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Lackner, M., & Skowron, P. (2020). Utilitarian welfare and representation guarantees of approval-based multiwinner rules. Artificial Intelligence, 288(103366), 103366. https://doi.org/10.1016/j.artint.2020.103366
E192-02 - Forschungsbereich Databases and Artificial Intelligence
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Journal:
Artificial Intelligence
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ISSN:
0004-3702
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Date (published):
2020
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Number of Pages:
47
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Peer reviewed:
Yes
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Keywords:
Artificial Intelligence; Linguistics and Language; Language and Linguistics
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Abstract:
To choose a suitable multiwinner voting rule is a hard and ambiguous task. Depending on the context, it varies widely what constitutes the choice of an ''optimal" subset of alternatives. In this paper, we provide a quantitative analysis of multiwinner voting rules using methods from the theory of approximation algorithms---we estimate how well multiwinner rules approximate two extreme objectives: ...
To choose a suitable multiwinner voting rule is a hard and ambiguous task. Depending on the context, it varies widely what constitutes the choice of an ''optimal" subset of alternatives. In this paper, we provide a quantitative analysis of multiwinner voting rules using methods from the theory of approximation algorithms---we estimate how well multiwinner rules approximate two extreme objectives: a representation criterion defined via the Approval Chamberlin--Courant rule and a utilitarian criterion defined via Multiwinner Approval Voting. With both theoretical and experimental methods, we classify multiwinner rules in terms of their quantitative alignment with these two opposing objectives. Our results provide fundamental information about the nature of multiwinner rules and, in particular, about the necessary tradeoffs when choosing such a rule.
