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On the Complexity of Candidates-Embedded Multiwinner Voting under the Hausdorff Function

Published: 06 May 2024 Publication History

Abstract

We study candidates-embedded approval-based multiwinner voting. In this model, we are given a metric ƒ on the set of candidates, and voters are free to approve or disapprove any candidates. The task is to select a k-committee that either minimizes the sum of distances from the committee to all votes (utilitarian rules) or minimizes the maximum distance from the committee to any vote (egalitarian rules). The distance from a committee to a vote is measured by certain set-to-set functions derived from ƒ. Previous works have considered the min, the max, and the sum functions. This paper examines the Hausdorff function. We show that in general computing winners under the Hausdorff function is hard, but we also derive several polynomial-time algorithms for certain special cases.

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cover image ACM Conferences
AAMAS '24: Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems
May 2024
2898 pages
ISBN:9798400704864

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International Foundation for Autonomous Agents and Multiagent Systems

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Published: 06 May 2024

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  1. hausdorff distance
  2. multiwinner voting
  3. np-hardness, parameterized complexity

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