Opinion formation and distribution in a bounded-confidence model on various networks
Name
PhysRevE.97.022312.pdf
Size
1.92 MB
Format
Adobe PDF
Checksum (MD5)
f8c8066dec7645e2d97fef8c196787d7
Author(s)
Van Gorder, Robert A.
Porter, Mason A.
Meng, Xianglin Flora
Date Issued
February 2018
Journal
Physical Review E
Publisher
American Physical Society
Citation
Meng, X. Flora et al. "Opinion formation and distribution in a bounded-confidence model on various networks." Physical Review E 97, 2 (February 2018): 022312 © 2018 American Physical Society
Version
Final published version
Abstract
In the social, behavioral, and economic sciences, it is important to predict which individual opinions eventually dominate in a large population, whether there will be a consensus, and how long it takes for a consensus to form. Such ideas have been studied heavily both in physics and in other disciplines, and the answers depend strongly both on how one models opinions and on the network structure on which opinions evolve. One model that was created to study consensus formation quantitatively is the Deffuant model, in which the opinion distribution of a population evolves via sequential random pairwise encounters. To consider heterogeneity of interactions in a population along with social influence, we study the Deffuant model on various network structures (deterministic synthetic networks, random synthetic networks, and social networks constructed from Facebook data). We numerically simulate the Deffuant model and conduct regression analyses to investigate the dependence of the time to reach steady states on various model parameters, including a confidence bound for opinion updates, the number of participating entities, and their willingness to compromise. We find that network structure and parameter values both have important effects on the convergence time and the number of steady-state opinion groups. For some network architectures, we observe that the relationship between the convergence time and model parameters undergoes a transition at a critical value of the confidence bound. For some networks, the steady-state opinion distribution also changes from consensus to multiple opinion groups at this critical value.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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DOI of Published Version
https://doi.org/10.1103/PhysRevE.97.022312
