Vorträge und Posterpräsentationen (mit Tagungsband-Eintrag):
M. Lackner, P. Skowron:
"A Quantitative Analysis of Multi-Winner Rules";
Vortrag: IJCAI 2019 - 28th International Joint Conference on Artificial Intelligence,
Macao, China;
10.08.2019
- 16.08.2019; 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;
S. 407
- 413.
Kurzfassung englisch:
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.
Schlagworte:
Agent-based and Multi-agent Systems: Computational Social Choice , Voting
"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)
http://dx.doi.org/10.24963/ijcai.2019
Zugeordnete Projekte:
Projektleitung Reinhard Pichler:
SuDeMa
Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.