Opinion Dynamic and Social Clustering in a 2D Space: An Agent Based Experiment

Area of Science:

• Computational Social Science
• Opinion Dynamics
• Agent-Based Modeling

Background:

• Understanding opinion dynamics is crucial for social cohesion and addressing polarization.
• Existing models often overlook the interplay between physical space, agent movement, and opinion formation.

Purpose of the Study:

• To investigate how individual movement and interactions influence opinion dynamics and polarization in a 2D space.
• To extend the Attraction-Repulsion Model (ARM) to incorporate geographic constraints and social interactions.
• To analyze the formation of opinion clusters and their relationship with polarization and social segregation.

Main Methods:

• Utilized an extended Attraction-Repulsion Model (ARM) in a two-dimensional space.
• Simulated agent movement and interactions based on opinion similarity and geographic proximity.
• Analyzed cluster formation, size, and polarization under varying parameters like visibility and tolerance.

Main Results:

• Similar opinions lead to like-minded groups; higher visibility results in fewer, larger clusters.
• Low tolerance significantly drives opinion polarization.
• Geographic constraints cause larger clusters to centralize, while smaller clusters remain peripheral.
• Increased opinion dimensions lead to fewer, larger clusters.

Conclusions:

• Agent movement and spatial constraints are significant factors in opinion polarization and the formation of echo chambers.
• Findings offer insights into social segregation, misinformation spread, and political polarization.
• Highlights the feedback loop between mobility, opinion formation, and collective social structures.