Conference item
What do multiwinner voting rules do? An experiment over the two-dimensional Euclidean domain
- Abstract:
- We visualize aggregate outputs of popular multiwinner voting rules — SNTV, STV, Bloc, k-Borda, Monroe, Chamberlin–Courant, and PAV — for elections generated according to the two-dimensional Euclidean model. We consider three applications of multiwinner voting, namely, parliamentary elections, portfolio/movie selection, and shortlisting, and use our results to understand which of our rules seem to be best suited for each application. In particular, we show that STV (one of the few nontrivial rules used in real high-stake elections) exhibits excellent performance, whereas the Bloc rule (also often used in practice) performs poorly.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, pdf, 1.3MB, Terms of use)
-
- Publisher copy:
- 10.1609/aaai.v31i1.10612
- Publication website:
- https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14924
Authors
- Publisher:
- AAAI Press
- Host title:
- Thirty-First AAAI Conference on Artificial Intelligence
- Pages:
- 494-501
- Publication date:
- 2017-02-10
- Acceptance date:
- 2016-11-11
- Event title:
- Thirty-First AAAI Conference on Artificial Intelligence
- Event location:
- San Francisco, California, USA
- Event website:
- https://www.aaai.org/Conferences/AAAI/aaai17.php
- Event start date:
- 2017-02-04
- Event end date:
- 2017-02-09
- DOI:
- Language:
-
English
- Keywords:
- Pubs id:
-
738752
- Local pid:
-
pubs:738752
- Deposit date:
-
2021-04-17
- ARK identifier:
Terms of use
- Copyright holder:
- Association for the Advancement of Artificial Intelligence
- Copyright date:
- 2017
- Rights statement:
- Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
- Notes:
- This paper was presented at the Thirty-First AAAI Conference on Artificial Intelligence, 4-9 February 2017, San Francisco, California, USA. This is the accepted manuscript version of the paper. The final version is available online from AAAI Press at: https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14924
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