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Optimal Statistical Hypothesis Testing for Social Choice
June 19, 2020 · Declared Dead · 🏛 Conference on Uncertainty in Artificial Intelligence
"No code URL or promise found in abstract"
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Authors
Lirong Xia
arXiv ID
2006.11362
Category
math.ST
Cross-listed
cs.AI,
cs.GT
Citations
1
Venue
Conference on Uncertainty in Artificial Intelligence
Last Checked
3 months ago
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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