Philippe J. Giabbanelli

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Attempts to model insurgency have suffered from several obstacles. Qualitative research may be vague and conflicting, while quantitative research is limited due to the difficulties of collecting sufficient data in war and inferring complex relationships. We propose an innovative combination of Fuzzy Cognitive Maps and Cellular Automata to capture this(More)
BACKGROUND Controlling bias is key to successful randomized controlled trials for behaviour change. Bias can be generated at multiple points during a study, for example, when participants are allocated to different groups. Several methods of allocations exist to randomly distribute participants over the groups such that their prognostic factors (e.g.,(More)
Retailers routinely use association mining to investigate trends in the use of their products. In the medical world, association mining is mostly used to identify associations between symptoms and diseases, or between drugs and adverse events. In comparison, there is a relative paucity of work that focuses on relationships between drugs exclusively. In this(More)
Insurgency emerges from many interactions between numerous social, economical, and geographical factors. Adequately accounting for the large number of potentially relevant interactions, and the complex ways in which they operate, is key to creating valuable models of insurgency. However, this has long been a challenging endeavour, as insurgency imposes(More)