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arXiv · 2507.16999

Bayesian preference elicitation for decision support in multiobjective optimization

Felix Huber·Sebastian Rojas Gonzalez·Raul Astudillo
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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.

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Felix Huber, Sebastian Rojas Gonzalez, Raul Astudillo. 2025-07-22. Bayesian preference elicitation for decision support in multiobjective optimization. https://doi.org/10.1002/mcda.70019

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