Evolution of beliefs in social networks

TL;DR

This paper introduces a new Markov chain-based framework to analyze how beliefs evolve in social networks, considering static, random, and homophily-driven dynamic structures, revealing convergence and divergence patterns.

Contribution

It extends prior models by incorporating both horizontal and vertical belief transmission within a unified Markov chain framework, analyzing different network dynamics.

Findings

Static and random networks converge to a single belief set.

Homophily-based networks may not converge and can have multiple belief clusters.

Lower bounds on the number of belief clusters depend on initial beliefs.

Abstract

Evolution of beliefs of a society are a product of interactions between people (horizontal transmission) in the society over generations (vertical transmission). Researchers have studied both horizontal and vertical transmission separately. Extending prior work, we propose a new theoretical framework which allows application of tools from Markov chain theory to the analysis of belief evolution via horizontal and vertical transmission. We analyze three cases: static network, randomly changing network, and homophily-based dynamic network. Whereas the former two assume network structure is independent of beliefs, the latter assumes that people tend to communicate with those who have similar beliefs. We prove under general conditions that both static and randomly changing networks converge to a single set of beliefs among all individuals along with the rate of convergence. We prove that homophily-based network structures do not in general converge to a single set of beliefs shared by all and prove lower bounds on the number of different limiting beliefs as a function of initial beliefs. We conclude by discussing implications for prior theories and directions for future work.