Dealing with interaction for complex systems modelling and prediction

The increasing complexity of problems in the context of system modeling is leading to a new epistemological approach able to provide a representation which allows from one hand, to model complex phenomena with the support of mathematical and computational instruments, and on the other hand able to capture the global system description. In this article is presented a methodology for complex dynamical systems modeling which is an extension of the supervised learning paradigm. The theoretical aspects of our methodology are introduced and then two different and heterogeneous case studies are presented.