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Collective group drift in a partial-differential-equation-based opinion dynamics model with biased perception
Christian Koertje1, Hiroki Sayama1,2
1Binghamton Center of Complex Systems (CoCo), Binghamton University, Binghamton, New York 13902-6000, USA.
Physical Review. E
|April 18, 2024
Summary
Biased information gathering can lead to polarized populations. Our model shows that while groups can form, extreme bias prevents consensus, causing collective drift towards extremism.
Area of Science:
- Mathematical Modeling
- Opinion Dynamics
- Computational Social Science
Background:
- Modern technology facilitates rapid, biased information gathering.
- This phenomenon contributes to societal polarization and extremism.
- Understanding opinion dynamics is crucial for social cohesion.
Purpose of the Study:
- To investigate opinion dynamics using a partial-differential-equation model.
- To analyze the impact of biased information gathering on consensus formation.
- To quantify collective behavior and group dynamics in polarized populations.
Main Methods:
- Developed a novel interaction kernel function for biased information gathering.
- Employed linear stability analysis to assess consensus conditions.
- Conducted numerical simulations to observe collective behavior and group evolution.
- Utilized temporal correlation functions and distance metrics (Manhattan, Euclidean) to analyze group characteristics.
Main Results:
- Biased populations can form cohesive opinionated groups.
- Excessive bias destabilizes consensus, leading to a stable homogeneous mixed state.
- Numerical simulations demonstrate collective drift towards extreme opinions.
- Characteristic time scales for group existence were quantified.
Conclusions:
- Individual biases can collectively drive groups to extreme viewpoints.
- The model captures the emergence of polarized groups and the loss of consensus.
- Boundary conditions can induce pattern formation at domain extremes, mirroring societal segregation.
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