Complexity Theory

Complexity science is a fast emerging field of study. Complexity science is a term commonly used to represent a growing body of interdisciplinary studies about the structure, behaviour and dynamics of change in a specific category of complex systems known as Complex Adaptive Systems (CAS). CASs are open, evolutionary systems in which the components are dynamic, self-organizing, and strongly interrelated. Complexity science gives us the opportunity to look at problems with multiple perspectives, studying the micro and macro issues and understanding how they are interdependent.

Assumptions

The inherent assumption in complexity science is that all problems cannot be solved using traditional scientific methods. Complexity science provides a more accurate view of reality. It provides the models, the conceptual frameworks, and the theories that help make even the illogical seem logical. Before complexity, all phenomena could be reduced to simple cause and effect relationships. The dominant metaphor in traditional science or Newtonian science is that of a machine. The machine can be explained by understanding each part separately (reductionism). However, complexity science believes that the emergent properties of the whole can not be explained by the parts. Another assumption is that most of the organizational models and measurement concepts currently in the management field today were designed for a world that no longer exists. Rapid improvements in technology have bridged distances and fostered simultaneous creation and distribution of information. With the compression of space and time, the significance of complexity science has become even greater. Finally, CASs are composed of independent agents. All of these agents contribute equally to the final outcome. This results in absence of central or external control.

Summary of key aspects

CASs do not have centralized control rather control is distributed throughout the system. This means that the outcome of a CAS comes from a process of self organization rather than being controlled externally. CAS agents are non linear i.e. a small input can cause a large effect on the outcome ("butterfly effect"). Hence one cannot accurately predict the size of the output based on the input size. CASs are history-dependent i.e. they are shaped by their prior experiences rather than being pre-designed. Also, CASs have many equilibrium points hence the assumption