Conference item
Individual-based stability in hedonic diversity games
- Abstract:
- In hedonic diversity games (HDGs), recently introduced by Bredereck, Elkind, and Igarashi (2019), each agent belongs to one of two classes (men and women, vegetarians and meat-eaters, junior and senior researchers), and agents' preferences over coalitions are determined by the fraction of agents from their class in each coalition. Bredereck et al. show that while an HDG may fail to have a Nash stable (NS) or a core stable (CS) outcome, every HDG in which all agents have single-peaked preferences admits an individually stable (IS) outcome, which can be computed in polynomial time. In this work, we extend and strengthen these results in several ways. First, we establish that the problem of deciding if an HDG has an NS outcome is NP-complete, but admits an XP algorithm with respect to the size of the smaller class. Second, we show that, in fact, all HDGs admit IS outcomes that can be computed in polynomial time; our algorithm for finding such outcomes is considerably simpler than that of Bredereck et al. We also consider two ways of generalizing the model of Bredereck et al. to k ≥ 2 classes. We complement our theoretical results by empirical analysis, comparing the IS outcomes found by our algorithm, the algorithm of Bredereck et al. and a natural better-response dynamics.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, 295.4KB, Terms of use)
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- Publisher copy:
- 10.1609/aaai.v34i02.5549
Authors
- Publisher:
- AAAI Press
- Host title:
- Proceedings of the AAAI Conference on Artificial Intelligence
- Volume:
- 34
- Issue:
- 2
- Pages:
- 1822-1829
- Publication date:
- 2020-04-03
- Acceptance date:
- 2019-11-10
- Event title:
- Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020)
- Event location:
- New York, USA
- Event website:
- https://aaai.org/Conferences/AAAI-20/
- Event start date:
- 2020-02-07
- Event end date:
- 2020-02-12
- DOI:
- ISBN:
- 9781577358237
- Language:
-
English
- Keywords:
- Pubs id:
-
1112735
- Local pid:
-
pubs:1112735
- Deposit date:
-
2021-05-03
Terms of use
- Copyright holder:
- Association for the Advancement of Artificial Intelligence
- Copyright date:
- 2020
- Rights statement:
- © 2020 Association for the Advancement of Artificial Intelligence.
- Notes:
- This conference paper was presented at the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020), 7-12 February 2020, New York, USA. This is the accepted manuscript version of the paper. The final version is available online from the Association for the Advancement of Artificial Intelligence at: https://doi.org/10.1609/aaai.v34i02.5549
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