Modelling nations' political risk via computational intelligence techniques
by Mohamed M. Mostafa
International Journal of Computational Intelligence Studies (IJCISTUDIES), Vol. 4, No. 3/4, 2015

Abstract: Since the concept of 'failed states' was coined in the early 1990s, it has come to occupy a top tier position in the international peace and security's agenda. This study uses computational intelligence techniques to examine the effect of various social, economic and political factors on states' failure at the global level. Computational intelligence techniques use a broad family of computationally intensive methods that include decision trees, neural networks, rule induction, machine learning and graphic visualisation. Three artificial neural network models: multi-layer perceptron neural network (MLP), radial basis function neural network (RBFNN) and self-organising maps neural network (SOM) and one machine learning technique [support vector machines (SVM)] are compared to a standard statistical method [linear discriminant analysis (LDA)]. The variable sets considered are demographic pressures, movement of refugees, group paranoia, human flight, regional economic development, economic decline, delegitimisation of the state, public services' performance, human rights status, security apparatus, elites' behaviour and the role played by other states or external political actors. The study shows how it is possible to identify various dimensions of states' failure by uncovering complex patterns in the dataset, and also shows the classification abilities of computational intelligence techniques.

Online publication date: Thu, 05-Nov-2015

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