Hochschulbibliographie

In the context of discrete-event simulation of production and logistics systems, modelling an exact representation of the real system is needed for obtaining sound and reliable results. The automatic generation of simulation models is an approach for saving time and effort for creating models and, therefore, it is a recurring issue in modelling methodology research. In automatic model generation, the approximation of dynamic behaviour is a challenging problem. This is for example the case when the dispatching and scheduling of production jobs needs to be adequately emulated, but the underlying rules are unknown. In previous work, we presented an approach for approximating dynamic behaviour through artificial neural networks. In this paper, we propose an improved approach and investigate its suitability again with artificial neuronal networks as well as with other data mining and supervised machine learning methods.

States and international organizations have compiled lists of a great variety of terrorist groups. The current European Union list includes 44 entities. This study analyzes the underlying motives of the terrorist organizations named in this list. In order to understand the groups' motivations and consequently be able to advise on methods of countering them with communication strategies, we employ a three-item typology provided by Waldmann (2001). The results show that only five of the 44 groups were religiously motivated to commit terrorism. Most of the groups (n=20) had nationalist-separatist motives, and 19 groups displayed social-revolutionary motives. Based on the respective motives, differing counter-terrorism strategies are proposed, e.g., developing rhetorical counter-narratives that address and reduce the groups' motivational and identity-generating characteristics.