Conference item
Algorithms for swap and shift bribery in structured elections
- Abstract:
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In computational social choice, shift bribery is the procedure of paying voters to shift the briber's preferred candidate forward in their preferences so as to make this candidate an election winner; the more general swap bribery procedure also allows one to pay voters to swap other candidates in their preferences. The complexity of swap and shift bribery is well-understood for many voting rules; typically, finding a minimum-cost bribery is computationally hard. In this paper we initiate the ...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Version of record, 1.2MB)
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- Publication website:
- https://dl.acm.org/doi/abs/10.5555/3398761.3398808
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Bibliographic Details
- Publisher:
- International Foundation for Autonomous Agents and Multiagent Systems Publisher's website
- Host title:
- AAMAS '20: Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems
- Pages:
- 366-374
- Publication date:
- 2020-01-01
- Event title:
- 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020)
- Event location:
- Auckland, New Zealand
- Event website:
- https://aamas2020.conference.auckland.ac.nz/
- Event start date:
- 2020-05-09
- Event end date:
- 2020-05-13
- EISSN:
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1558-2914
- ISSN:
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1548-8403
- ISBN:
- 9781450375184
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1148904
- Local pid:
- pubs:1148904
- Deposit date:
- 2021-04-19
Terms of use
- Copyright holder:
- International Foundation for Autonomous Agents and Multiagent Systems
- Copyright date:
- 2020
- Rights statement:
- © 2020 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
- Notes:
- This paper was presented at the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020), 9–13 May 2020, Auckland, New Zealand. This is the publisher's version of the paper. The final version is available online from the International Foundation for Autonomous Agents and Multiagent Systems at: https://dl.acm.org/doi/abs/10.5555/3398761.3398808
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