Conference item
A Hotelling-Downs framework for party nominees
- Abstract:
- We present a model for the strategic selection of party nominees, where competing groups choose their representatives based on the expected electoral returns. Technically, we look at a generalisation of the Hotelling-Downs model, where each nominee has a predefined position on the political spectrum and attracts the closest voters compared to all other representatives. Within this framework we explore the algorithmic properties of Nash equilibria, which are not guaranteed to exist even in two party competitions. We show that finding a Nash equilibrium is NP-complete for the general case. However, if there are only two competing parties, this can be achieved in linear time. The results readily extend to games with restricted positioning options for the players involved, such as facility location and Voronoi games.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, 740.6KB, Terms of use)
-
- Publisher copy:
- 10.5555/3463952.3464025
- Publication website:
- http://www.ifaamas.org/Proceedings/aamas2021/forms/index.htm
Authors
- Publisher:
- Association for Computing Machinery
- Host title:
- Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021)
- Pages:
- 593-601
- Publication date:
- 2021-05-03
- Acceptance date:
- 2020-12-17
- Event title:
- 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 20201)
- Event location:
- Virtual event
- Event website:
- https://aamas2021.soton.ac.uk/
- Event start date:
- 2021-05-03
- Event end date:
- 2021-05-07
- DOI:
- ISSN:
-
2523-5699
- ISBN:
- 9781450383073
- Language:
-
English
- Keywords:
- Pubs id:
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1168870
- Local pid:
-
pubs:1168870
- Deposit date:
-
2021-03-22
Terms of use
- Copyright holder:
- International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
- Copyright date:
- 2021
- Rights statement:
- Copyright © 2021 by International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).
- Notes:
- This paper was presented at the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 20201), 3-7 May 2021, Virtual event. This is the accepted manuscript version of the paper. The final version is available online from the Association for Computing Machinery at: https://dl.acm.org/doi/proceedings/10.5555/3463952
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