Preventing Extreme Polarization of Political Attitudes
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Abstract (arXiv)
Extreme polarization can undermine democracy by making compromise impossible and transforming politics into a zero-sum game. Ideological polarization - the extent to which political views are widely dispersed - is already strong among elites, but less so among the general public (McCarty, 2019, p. 50-68). Strong mutual distrust and hostility between Democrats and Republicans in the U.S., combined with the elites' already strong ideological polarization, could lead to increasing ideological polarization among the public. The paper addresses two questions: (1) Is there a level of ideological polarization above which polarization feeds upon itself to become a runaway process? (2) If so, what policy interventions could prevent such dangerous positive feedback loops? To explore these questions, we present an agent-based model of ideological polarization that differentiates between the tendency for two actors to interact (exposure) and how they respond when interactions occur, positing that interaction between similar actors reduces their difference while interaction between dissimilar actors increases their difference. Our analysis explores the effects on polarization of different levels of tolerance to other views, responsiveness to other views, exposure to dissimilar actors, multiple ideological dimensions, economic self-interest, and external shocks. The results suggest strategies for preventing, or at least slowing, the development of extreme polarization.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Mar 2021.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 2; 2026: 1.
4 papers.
- 2022Representation Learning in Continuous-Time Dynamic Signed Networkscited by 6
- 2025Can Large Language Models Predict Associations Among Human Attitudes?cited by 1
- 2025A Multi-Labeled Dataset for Indonesian Discourse: Examining Toxicity, Polarization, and Demographics Informationcited by 0
- 2026Automated Identification of Competing Narratives in Political Discourse on Social Mediacited by 0
- 2026Automated Identification of Competing Narratives in Political Discourse on Social Media10 Sep 2026
- 2025Can Large Language Models Predict Associations Among Human Attitudes?26 Mar 2025
- 2025A Multi-Labeled Dataset for Indonesian Discourse: Examining Toxicity, Polarization, and Demographics Information1 Mar 2025
- 2022Representation Learning in Continuous-Time Dynamic Signed Networks7 Jul 2022