TY - GEN
T1 - Achieving fully proportional representation by clustering voters
AU - Faliszewski, Piotr
AU - Slinko, Arkadii
AU - Stahl, Kolja
AU - Talmon, Nimrod
N1 - Funding Information:
We are grateful to the reviewers for their comments. Piotr Faliszewski was supported by NCN grant DEC-2012/06/M/ST1/00358. Arkadii Slinko was supported by the Royal Society of NZ Marsden Fund UOA-254. Kolja Stahl was supported by DFG project PAWS (NI 369/10). Nimrod Talmon was supported by the DFG Research Training Group MDS (GRK 1408).
Publisher Copyright:
Copyright © 2016, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
PY - 2016/1/1
Y1 - 2016/1/1
N2 - Both the Chamberlin-Courant and Monroe rules are voting rules solving the problem of so-called fully proportional representation: they select committees whose members represent the voters so that voters' satisfaction with their assigned representatives is maximized. These rules suffer from a common disadvantage, being that it is computationally intractable to compute the winning committee exactly. As both of these rules, explicitly or implicitly, partition voters, they can be seen as clustering the voters so that the voters in each group share the same representative. This suggests studying approximation algorithms for these voting rules by means of cluster analysis, which is the subject of this paper. We develop several algorithms based on clustering the voters and analyze their performance experimentally.
AB - Both the Chamberlin-Courant and Monroe rules are voting rules solving the problem of so-called fully proportional representation: they select committees whose members represent the voters so that voters' satisfaction with their assigned representatives is maximized. These rules suffer from a common disadvantage, being that it is computationally intractable to compute the winning committee exactly. As both of these rules, explicitly or implicitly, partition voters, they can be seen as clustering the voters so that the voters in each group share the same representative. This suggests studying approximation algorithms for these voting rules by means of cluster analysis, which is the subject of this paper. We develop several algorithms based on clustering the voters and analyze their performance experimentally.
KW - Clustering
KW - Multiwinner elections
KW - Voting
UR - http://www.scopus.com/inward/record.url?scp=85014163655&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85014163655
T3 - Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
SP - 296
EP - 304
BT - AAMAS 2016 - Proceedings of the 2016 International Conference on Autonomous Agents and Multiagent Systems
PB - International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
T2 - 15th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2016
Y2 - 9 May 2016 through 13 May 2016
ER -