Edgeware - Primer


Why is complexity science relevant now?

The seeds for complexity science have been around for a long time. The founding parents of complexity science were often far ahead of their time. Why is now the right time for complexity science? More specifically, why is this the time for complexity science studies of human organizations?  Turbulence, change, adaptability and connectedness are not new to the late 20th century. There are at least four reasons why now is the time for complexity science:

  1. the limit to the machine metaphor
  2. the coming together of biology and technology
  3. the connections between studies of "micro" and "macro" phenomena,
  4. the apparent compressions of space and time.

The first three reasons will be outlined briefly in this section. The last reason, the compression of space and time, will be described in the next section.

Complexity science is a direct challenge to the dominance of the machine metaphor. Since Newton, the machine metaphor has been used as the lens to make sense of our physical and social worlds, including human organizations. The machine metaphor has been a powerful force in creating manufacturing, medical and organizational advances. However, its limits are now becoming more obvious. It is as if we have collectively learned all we can from the machine metaphor and will continue to use that knowledge where appropriate. But we have more and more instances where the machine metaphor is simply not helpful. For example, it does not explain the emergent aspects of an organization's strategy or the evolution of an industry.  Complexity science, with its focus on emergence, self-organization, inter-dependencies, unpredictability and nonlinearity provides a useful alternative to the machine metaphor.

In addition to changing the metaphor to interpret events, complexity science is gaining momentum because of the coming together of biology and technology. Biologists are using technology to understand biology, for example, in biotechnology. Computer technologists are using biology to create computer software which has some life-like characteristics. Without the technologicali_11.gif (10183 bytes) advancements, due in part from the machine metaphor, we would not be able to replicate nature's fractal forms, or understand the implicit process rules that allow flocks of birds to move as one, or explain the chaotic heart rates of healthy humans. Complexity science is understandable to us now because of both the advances in technology and the increased respect for biological lessons.

Complexity science brings together the two solitudes of micro-studies and macro-analysis. For example, the micro studies of the human genome and the macro studies of evolutionary biology are coming together with complexity science. The lessons from the micro studies are informing the macro analysis and the lessons from the macro studies are informing the micro. This second learning - the macro informing the micro - has been underplayed in our search for applying Newtonian scientific thinking to life. A Newtonian perspective suggests that the parts can explain the whole. Therefore, the quest is to study the parts in greater and greater detail. Complexity science suggests that the whole is not the sum of the parts. Emergent properties of the whole are inexplicable by the parts. In complexity, studies of natural and human systems are explained by both kinds of analysis - micro (or analysis of the parts) and macro (or holistic analysis).



Murray Gell-Mann, a Nobel Prize winner, discovered and named the quark - clearly a study of micro parts. But his journey of discovery into the tiniest parts led him to a path of holistic understanding and an appreciation for ecology. His book "The Quark and the Jaguar" exemplifies this coming together of the appreciation of the micro and macro analysis.  E.O. Wilson, a renowned biologist, argued that we are seeing the confluence of the two major foundations of biology: (1) the molecular basis of life, and (2) the evolutionary basis for human (and ecosystem) behavior. This has profound impacts on both clinical and organizational aspects of health care. Some health care interventions are seen to be context dependent - we cannot explain the micro functioning without understanding the macro context. Community health impacts the well-being of the individuals within the community. Complexity provides us with the opportunity to look at problems with multiple perspectives, studying the micro and macro issues and understanding how they are interdependent.

This section provided some explanations for the complexity science movement in the physical and natural sciences. But there is an additional explanation for its power in social systems - the compression of time and space. The next section describes this seemingly esoteric issue. Some readers may not feel the need to understand the roots of complexity from this perspective and may skip ahead to the section which addresses the paradoxes of complexity.

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All Components of Edgeware Primer Copyright 2000, Brenda J. Zimmerman.
Schulich School of Business, York University, Toronto, Canada.
Permission to copy for educational purposes only. All other rights reserved.