What is it about?
Polarization has many shapes. Typically seen as some people believing one thing and others believing the opposite, it is also associated with how these beliefs are distributed in the social fabric. Here, we ask the question: When is polarization 'bad?' How does it affect individuals' strategic choices in pursuing subjectively better outcomes? We take an extreme case in which individuals agree on the best course of action but to different extents. We show that, while the diversity of opinions is, in fact, catalytic of social change—by triggering cascades of trust that others will enable one's best outcomes—their segregation reduces the opportunities for individuals to achieve higher gains, even when all individuals agree on an optimal choice.
Photo by Oxana Melis on Unsplash
Why is it important?
We now understand the beneficial and nefarious impacts of different manifestations of polarization in individuals' ability to reach solutions that improve everyone's wellbeing. This knowledge is essential to designing the right strategies to reflect the collective agreement. It also highlights the pervasiveness of the problems of polarization, which extend to situations of agreement on advantageous options.
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This page is a summary of: Segregation and clustering of preferences erode socially beneficial coordination, Proceedings of the National Academy of Sciences, December 2021, Proceedings of the National Academy of Sciences, DOI: 10.1073/pnas.2102153118.
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Like a natural system, democracy faces collapse as polarization leads to loss of diversity
Description of the set of articles in the special feature on polarization
Dynamics of Political Polarization Special Feature
Articles in the special feature on political polarization
Replication Data for: Segregation and Clustering of Preferences Erode Socially Beneficial Coordination.
Replication Data for: Segregation and Clustering of Preferences Erode Socially Beneficial Coordination. This dataset contains data collected in an online experiment and the Stata code used to analyze the experimental data. The online experiment was conducted in February 2021. Participants were recruited on the prolific platform (https://prolific.co/). The experiment was programmed in oTree (Chen et al. 2016).
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