A Hybrid Opinion Formation and Polarization Model

Area of Science:

• Social Sciences
• Computational Social Science
• Network Science

Background:

• Traditional opinion formation models use numerical representations, limiting nuanced opinion dynamics.
• Existing models often fail to capture the gradual shift in opinion strength.

Purpose of the Study:

• To propose a novel hybrid opinion model integrating discrete opinions with continuous support degrees.
• To investigate the evolution of both opinion and support degree within social networks.
• To explore extensions of the model leading to opinion polarization.

Main Methods:

• Developed a hybrid agent-based model representing opinions as discrete states (Yes/No/Neutral) with associated continuous support degrees.
• Simulated opinion evolution by updating support degrees based on interactions, allowing for progressive changes.
• Analyzed the impact of key parameters: number of agents, initial support degree distribution, and support degree change per interaction.

Main Results:

• The hybrid model captures the dual evolution of opinion state and support intensity.
• Simulations demonstrate how progressive changes in support degree can lead to opinion shifts.
• Model extensions show potential for generating opinion polarization within social groups.

Conclusions:

• The hybrid opinion model offers a more realistic representation of opinion dynamics in social systems.
• Support degree evolution is a critical factor influencing opinion change and polarization.
• The model provides a framework for studying complex social phenomena like opinion polarization.