期刊论文详细信息
PLOS ONE 卷:21
A computational model of spatial politics: Hotelling-downs model as statistical physics
Research Article
Christopher Campbell1  Graeme J. Ackland1 
[1] School of Physics and Astronomy, University of Edinburgh, Edinburgh, Scotland, United Kingdom
关键词: Polarized electromagnetic radiation;    Elections;    Statistical mechanics;    Political parties;    Governments;    System stability;    Monte Carlo method;    Political theory;   
DOI  :  10.1371/journal.pone.0352242
 received in 2025-09-29, accepted in 2026-06-08,  发布年份 2026
来源: PLOS
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【 摘 要 】

The Hotelling-Downs model considers parties changing policy to maximise their vote-share. Where policy position lies on a left-right axis, it describes a tendency for political parties to move towards centrist platforms. This is in contrast with widely observed political polarisation. We extend the model to two dimensions, with many parties and with single and multiple-peaked voter distribution. We find that a two party system reduces polarisation, even if voters are polarised with a bimodal distribution. By contrast, multiparty systems induce polarisation, even when most voters favour moderate position. We model the effect of turnout and activists as influences on the parties, showing that this results in more polarisation, even in a two-party system. This suggests that polarisation of parties can be driven by abstention, intra-party politics and turnout on the extremes. In the two-party case, the winning party’s positions are more moderate than the views of their supporters but better representative of the electorate as a whole. With polarisation, individual voters are better able to find a party which represents their views, but the government (winning part or coalition) is less representative of the population, even when the population has a clear consensus on all issues.

【 授权许可】

CC BY   
Campbell, Ackland 2026

【 预 览 】
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RO202609300003947ZK.pdf 1455KB PDF download
Supporting File 2 6972KB Other download
RO202609300000919ZK.pdf 2141KB PDF download
Fig. 4 266KB Image download
Fig. 5 687KB Image download
MediaObjects/13756_2026_1821_MOESM1_ESM.pdf 95KB PDF download
Fig. 2 365KB Image download
Fig. 6 321KB Image download
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