Several obstacles have limited our understanding of how socio-environmental factors interact to shape one’s behavior. One obstacle was the difficulty of developing models when the factors are difficult to measure, given uncertainty or inconsistency in data. One of our major contributions has been to develop a methodology that could be used in this setting (Giabbanelli 2013). We further demonstrated that the methodology had several applications. First, it was not limited to systems with high uncertainty: indeed, it could be interfaced with commonly used techniques in order to capture systems with high uncertainty on certain domains but less on others (Giabbanelli et al., 2014). Second, it could be used as part of randomized clinical trials in order to allocate patients to groups given the uncertainty in the factors involved in the clinical outcome (Giabbanelli et al., 2014). Third, it was suitable for application contexts ranging from complex health behaviors to dynamic societal issues unfolding over a large geographical scale (Giabbanelli 2014; Pratt et al. 2012).
- Pratt, S.F., Giabbanelli, P.J., Jackson, P., Mago, V.K. (2012). Rebel with many causes: A computational model of insurgency. In Proceedings of the 2012 IEEE International Conference on Intelligence and Security Informatics (ISI).
- Giabbanelli, P.J. (2013) A novel framework for complex networks and chronic diseases. Studies in Computational Intelligence, 424, 207-215.
- Giabbanelli, P.J., Jackson, P., Finegood, D.T. (2014) Modeling the joint effect of social determinants and peers on obesity. Intelligent Systems Reference Library, 52, 145-160.
- Giabbanelli, P.J., Crutzen, R. (2014) Creating groups with similar expected behavioral response in randomized controlled trials: a fuzzy cognitive map approach. BMC Medical Research Methodology, 14, 130.
- Giabbanelli, P.J. (2014) Modeling the spatial and social dynamics of insurgency. Security Informatics, 3(1):1-15.
- Dr. Piper Jackson, Simon Fraser University, Canada