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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.

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Summary
This summary is machine-generated.

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.

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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.