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Decisions under uncertainty or with multiple objectives usually require thedecision maker to formulate a preference regarding risks or trade-offs. If thispreference is known, the ordered weighted averaging (OWA) criterion can beapplied to aggregate scenarios or objectives into a single function.Formulating this preference, however, can be challenging, as we need to makeexplicit what is usually only implicit knowledge. We explore anoptimization-based method of preference elicitation to identify appropriate OWAweights. We follow a data-driven approach, assuming the existence ofobservations, where the decision maker has chosen the preferred solution, butotherwise remains passive during the elicitation process. We then use theseobservations to determine the underlying preference by finding the preferencevector that is at minimum distance to the polyhedra of feasible vectors foreach of the observations. Using our optimization-based model, weights aredetermined by solving an alternating sequence of linear programs and standardOWA problems. Numerical experiments on risk-averse preference vectors forselection, assignment and knapsack problems show that our passive elicitationmethod compares well against having to conduct pairwise comparisons andperforms particularly well when there are inconsistencies in the decisionmaker's choices.

A Preference Elicitation Approach for the Ordered Weighted Averaging Criterion using Solution Choice Observations