icbnet

“Landscape of risk” (RL) is a metaphor to describe agglomerations of interdependent risk. The idea is to integrate the full scale, variety, velocity, variability and the related determinants of a complex operations’ system into one computable model. The atomic elements of this network are managed nodes that are exposed to risk, thus becoming the source or target of unplanned events with positive or negative impacts and propagation effects. Management is understood as continuing effort of operations’ intelligence to realise and evaluate risk and to effectively act on it. The challenges are vast increases of the resolution of object and time and the accelerating speed of change, particularly of technological innovation. This are the reasons that RLs become more and dynamic, that models need to identify and capture interdependency and that learning needs to be directly integrated into the managerial workflows. Therefore, the concept allows for the integration of the “Big V” of data (volume, velocity, variability) as well as for human and machine intelligence respectively learning. We discuss various problems and alternative models as well as architectures for processing complex landscapes and provide a first formal semantic model about the managerial handling.