Stochastic Opinion Dynamics under Social Pressure in Arbitrary Networks
Jennifer Tang, Aviv Adler, Amir Ajorlou, and Ali Jadbabaie

TL;DR
This paper analyzes a stochastic opinion dynamics model where social pressure influences agents' declared opinions, revealing conditions for consensus and insights into inferring true beliefs under social influence.
Contribution
It introduces a comprehensive analysis of the Polya urn-based opinion model under social pressure, providing convergence and consensus conditions using Lyapunov and stochastic approximation methods.
Findings
Agents' declaration probabilities converge to equilibrium points.
Conditions for reaching consensus on declared opinions are established.
The model offers insights into inferring true beliefs under social pressure.
Abstract
Social pressure is a key factor affecting the evolution of opinions on networks in many types of settings, pushing people to conform to their neighbors' opinions. To study this, the interacting Polya urn model was introduced by Jadbabaie et al., in which each agent has two kinds of opinion: inherent beliefs, which are hidden from the other agents and fixed; and declared opinions, which are randomly sampled at each step from a distribution which depends on the agent's inherent belief and her neighbors' past declared opinions (the social pressure component), and which is then communicated to her neighbors. Each agent also has a bias parameter denoting her level of resistance to social pressure. At every step, each agent updates her declared opinion (simultaneously with all other agents) according to her neighbors' aggregate past declared opinions, her inherent belief, and her bias…
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Taxonomy
TopicsOpinion Dynamics and Social Influence · Complex Network Analysis Techniques · Mathematical and Theoretical Epidemiology and Ecology Models
