The invention pertains to a method for modeling and simulating entities whose interdependencies, as well as the resulting system behavior, can be used to make statements about real behavior. It is comprised of the following steps: the real entities are each represented by an individual software-object which stores the individual behavior of the corresponding entity, wherein this behavior is extracted from real data about the entity and its environment using machine learning methods, in order to then store the individual behavior within the software-object via a set of probabilistic networks (PN), wherein each PN models one sub-behavior of the entity as quantified linear or non-linear dependencies between a set of influence factors and behavior aspects, the influence factors and behavior aspects are represented by the corresponding nodes in the PN. The global interdependencies between the entities are extracted from real data and stored as linear or non-linear dependencies between the entities in the meta-PN and the meta-PN are generated by merging local PNs and by adding the extracted global interdependencies.