Computational Research into Complex Systems

This report outlines the status of computational research into complex systems. It describes characteristics of such systems, why this kind of research is important, what software is available to do it and who is active in the research field. The understanding of complex systems refutes the bottom-up approach of traditional science. The latter assumes that the underlying mechanisms of a system’s behaviour can be understood by studying its parts independently. A complex system, however, is more than the sum of its parts. Its behaviour emerges through “interaction between the constituent parts”. Although the term “complex system ” has multiple usages, some of the properties of a complex system, generally agreed upon are: • It consists of many parts, out of whose interaction “emerges ” behaviour not present in the parts alone; • It is coupled to an environment with which it exchanges energy, information, or other types of resources; • It exhibits both order and randomness – in its (spatial) structure or (temporal) behaviour; • There is no central control element, neither internal nor external; • The structure / behaviour is robust against significant perturbation; • The system has memory and feedback; • The system can adapt itself according to its history or feedback. Examples of complex systems can be found in all areas of science – non-linear dynamical sys-tems (mathematics), networks (statistics), non-equilibrium chemical reactions (chemistry), protein interaction networks (molecular biology), the brain (neuroscience), ant colonies (animal behaviour), cellular automata (computer science), market behaviour (economics), etc. The study of these sys-tems requires combining methods from more than one discipline as well as the development of new mathematical and computational tools