Tag
This paper evaluates four machine learning models for discrete choice modeling in policy preference elicitation, using Monte Carlo experiments and a real energy policy case study to assess performance under individual heterogeneity and choice complexity.
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.
Proposes reframing Pairwise Ranking Prompting (PRP) reranking as active learning from noisy pairwise comparisons, improving NDCG@10 per call under budget constraints, and introduces a randomized-direction oracle that reduces LLM calls per pair.