An Analysis Framework for Metric Voting based on LP Duality
David Kempe

TL;DR
This paper introduces a unified LP-duality based framework for analyzing the distortion of voting rules in metric spaces, providing new bounds and insights into existing and proposed rules.
Contribution
It presents a simplified, unified method for proving distortion bounds, extends bounds to new rules, and proposes a candidate rule for optimal distortion.
Findings
Copeland rule distortion at most 5, generalized to rules with short paths
Ranked Pairs and Schulze rules have distortion Θ(√n)
Proposes a new rule potentially achieving distortion 3
Abstract
Distortion-based analysis has established itself as a fruitful framework for comparing voting mechanisms. m voters and n candidates are jointly embedded in an (unknown) metric space, and the voters submit rankings of candidates by non-decreasing distance from themselves. Based on the submitted rankings, the social choice rule chooses a winning candidate; the quality of the winner is the sum of the (unknown) distances to the voters. The rule's choice will in general be suboptimal, and the worst-case ratio between the cost of its chosen candidate and the optimal candidate is called the rule's distortion. It was shown in prior work that every deterministic rule has distortion at least 3, while the Copeland rule and related rules guarantee worst-case distortion at most 5; a very recent result gave a rule with distortion . We provide a framework based on…
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