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Optimal Statistical Hypothesis Testing for Social Choice

Conference Paper Accepted Paper Artificial Intelligence · Machine Learning · Uncertainty in Artificial Intelligence

Abstract

We address the following question in this paper: “What are the most robust statistical methods for social choice? ” By leveraging the theory of uniformly least favorable distributions in the Neyman-Pearson framework to finite models and randomized tests, we characterize uniformly most powerful (UMP) tests, which is a well-accepted statistical optimality w. r. t. robustness, for testing whether a given alternative is the winner under Mallows’ model and under Condorcet’s model, respectively.

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Context

Venue
Conference on Uncertainty in Artificial Intelligence
Archive span
1985-2025
Indexed papers
3717
Paper id
82990880435868190
v2026.09.13