Conformal Inverse Optimization for Adherence-Aware Prescriptive Analytics

When “Optimal” Advice Gets Ignored Algorithms can recommend decisions that look optimal on paper but conflict with how people assess the situation, making those recommendations less likely to be followed. In “Conformal Inverse Optimization for Adherence-Aware Prescriptive Analytics,” Chan, Delage, and Lin address this challenge by using past decisions to learn how people evaluate different options. Because those decisions may reflect diverse preferences, noisy observations, or imperfect models, the authors do not rely on a single estimated preference profile. Instead, they construct an uncertainty set around the inferred parameters and use robust optimization to generate recommendations that perform well both objectively and from the decision maker’s perspective. Their method comes with statistical and performance guarantees and outperforms standard inverse optimization in numerical experiments. In a simulated Toronto food-delivery study, it substantially improves estimated courier adherence, maintaining comparable delivery times, showing how better-aligned recommendations can benefit both users and organizations.