Calibrating bayesian network representations of social-behavioral models

Authors:
Paul Whitney;Stephen Walsh
Affiliations:
Pacific Northwest National Laboratory, Richland, WA;Pacific Northwest National Laboratory, Richland, WA
Venue:
SBP'10 Proceedings of the Third international conference on Social Computing, Behavioral Modeling, and Prediction
Year:
2010

Citing 3
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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.