Paths to Polarization: How Extreme Views, Miscommunication, and Random Chance Drive Opinion Dynamics

Understanding the social conditions that tend to increase or decrease polarization is important for many reasons. We study a network-structured agent-based model of opinion dynamics, extending a model previously introduced by Flache and Macy (2011), who found that polarization appeared to increase w...

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Bibliographic Details
Published in:Complexity (New York, N.Y.) N.Y.), 2018-01, Vol.2018 (2018), p.1-17
Main Authors: Turner, Matthew A., Smaldino, Paul E.
Format: Article
Language:English
Subjects:
Analysis
Assimilation
Complexity
Cooperation
Culture
Dynamic structural analysis
Evolution
Extremism
Multiculturalism & pluralism
Polarization
Population
Population biology
Simulation
Social aspects
Social networks
Sociology
Theory
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Summary:Understanding the social conditions that tend to increase or decrease polarization is important for many reasons. We study a network-structured agent-based model of opinion dynamics, extending a model previously introduced by Flache and Macy (2011), who found that polarization appeared to increase with the introduction of long-range ties but decrease with the number of salient opinions, which they called the population’s “cultural complexity.” We find the following. First, polarization is strongly path dependent and sensitive to stochastic variation. Second, polarization depends strongly on the initial distribution of opinions in the population. In the absence of extremists, polarization may be mitigated. Third, noisy communication can drive a population toward more extreme opinions and even cause acute polarization. Finally, the apparent reduction in polarization under increased “cultural complexity” arises via a particular property of the polarization measurement, under which a population containing a wider diversity of extreme views is deemed less polarized. This work has implications for understanding the population dynamics of beliefs, opinions, and polarization as well as broader implications for the analysis of agent-based models of social phenomena.
ISSN:1076-2787
1099-0526
DOI:10.1155/2018/2740959