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

One form of long-term behavior revealed by opinion dynamics simulations is intermit-tency, 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.(C) 2022 Elsevier B.V. All rights reserved.

Automated summary

This article explores a long-term behavior known as intermittency, where individuals cycle between stable and turbulent beliefs. Through simulations, it is found that different types of network structures lead to different types of intermittency, and the probability density functions of dwell times also vary. Therefore, the underlying network structure can be inferred by observing the dwell times of learners.