In brief, complex systems science involves the study of how many elements develop behaviors that are beyond those behaviors possible by considering the individual elements alone. While the behavior of each individual component of a system in isolation may support intricate dynamics, together the individual components interact to support group behaviors and system dynamics well beyond those possible from individual components alone.
Complex systems science is a rapidly growing and emerging field that is inherently interdisciplinary. It can be applied to a wide variety of fields including biology; medicine and cognitive science; mechanical, chemical, electrical and civil engineering; physics and astronomy; economics and social sciences. The future of research in these fields lies in understanding not just the isolated components of a given system, but the manner in which the individual components interact to produce emergent group behavior.
In contrast to data mining or big data, in which a primary focus is to understand hidden patterns or structure in large data sets, complex systems science attempts to identify causality and uncover the universality that exists in large-scale systems. Causality and universality are due to peer and hierarchical interactions, patterns and scaling of individual system components. Universality has been observed across a wide range of fields such as brain science, insect swarming, social science and fluid dynamics.
Key to the advancement of complex systems science is the development and use of mathematical tools designed to understand the resultant outcome of group behaviors that are not evident when studying the behavior individual elements alone. Mathematical tools for complex systems science are drawn from the following fields: