Discrete model of opinion changes using knowledge and emotions as control variables

Area of Science:

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

Background:

• Understanding opinion change is crucial for social simulations.
• Existing models often lack flexibility in representing individual attitude dynamics.
• Emotional states and information processing significantly influence individual opinions.

Purpose of the Study:

• To develop a flexible, discrete model of individual attitude dynamics for agent-based simulations.
• To represent opinion changes influenced by an agent's emotional state and information level.
• To explore the application of this model in diverse social environments.

Main Methods:

• A discrete version of the cusp catastrophe model was adapted.
• Information was modeled as a normal factor, and emotional arousal as a splitting factor.
• Agent communication was simulated with a fully connected topology.

Main Results:

• The model demonstrates complex agent behavior and opinion dynamics.
• Phase transitions were observed, driven by external propaganda and symmetry breaking.
• Stable distributions of agents with varying emotions, information, and opinions emerged.

Conclusions:

• The proposed model offers a flexible representation of opinion dynamics.
• Agent emotional states and information levels are key drivers of opinion change.
• The model's complexity highlights the impact of external factors like propaganda on social systems.