A simulation assessment of methods to infer cultural transmission on dark networks

Abstract

The social transmission of beliefs, behaviors, and technologies is a central function of dark networks, just as it is in legitimate networks. One motivation for disrupting dark networks is to break the flow of information and learning. It is often unclear, however, which network should be targeted for disruption because individuals inhabit multiple and correlated networks, and the most relevant network for a given cultural process must be inferred from limited empirical data. Three analytic methods potentially are able to distinguish among alternative network diffusion processes: autoregression, dyadic regression with permutations, and dyadic regression with or random effects. All three rely on having measureable cultural outcomes and network or tree-like connections among the data points. We tested the ability of each method to infer cultural diffusion correctly within 4000 simulated datasets generated on two historical networks that linked violent and pacifist Anabaptist religious groups. Under both frequentist and Bayesian inference procedures, regression of dyadic matrices with random effects exhibited the best statistical performance. We found similar results in a more comprehensive search of the network parameter space that simulated both network structures and the diffusion of traits.