The science of complexity and the study of complex adaptive systems emerged in the late 1980s. It was spurred by a recognition that there were an a wide range of entities that were (a) systems by any reasonable definition of the term and (b) not designed, built, and maintained deliberately by humans.
These ‘self-organized’ systems appear across many of the scientific and quasi-scientific domains: biology, sociology, economics, epidemiology, public policy, military conflict, political movements, population dynamics, etc.
There are two key features that characterize the systems under consideration by complexity scientists:
- The whole is more than the sum of its parts. More specifically, as one tries to create an analytical model of the system, the typical engineering approach of subdividing the system into its parts, analyzing the rule sets and interconnections among the parts, and piecing it all together in a straightforward manner, one fails. There is something in the interactions or the structure or the situation or the history of the system that prevents this reductionist approach.
- The individual elements of the system — be they people, groups, animals, or other decision-making entities — have the ability to adapt. They might have a memory, be able to learn, be able to communicate, or even anticipate future conditions or the actions of others. Because of this adaptation, the system’s future cannot be predicted based on its past behavior alone. As Heraclitus said, “You never step in the same river twice”
So, a complexity scientist is one who establishes truths that are specific, universal and certain about self-organized systems.
Ken Comer 