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One form of long-term behavior revealed by opinion dynamics simulations isintermittency, where an individual cycles between eras of stable, constantbeliefs and turbulent, fluctuating beliefs, for example when inferring thepolitical bias of a media organization. We explore this phenomenon by buildingan idealized network of Bayesian learners, who infer the bias of a coin fromobservations of coin tosses and peer pressure from political allies andopponents. Numerical simulations reveal that three types of network structurelead to three different types of intermittency, which are caused by agents``locking out'' opponents from sure beliefs in specific ways. The probabilitydensity functions of the dwell times, over which the learners sustain stable orturbulent beliefs, differ across the three types of intermittency. Hence, onecan observe the dwell times of a learner to infer the underlying networkstructure, at least in principle.

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