The Value of Human Expertise
2026-08-26 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors study situations where someone needs to make the best choice but doesn't know some important details exactly. They focus on cases where the person believes the best possible outcome wouldn't be very high, based on their experience or knowledge outside of data. They suggest a new way to check how good a decision method is, which gives stronger confidence if that belief is right. Their main finding links the value of this human insight to a specific mathematical problem, and they show examples in choosing items to sell and finding shortest paths.
optimizationnominal problemperformance guaranteesconvex programmingminimax problemworst-case analysisassortment optimizationshortest path problemhuman expertisedecision making under uncertainty
Authors
Bradley Sturt
Abstract
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.