Communication, Distortion, and Randomness in Metric Voting

TL;DR

This paper investigates the tradeoff between communication complexity and approximation quality in metric voting rules, providing bounds and mechanisms for deterministic and randomized voting under limited information.

Contribution

It establishes tight bounds on distortion for communication-bounded voting mechanisms and introduces a simple randomized rule that nearly matches the lower bound.

Findings

Deterministic voting with limited communication has a lower bound of 2n/k, matching an upper bound.

Randomized mechanisms with limited bits per voter have a lower bound of 2n/b on distortion.

A simple randomized rule achieves expected distortion close to the theoretical lower bound of 3.

Abstract

In distortion-based analysis of social choice rules over metric spaces, one assumes that all voters and candidates are jointly embedded in a common metric space. Voters rank candidates by non-decreasing distance. The mechanism, receiving only this ordinal (comparison) information, should select a candidate approximately minimizing the sum of distances from all voters. It is known that while the Copeland rule and related rules guarantee distortion at most 5, many other standard voting rules, such as Plurality, Veto, or $k$-approval, have distortion growing unboundedly in the number $n$ of candidates. Plurality, Veto, or $k$-approval with small $k$ require less communication from the voters than all deterministic social choice rules known to achieve constant distortion. This motivates our study of the tradeoff between the distortion and the amount of communication in deterministic social choice rules. We show that any one-round deterministic voting mechanism in which each voter communicates only the candidates she ranks in a given set of $k$ positions must have distortion at least $\frac{2n-k}{k}$; we give a mechanism achieving an upper bound of $O(n/k)$, which matches the lower bound up to a constant. For more general communication-bounded voting mechanisms, in which each voter communicates $b$ bits of information about her ranking, we show a slightly weaker lower bound of $\Omega(n/b)$ on the distortion. For randomized mechanisms, it is known that Random Dictatorship achieves expected distortion strictly smaller than 3, almost matching a lower bound of $3-\frac{2}{n}$ for any randomized mechanism that only receives each voter's top choice. We close this gap, by giving a simple randomized social choice rule which only uses each voter's first choice, and achieves expected distortion $3-\frac{2}{n}$.