A Preference Elicitation Approach for the Ordered Weighted Averaging Criterion using Solution Choice Observations
Werner Baak, Marc Goerigk, Michael Hartisch

TL;DR
This paper presents an optimization-based, data-driven method for eliciting preferences in the ordered weighted averaging (OWA) criterion using passive solution choice observations, effectively capturing decision maker preferences under uncertainty.
Contribution
It introduces a novel passive preference elicitation approach for OWA weights using observed choices, avoiding explicit comparisons and handling inconsistencies.
Findings
Method performs well with inconsistent choices.
Outperforms pairwise comparison methods.
Effective in risk-averse decision scenarios.
Abstract
Decisions under uncertainty or with multiple objectives usually require the decision maker to formulate a preference regarding risks or trade-offs. If this preference is known, the ordered weighted averaging (OWA) criterion can be applied to aggregate scenarios or objectives into a single function. Formulating this preference, however, can be challenging, as we need to make explicit what is usually only implicit knowledge. We explore an optimization-based method of preference elicitation to identify appropriate OWA weights. We follow a data-driven approach, assuming the existence of observations, where the decision maker has chosen the preferred solution, but otherwise remains passive during the elicitation process. We then use these observations to determine the underlying preference by finding the preference vector that is at minimum distance to the polyhedra of feasible vectors for…
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Taxonomy
TopicsMulti-Criteria Decision Making · Optimization and Mathematical Programming · Supply Chain and Inventory Management
