Thumbnail Image
Item

Voter-like Dynamics with Conflicting Preferences on Modular Networks

Title / Series / Name
Publication Volume
Publication Issue
Pages
Authors
Zimmaro, Filippo
Contucci, Pierluigi
Kertész, János
Editors
Keywords
agent-based models
networks
opinion dynamics
sociophysics
Information Systems
Mathematical Physics
Physics and Astronomy (miscellaneous)
General Physics and Astronomy
Electrical and Electronic Engineering
Show all metadata
Files
Thumbnail Image
Kertesz-Janos7_2023.pdf
Adobe PDF, 7.11 MB
URI
https://hdl.handle.net/20.500.14018/26988
Abstract
Two of the main factors shaping an individual’s opinion are social coordination and personal preferences, or personal biases. To understand the role of those and that of the topology of the network of interactions, we study an extension of the voter model proposed by Masuda and Redner (2011), where the agents are divided into two populations with opposite preferences. We consider a modular graph with two communities that reflect the bias assignment, modeling the phenomenon of epistemic bubbles. We analyze the models by approximate analytical methods and by simulations. Depending on the network and the biases’ strengths, the system can either reach a consensus or a polarized state, in which the two populations stabilize to different average opinions. The modular structure generally has the effect of increasing both the degree of polarization and its range in the space of parameters. When the difference in the bias strengths between the populations is large, the success of the very committed group in imposing its preferred opinion onto the other one depends largely on the level of segregation of the latter population, while the dependency on the topological structure of the former is negligible. We compare the simple mean-field approach with the pair approximation and test the goodness of the mean-field predictions on a real network.
Topic
Publisher
Place of Publication
Type
Journal article
Date
2023-05-24
Language
ISBN
Identifiers
10.3390/e25060838
Publisher link
Unit
Collections
Network and Data Science