Vacillating about media bias: changing one's mind intermittently within a network of political allies and opponents

TL;DR

This paper investigates how individuals in political networks intermittently change their beliefs about media bias, influenced by network structure, peer pressure, and Bayesian inference, revealing patterns of belief stability and turbulence.

Contribution

It introduces an idealized Bayesian network model to analyze how network structure causes different types of intermittency in opinion dynamics.

Findings

Three network structures produce distinct intermittency types.

Dwell time distributions vary with network structure.

Belief stability and turbulence can indicate underlying network properties.

Abstract

One form of long-term behavior revealed by opinion dynamics simulations is intermittency, where an individual cycles between eras of stable, constant beliefs and turbulent, fluctuating beliefs, for example when inferring the political bias of a media organization. We explore this phenomenon by building an idealized network of Bayesian learners, who infer the bias of a coin from observations of coin tosses and peer pressure from political allies and opponents. Numerical simulations reveal that three types of network structure lead to three different types of intermittency, which are caused by agents ``locking out'' opponents from sure beliefs in specific ways. The probability density functions of the dwell times, over which the learners sustain stable or turbulent beliefs, differ across the three types of intermittency. Hence, one can observe the dwell times of a learner to infer the underlying network structure, at least in principle.