Agent-based and regression models of social influence

This paper studies social influence (i.e., adoption of belief) using agent-based simulation and regression models. Each agent is modeled by a linear regression model. Agents interact with neighbors by exchanging social beliefs. It is observed that if individual belief is linear in neighbors' beliefs, system-level belief and aggregated neighbors' beliefs can also be described by a linear regression model. Analysis is conducted on a simplified 2-node network to provide insight into the interactions and results of general models. Least squares estimates are developed. Explicit expressions are obtained to explain relationship between initial belief and current belief.