Bayesian Preference Elicitation for Decision Support in Multi‐Objective Optimization
Felix Huber, Sebastian Rojas Gonzalez, Raul Astudillo · Journal of Multi-Criteria Decision Analysis · 2025
ABSTRACT We present a novel approach to help decision‐makers efficiently identify preferred solutions from the Pareto set of a multi‐objective optimization problem. Our method uses a Bayesian model to estimate the decision‐maker's utility function based on pairwise comparisons. Aided by this model, a principled elicitation strategy selects queries interactively to balance exploration and exploitation, guiding the discovery of high‐utility solutions. The approach is flexible: it can be used interactively or a posteriori after estimating the Pareto front through standard multi‐objective optimization techniques. Additionally, at the end of the elicitation phase, it generates a reduced menu of high‐quality solutions, simplifying the decision‐making process. Through experiments on test problems with up to nine objectives, our method demonstrates superior performance in finding high‐utility solutions with a small number of queries. We also provide an open‐source implementation of our method to support its adoption by the broader community.