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Explaining opinion polarisation with opinion copulas
1IZA - Institute of Labor Economics, Schaumburg-Lippe-Str. 5/9, D-53113, Bonn Germany.
Plos One
|August 23, 2017
Summary
Homophily drives opinion dynamics, but noise in models leads to consensus. This study introduces opinion copulas and heterogeneous priors to explain how opinion clusters and polarization emerge in stochastic models.
Area of Science:
- Social Psychology
- Computational Social Science
- Opinion Dynamics
Background:
- Homophily, the tendency for similar individuals to interact, is a key driver of opinion dynamics.
- Bounded Confidence Models (BCM) assume interaction is limited by a tolerance threshold.
- Stochastic BCM with noise typically result in consensus, failing to explain opinion clustering and polarization.
Purpose of the Study:
- To explain the emergence and sustainment of opinion clusters and polarization in stochastic opinion dynamics.
- To investigate the role of heterogeneous priors in opinion formation.
- To introduce and validate the concept of opinion copulas in modeling opinion dynamics.
Main Methods:
- Investigated the role of heterogeneous priors in opinion formation.
- Introduced the concept of opinion copulas.
- Utilized stochastic bounded confidence models.
Main Results:
- Demonstrated that heterogeneous priors and opinion copulas can lead to opinion clustering in stochastic BCM.
- Showed that opinion clustering can occur without additional assumptions beyond bounded confidence and noise.
- Provided a mechanism for the emergence of polarization from homophilous interactions.
Conclusions:
- Heterogeneous priors, modeled using opinion copulas, are crucial for understanding opinion clustering in stochastic environments.
- The proposed framework reconciles homophily with the observed persistence of diverse opinions and polarization.
- This research offers a novel explanation for complex opinion dynamics previously unexplained by standard models.
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