Abstract
While human behavior has long been studied, recent and ongoing advances in computational modeling present opportunities for recasting research outcomes in human behavior. In this paper we describe how Bayesian networks can represent outcomes of human behavior research. We demonstrate a Bayesian network that represents political radicalization research – and show a corresponding visual representation of aspects of this research outcome. Since Bayesian networks can be quantitatively compared with external observations, the representation can also be used for empirical assessments of the research which the network summarizes. For a political radicalization model based on published research, we show this empirical comparison with data taken from the Minorities at Risk Organizational Behaviors database.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jensen, F.V., Nielsen, T.D.: Bayesian Networks and Decision Graphs, 2nd edn. Springer, New York (2007)
McCauley, C., Moskalenko, S.: Mechanisms of Political Radicalization: Pathways Toward Terrorism. Terrorism and Political Violence 20, 415–433 (2008)
Minorities at risk organizational behavior dataset (2008), http://www.cidcm.umd.edu/mar (retrieved from June 2009)
GeNIe Tutorials, Decision Systems Laboratory of the University of Pittsburgh, http://genie.sis.pitt.edu/wiki/GeNIe_Documentation (accessed December 2008)
Tanner, M.: Tools for Statistical Inference. Springer, New York (1996)
Gelman, A., Carlin, J., Stern, H., Rubin, D.: Bayesian Data Analysis. Chapman and Hall/CRC, Boca Raton (2004)
R: A Language and Environment for Statistical Computing, http://www.R-project.org
Højsgaard, S.: gRain; A Graphical Independence Networks package in R (2009), http://genetics.agrsci.dk/~sorenh/public/R/gRainweb (retrieved from May 2009)
Riggelson, C.: Learning parameters of Bayesian networks from incomplete data via importance sampling. International Journal of Approximate Reasoning 42, 69–83 (2005)
Heckerman, D., Geiger, D.: Chickering: Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning 20, 197–243 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Whitney, P., Walsh, S. (2010). Calibrating Bayesian Network Representations of Social-Behavioral Models. In: Chai, SK., Salerno, J.J., Mabry, P.L. (eds) Advances in Social Computing. SBP 2010. Lecture Notes in Computer Science, vol 6007. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12079-4_42
Download citation
DOI: https://doi.org/10.1007/978-3-642-12079-4_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12078-7
Online ISBN: 978-3-642-12079-4
eBook Packages: Computer ScienceComputer Science (R0)