Linear Social Choice with Few Queries: A Moment-Based Approach
Luise Ge, Daniel Halpern, Gregory Kehne, Yevgeniy Vorobeychik

TL;DR
This paper introduces a moment-based approach to linear social choice under extreme communication constraints, showing that a small number of pairwise or graded comparisons per voter can recover voter distribution moments for diverse social choice objectives.
Contribution
It demonstrates that only two comparisons per voter are needed to identify the entire voter-type distribution in a linear social choice model, enabling complex social choice objectives.
Findings
One comparison per voter suffices for social welfare maximization.
Two comparisons per voter identify the second moment of voter types.
Richer queries enable full recovery of voter distribution moments.
Abstract
Most social choice rules assume access to full rankings, while current alignment practice -- despite aiming for diversity -- typically treats voters as anonymous and comparisons as independent, effectively extracting only about one bit per voter. Motivated by this gap, we study social choice under an extreme communication budget in the linear social choice model, where each voter's utility is the inner product between a latent voter type and the embedding of the context and candidate. The candidate and voter spaces may be very large or even infinite. Our core idea is to model the electorate as an unknown distribution over voter types and to recover its moments as informative summary statistics for candidate selection. We show that one pairwise comparison per voter already suffices to select a candidate that maximizes social welfare, but this elicitation cannot identify the second moment…
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Taxonomy
TopicsGame Theory and Voting Systems · Mobile Crowdsensing and Crowdsourcing · Complexity and Algorithms in Graphs
