Talks and Poster Presentations (with Proceedings-Entry):

M. Lackner, P. Skowron:
"A Quantitative Analysis of Multi-Winner Rules";
Talk: IJCAI 2019 - 28th International Joint Conference on Artificial Intelligence, Macao, China; 2019-08-10 - 2019-08-16; in: "Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, {IJCAI} 2019, Macao, China, August 10-16, 2019", ijcai.org, (2019), ISBN: 978-0-9992411-4-1; 407 - 413.

English abstract:
To choose a suitable multi-winner voting rule is a hard and ambiguous task. Depending on the context, it varies widely what constitutes the choice of an "optimal" subset. In this paper, we offer a new perspective on measuring the quality of such subsets and-consequently-of multi-winner rules. We provide a quantitative analysis using
methods from the theory of approximation algorithms and estimate how well multi-winner rules
approximate two extreme objectives: diversity as
captured by the Approval Chamberlin-Courant rule
and individual excellence as captured by Multiwinner
Approval Voting. With both theoretical and
experimental methods we classify multi-winner
rules in terms of their quantitative alignment with
these two opposing objectives.

Agent-based and Multi-agent Systems: Computational Social Choice , Voting

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Related Projects:
Project Head Reinhard Pichler:

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