A simulation assessment of methods to infer cultural transmission on dark networks
Authored by Rouslan Karimov, Luke J Matthews
Date Published: 2017
DOI: 10.1177/1548512916679900
Sponsors:
RAND Corporation
Platforms:
R
Model Documentation:
Other Narrative
Model Code URLs:
Model code not found
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.
Tags
Agent-based models
Evolution
language
Autocorrelation
Network modeling
Network simulation
Galton problem