Journal article
How out-group animosity can shape partisan divisions: A model of affective polarization
PNAS nexus, Vol.4(3), pgaf082
03/2025
DOI: 10.1093/pnasnexus/pgaf082
PMCID: PMC11927084
PMID: 40125444
Abstract
Politically divided societies are also often divided emotionally: people like and trust those with similar political views (in-group favoritism) while disliking and distrusting those with different views (out-group animosity). This phenomenon, called affective polarization, influences individual decisions, including seemingly apolitical choices such as whether to wear a mask or what car to buy. We present a dynamical model of decision-making in an affectively polarized society, identifying three potential global outcomes separated by a sharp boundary in the parameter space: consensus, partisan polarization, and nonpartisan polarization. Analysis reveals that larger out-group animosity compared to in-group favoritism, i.e.
, is sufficient for polarization, while larger in-group favoritism compared to out-group animosity, i.e.
, is necessary for consensus. We also show that, counterintuitively, increasing cross-party connections facilitates polarization, and that by emphasizing partisan differences, mass media creates self-fulfilling prophecies that lead to polarization. Affective polarization also creates
in the opinion landscape where one group suddenly reverses their trends. Our findings aid in understanding and addressing the cascading effects of affective polarization, offering insights for strategies to mitigate polarization.
Details
- Title: Subtitle
- How out-group animosity can shape partisan divisions: A model of affective polarization
- Creators
- Buddhika Nettasinghe - University of IowaAllon G Percus - Claremont Graduate UniversityKristina Lerman - University of Southern California
- Resource Type
- Journal article
- Publication Details
- PNAS nexus, Vol.4(3), pgaf082
- DOI
- 10.1093/pnasnexus/pgaf082
- PMID
- 40125444
- PMCID
- PMC11927084
- NLM abbreviation
- PNAS Nexus
- ISSN
- 2752-6542
- eISSN
- 2752-6542
- Publisher
- OXFORD UNIV PRESS
- Grant note
- Defense Advanced Research Projects Agency (DARPA): HR001121C0168
Mathematical models leveraged in this project were funded in part by Defense Advanced Research Projects Agency (DARPA) under contract HR001121C0168. However, the application outlined in this paper was out of scope for the DARPA research. The views, opinions and/or findings expressed are those of the authors and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government.
- Language
- English
- Date published
- 03/2025
- Academic Unit
- Business Analytics
- Record Identifier
- 9984802501002771
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