互。我们提出一种策略评估方法，若决策者的信念正确，该方法能提供更严格的性能保证。我们的主要结果表明，若计算策略的最坏情况性能是一个凸规划，则人类专业知识的价值——即从对名义问题的信念中可获得的性能保证的最大提升——等于一个极大极小问题的极小极大间隙。我们在 assortment optimization（ assortment 优化）和 shortest path problems（最短路径问题）中说明了我们的研究进展。
英文摘要
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.
