Nicodemo, Nicola (2017) Emergent Patterns In Global Terrorism. [Laurea triennale]
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The purpose of this work is to point out regular patterns observed in terrorist activity. In the first part of the work, we have collected records of terrorist attacks from publicly available databases. We have represented the frequency distributions of these fatal events with respect to the number of casualties and have shown that they scale as power laws, with scaling parameter 2.5. We have shown that national conflicts exhibit different scaling parameters. However, two cluster emerge corresponding to industrialized countries (i.e. G7 countries, scaling parameter 1.70) and non-industrialized countries (i.e. non-G7 countries, scaling parameter 2.55). The scaling parameter can be thought as a measure of the type of conflict, thus reflecting the characteristics of the underlying mechanism of terrorist organizations. The power law model is fit to empirical data using statistical methods proposed by Clauset and performed in R by means of “poweRlaw” package. In particular, the scaling parameter is extimated by maximum likelihood method. Kolmogorov-Smirnov statistics is then used to validate the power-law hypotesis. One case of that can be studied in detail is the online ecology of insurgent aggregates. Research shows that the number of aggregates of size s also scales as a power law with scaling exponent 2.33. In the second part, we have introduced a model to relate the internal dynamics for terrorist organization to the macroscopical distribution of terrorist attacks, based on previous work by Johnson et al. and Clauset et al. The master equation of this model can be solved analytically. It yelds a power law distribution in the number of cells of size s, with scaling parameter 5/2, in agreement with experimental data. Ultimately, we have depicted a framework in which predictions about the dynamics of insurgent organizations can be done based on empirical data. It also poses the basis for a more consistent modelization of terrorism.
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