  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.
