Conference item
OWA-based extensions of the Chamberlin–Courant rule
- Abstract:
- Given a set of voters V, a set of candidates C, and voters’ preferences over the candidates, multiwinner voting rules output a fixed-size subset of candidates (committee). Under the Chamberlin–Courant multiwinner voting rule, one fixes a scoring vector of length |C|, and each voter’s ‘utility’ for a given committee is defined to be the score that she assigns to her most preferred candidate in that committee; the goal is then to find a committee that maximizes the joint utility of all voters. The joint utility is typically identified either with the sum of all voters’ utilities or with the utility of the least satisfied voter, resulting in, respectively, the utilitarian and the egalitarian variant of the Chamberlin–Courant’s rule. For both of these cases, the problem of computing an optimal committee is NP-hard for general preferences, but becomes polynomial-time solvable if voters’ preferences are single-peaked or single-crossing. In this paper, we propose a family of multiwinner voting rules that are based on the concept of ordered weighted average (OWA) and smoothly interpolate between the egalitarian and the utilitarian variants of the Chamberlin–Courant rule. We show that under moderate constraints on the weight vector we can recover many of the algorithmic results known for the egalitarian and the utilitarian version of Chamberlin–Courant’s rule in this more general setting.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Accepted manuscript, pdf, 340.3KB, Terms of use)
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- Publisher copy:
- 10.1007/978-3-319-23114-3_29
Authors
- Publisher:
- Springer
- Host title:
- Lecture Notes in Computer Science: Algorithmic Decision Theory
- Journal:
- Lecture Notes in Computer Science: Algorithmic Decision Theory More from this journal
- Volume:
- 9346
- Pages:
- 486-502
- Publication date:
- 2015-08-28
- DOI:
- ISSN:
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0302-9743, 1611-3349
- ISBN:
- 9783319231143
- Keywords:
- Pubs id:
-
pubs:575695
- UUID:
-
uuid:22baf976-e250-4ece-9c31-d7b41b36e807
- Local pid:
-
pubs:575695
- Source identifiers:
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575695
- Deposit date:
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2018-07-07
- ARK identifier:
Terms of use
- Copyright holder:
- Springer International Publishing Switzerland
- Copyright date:
- 2015
- Notes:
- © Springer International Publishing Switzerland 2015. This paper was presented at the International Conference on Algorithmic Decision Theory.
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