Abstract
In multiwinner approval voting, the goal is to select kmember committees based on voters' approval ballots. A well-studied concept of proportionality in this context is the justified representation (JR) axiom, which demands that no large cohesive group of voters remains unrepresented. However, the JR axiom may conflict with other desiderata, such as coverage (maximizing the number of voters who approve at least one committee member) or social welfare (maximizing the number of approvals obtained by committee members). In this work, we investigate the impact of imposing the JR axiom (as well as the more demanding EJR axiom) on social welfare and coverage. Our approach is threefold: we derive worst-case bounds on the loss of welfare/coverage that is caused by imposing JR, study the computational complexity of finding 'good' committees that provide JR (obtaining a hardness result, an approximation algorithm, and an exact algorithm for one-dimensional preferences), and examine this setting empirically on several synthetic datasets.
| Original language | English (US) |
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| Title of host publication | AAAI-22 Technical Tracks 5 |
| Publisher | Association for the Advancement of Artificial Intelligence |
| Pages | 4983-4990 |
| Number of pages | 8 |
| ISBN (Electronic) | 1577358767, 9781577358763 |
| DOIs | |
| State | Published - Jun 30 2022 |
| Event | 36th AAAI Conference on Artificial Intelligence, AAAI 2022 - Virtual, Online Duration: Feb 22 2022 → Mar 1 2022 |
Publication series
| Name | Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022 |
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| Volume | 36 |
Conference
| Conference | 36th AAAI Conference on Artificial Intelligence, AAAI 2022 |
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| City | Virtual, Online |
| Period | 2/22/22 → 3/1/22 |
Funding
This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 101002854), from the Deutsche Forschungsgemeinschaft under grant BR 4744/2-1, from JST PRESTO under grant number JPMJPR20C1, and from an NUS Start-up Grant. We would like to thank the anonymous reviewers for their valuable comments. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 101002854), from the Deutsche Forschungsge-meinschaft under grant BR 4744/2-1, from JST PRESTO under grant number JPMJPR20C1, and from an NUS Start-up Grant. We would like to thank the anonymous reviewers for their valuable comments.
ASJC Scopus subject areas
- Artificial Intelligence