Dynamics of Affective Polarization: From Consensus to Partisan Divides
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arXiv
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| Format: | Preprint |
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2024
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| author | Nettasinghe, Buddhika Percus, Allon G. Lerman, Kristina |
| author_facet | Nettasinghe, Buddhika Percus, Allon G. Lerman, Kristina |
| contents | 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 non-partisan polarization. Analysis reveals that larger out-group animosity compared to in-group favoritism, i.e. more hate than love, is sufficient for polarization, while larger in-group favoritism compared to out-group animosity, i.e., more love than hate, is necessary for consensus. We also show that, counter-intuitively, 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 tipping points 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_16940 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Dynamics of Affective Polarization: From Consensus to Partisan Divides Nettasinghe, Buddhika Percus, Allon G. Lerman, Kristina Social and Information Networks Multiagent Systems Physics and Society 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 non-partisan polarization. Analysis reveals that larger out-group animosity compared to in-group favoritism, i.e. more hate than love, is sufficient for polarization, while larger in-group favoritism compared to out-group animosity, i.e., more love than hate, is necessary for consensus. We also show that, counter-intuitively, 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 tipping points 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. |
| title | Dynamics of Affective Polarization: From Consensus to Partisan Divides |
| topic | Social and Information Networks Multiagent Systems Physics and Society |
| url | https://arxiv.org/abs/2403.16940 |