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Introduces a stratified framework to identify the minimal aggregated preference information needed to compute disagreement measures, proposing the plurality matrix and showing that pairwise comparisons are insufficient; designs elicitation protocols that trade off participant numbers and cognitive load.
This paper introduces a new embedding model designed to capture preferential similarity rather than just semantic similarity, improving preference prediction for collective decision-making systems.