Abstract
We study the (parameterized) complexity of SHIFT BRIBERY for multiwinner voting rules. We focus on the SNTV, Bloc, k-Borda, and Chamberlin-Courant rules, as well as on approximate variants of the Chamberlin-Courant rule, since the original rule is NP-hard to compute. We show that SHIFT BRIBERY tends to be significantly harder in the multiwinner setting than in the single-winner one by showing settings where SHIFT BRIBERY is easy in the single-winner cases, but is hard (and hard to approximate) in the multiwinner ones. We show that the non-monotonicity of those rules which are based on approximation algorithms for the Chamberlin-Courant rule sometimes affects the complexity of SHIFT BRIBERY.
| Original language | American English |
|---|---|
| Title of host publication | 30th AAAI Conference on Artificial Intelligence, AAAI 2016 |
| Pages | 2452-2458 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781577357605 |
| State | Published - 1 Jan 2016 |
| Externally published | Yes |
| Event | 30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States Duration: 12 Feb 2016 → 17 Feb 2016 |
Publication series
| Name | 30th AAAI Conference on Artificial Intelligence, AAAI 2016 |
|---|
Conference
| Conference | 30th AAAI Conference on Artificial Intelligence, AAAI 2016 |
|---|---|
| Country/Territory | United States |
| City | Phoenix |
| Period | 12/02/16 → 17/02/16 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 16 Peace, Justice and Strong Institutions
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
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