The Metric Distortion of Multiwinner Voting

Abstract

We extend the recently introduced framework of metric distortion to multiwinner voting. In this framework, n agents and m alternatives are located in an underlying metric space. e exact distances between agents and alternatives are unknown. Instead, each agent provides a ranking of the alternatives, ordered from the closest to the farthest. Typically, the goal is to select a single alternative that approximately minimizes the total distance from the agents, and the worst-case approximation ratio is termed distortion. In the case of multiwinner voting, the goal is to select a commi ee of k alternatives that (approximately) minimizes the total cost to all agents. We consider the scenario where the cost of an agent for a commi ee is her distance from the q-th closest alternative in the commi ee. We reveal a surprising trichotomy on the distortion of multiwinner voting rules in terms of k and q: e distortion is unbounded when q k/3, asymptotically linear in the number of agents when k/3 < q k/2, and constant when q > k/2.