The military missions referred to as irregular warfare, which include counterinsurgency, counterterrorism, and stability operations, share the essential tenet of influence over populations. Within the defense analysis community, a new emphasis on such influence has been realized simultaneously with the emergence of the field of computational social science. This intersection of urgent requirement and nascent capability has resulted in new simulation models being rushed into service. In some instances these complex simulations are employed without empirical knowledge of their behaviors.
This thesis examines one such model, Nexus Schema Learner and its performance, over a large number of replications. Several input parameters of the Nexus model are explored through experiments. The propagation of uncertainty in these parameters to the output of the model is examined. The results of Nexus are also compared to a very simple model of a single social theory.
The research provides perspectives, techniques, and measures important to the practical application of Nexus. Influential parameters and associated broad categories of behavior are identified. These behaviors are also compared to the simple model. The findings provide an opportunity to consider empirical results of the simulation against its intended use and the social theories it is purports to represent.
Additional areas for conceptual, technical, and analytical development are outlined, pursuit of which will make future simulation studies using Nexus Schema Learner more effective and efficient. Some of these results provide compelling lessons for developers and potential users of all types of computational social science models.
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|Advisor:||Dorp, Johan Rene van|
|School:||The George Washington University|
|School Location:||United States -- District of Columbia|
|Source:||MAI 49/01M, Masters Abstracts International|
|Subjects:||Social research, Military studies, Operations research|
|Keywords:||Cognitive dissonance, Computational social science, Irregular warfare, Neural networks, Sensitivity analysis, Uncertainy analysis|
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