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Related Experiment Video

Updated: May 7, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Opinion dynamics model with weighted influence: exit probability and dynamics.

Soham Biswas1, Suman Sinha, Parongama Sen

  • 1Department of Theoretical Physics, Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai 400 005, India.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|September 17, 2013
PubMed
Summary
This summary is machine-generated.

We developed a new stochastic model for opinion dynamics. It reveals unique behaviors like step function exit probabilities and novel coarsening exponents, distinguishing it from deterministic models.

Related Experiment Videos

Last Updated: May 7, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

  • Statistical Physics
  • Complex Systems
  • Sociophysics

Background:

  • Opinion dynamics models are crucial for understanding social behavior.
  • Existing models often lack the complexity to capture nuanced opinion shifts.
  • Stochasticity plays a significant role in real-world opinion formation.

Purpose of the Study:

  • To introduce a novel stochastic model for binary opinion dynamics.
  • To investigate the influence of neighboring domain sizes on opinion formation.
  • To analyze the unique dynamical properties of this model.

Main Methods:

  • Development of a stochastic model based on neighboring domain sizes.
  • Analysis of exit probability to identify basins of attraction.
  • Coarsening studies to determine novel exponent values.

Main Results:

  • The model exhibits a step function behavior in exit probability, indicating a separatrix.
  • This step function behavior is unique compared to other one-dimensional opinion dynamics models.
  • Novel coarsening exponent values were obtained, with a lower persistence exponent for the stochastic model versus deterministic ones.

Conclusions:

  • The proposed stochastic model represents a unique dynamical class.
  • The observed behaviors, including the step function and novel exponents, offer new insights into opinion formation.
  • The findings highlight the importance of stochasticity in differentiating opinion dynamics.