On the Distortion of Committee Election with 1-Euclidean Preferences and Few Distance Queries

TL;DR

This paper studies the limits of committee election algorithms based on ordinal preferences and few distance queries, revealing bounds on distortion for different committee sizes and query complexities.

Contribution

It introduces bounds on the distortion achievable with limited distance queries in committee elections with Euclidean preferences.

Findings

Bounded distortion for k=2 without distance queries.

Distortion of 3 for m=3 when k=2.

Deterministic algorithms with O(n) distortion using O(k) distance queries.

Abstract

We consider committee election of $k \geq 2$ (out of $m \geq k+1$) candidates, where the voters and the candidates are associated with locations on the real line. Each voter's cardinal preferences over candidates correspond to her distance to the candidate locations, and each voter's cardinal preferences over committees is defined as her distance to the nearest candidate elected in the committee. We consider a setting where the true distances and the locations are unknown. We can nevertheless have access to degraded information which consists of an order of candidates for each voter. We investigate the best possible distortion (a worst-case performance criterion) wrt. the social cost achieved by deterministic committee election rules based on ordinal preferences submitted by $n$ voters and few additional distance queries. For $k = 2$, we achieve bounded distortion without any distance queries; we show that the distortion is $3$ for $m = 3$, and that the best possible distortion achieved by deterministic algorithms is at least $n-1$ and at most $n+1$, for any $m \geq 4$. For any $k \geq 3$, we show that the best possible distortion of any deterministic algorithm that uses at most $k-3$ distance queries cannot be bounded by any function of $n$, $m$ and $k$. We present deterministic algorithms for $k$-committee election with distortion of $O(n)$ with $O(k)$ distance queries and $O(1)$ with $O(k \log n)$ distance queries.