A Simulation Assessment of Methods to Infer Cultural Transmission on Dark Networks

Published in: The Journal of Defense Modeling and Simulation, Volume 14, Issue 1 (January 2017), Pages 7-16. doi: 10.1177/1548512916679900

Posted on RAND.org on October 18, 2018

by Rouslan I. Karimov, Luke J. Matthews

Read More

Access further information on this document at The Journal of Defense Modeling and Simulation

This article was published outside of RAND. The full text of the article can be found at the link above.

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.

Research conducted by

This report is part of the RAND Corporation External publication series. Many RAND studies are published in peer-reviewed scholarly journals, as chapters in commercial books, or as documents published by other organizations.

Our mission to help improve policy and decisionmaking through research and analysis is enabled through our core values of quality and objectivity and our unwavering commitment to the highest level of integrity and ethical behavior. To help ensure our research and analysis are rigorous, objective, and nonpartisan, we subject our research publications to a robust and exacting quality-assurance process; avoid both the appearance and reality of financial and other conflicts of interest through staff training, project screening, and a policy of mandatory disclosure; and pursue transparency in our research engagements through our commitment to the open publication of our research findings and recommendations, disclosure of the source of funding of published research, and policies to ensure intellectual independence. For more information, visit www.rand.org/about/principles.

The RAND Corporation is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.