ABSTRACT
Complexity science is the study of how large numbers of relatively simple entities organize themselves into a collective whole that creates patterns, uses information, and, in some cases, evolves and learns. Those collective wholes that do not evolve and learn are complex systems; those that do are complex adaptive systems (CAS). Complexity and its various systems have been a topic of study in the natural sciences for decades already (Mitchell, 2011). Physics, chemistry, biology, mathematics, meteorology, and engineering practitioners have used the concept of complex systems to explain phenomena as diverse as phase transitions in physical matter, immune system functions, and weather patterns (Braha et al., 2006; Callebaut and Rasskin-Gutman, 2005; Flake, 1998; Gell-Mann, 1994; Gleick, 1988; Nicolis and Prigogine, 1989; von Bertalanffy, 1969). The CAS these scientists have modeled use, for the most part, the concept of dynamic systems and nonlinear equations. In short, CAS is a nice fit for the natural sciences in many ways. The goal of this volume, however, is to show where and how the idea of complexity has spread beyond the natural sciences. Our authors show how complexity ontology with its corresponding emphasis on modeling has already effectively spread to the social sciences and is at the very threshold of making a significant impact on the humanities.
