# The Value of Human Expertise

arXiv: https://arxiv.org/abs/2608.26051 · HTML: https://mlsanity.com/paper/2608.26051/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 50 references that are in the map.
- First posted on arXiv 26 Aug 2026.

## Builds on (most cited first, 17 of 17)

- [Inverse Optimization: Theory and Applications](https://mlsanity.com/paper/2109.03920/) (2021)
- [From Data to Decisions: Distributionally Robust Optimization is Optimal](https://mlsanity.com/paper/1704.04118/) (2017)
- [Inverse Optimization with Noisy Data](https://mlsanity.com/paper/1507.03266/) (2015)
- [Data-driven Inverse Optimization with Imperfect Information](https://mlsanity.com/paper/1512.05489/) (2015)
- [A Unified Theory of Robust and Distributionally Robust Optimization via the Primal-Worst-Equals-Dual-Best Principle](https://mlsanity.com/paper/2105.00760/) (2021)
- [Learning Decision-Focused Uncertainty Sets in Robust Optimization](https://mlsanity.com/paper/2305.19225/) (2023)
- [Debiasing In-Sample Policy Performance for Small-Data, Large-Scale Optimization](https://mlsanity.com/paper/2107.12438/) (2021)
- [What Data Enables Optimal Decisions? An Exact Characterization for Linear Optimization](https://mlsanity.com/paper/2505.21692/) (2025)
- [Nash Equilibria, Regularization and Computation in Optimal Transport-Based Distributionally Robust Optimization](https://mlsanity.com/paper/2303.03900/) (2023)
- [Beating the Winner's Curse via Inference-Aware Policy Optimization](https://mlsanity.com/paper/2510.18161/) (2025)
- [The Value of Robust Assortment Optimization Under Ranking-based Choice Models](https://mlsanity.com/paper/2112.05010/) (2021)
- [Data Informativeness in Linear Optimization under Uncertainty](https://mlsanity.com/paper/2602.15365/) (2026)
- [A Nonparametric Approach with Marginals for Modeling Consumer Choice](https://mlsanity.com/paper/2208.06115/) (2022)
- [Randomized Robust Price Optimization](https://mlsanity.com/paper/2306.09659/) (2023)
- [The Best of Many Robustness Criteria in Decision Making: Formulation and Application to Robust Pricing](https://mlsanity.com/paper/2403.12260/) (2024)
- [Fast Revenue Maximization](https://mlsanity.com/paper/2407.07316/) (2024)
- [Diffusion-Robust Optimization over Graphs](https://mlsanity.com/paper/2605.30853/) (2026)

## Abstract (arXiv)

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.