An Introduction to Complexity Theory

What it is, what it replaces, and why it’s important.

Complexity Theory allows us to better understand systems as diverse as cells, human beings, forest ecosystems, and organizations, that are only partially understood by traditional scientific methods (Zimmerman et al. 2001). While it represents a relatively nascent field of study, it spans across a wide variety of disciplines in the physical, biological, and social sciences, and has profound implications for the way we think about and act within the world (Schneider & Somers, 2006).

This is especially important in the study of organizations, organizational change, and leadership, where complexity theory can offer insights into how organizations become more sustainable, adaptive, and innovative (Uhl-Bien et al., 2007). In the following sections, we will examine the origins of the mechanical, bureaucratic paradigms of organizations and leadership, the development of complexity science, and the implications that a paradigm shift from the former to the latter has on the study and leadership of organizations.

The Mechanical Worldview

Rene Descartes (1596–1650)

Rene Descartes — a brilliant philosopher, mathematician, and scientist — paved the way into this new way of thinking. His method of analytically breaking down problems into smaller components, and solving and organizing them in a logical fashion has become an essential characteristic of modern scientific thought. Descartes believed that the universe could be described and understood in terms of exact mathematical laws and relationships; this vision was reflected in his landmark development of analytic geometry, or the use of algebra to describe geometric shapes (Capra and Luisi, 2014a).

Isaac Newton (1642–1727)

This conceptual framework, developed by Descartes in the 17th century, was made complete by the genius of Isaac Newton, who developed a comprehensive system of mathematics that would synthesize and validate the works of Copernicus, Kepler, Galileo, and Descartes. The Newtonian worldview, presented in 1687 in the Mathematical Principles of Natural Philiosophy or The Principia, synthesized both the systematic observation of natural phenomena emphasized by Bacon, as well as its mathematical and first-principles analysis advocated by Descartes. The mathematics and worldview of Newtonian physics was applied with tremendous success to a wide variety of scientific and technological endeavours throughout the 18th-19th centuries, and generated enormous enthusiasm from scientists and the public alike. It also laid the foundations for the Industrial revolution; with the view of the universe as an inanimate, mechanical system, there was now “scientific” justification for the manipulation and exploitation of nature. This revolution had a profound impact on Western culture and development over the next 300 years, and to this day — even with much of Newtonian physics having been put into disarray with the theory of relativity and quantum theory — the assumptions and perspectives of the Newtonian worldview still dominate our metaphors and mental models across all domains (Capra and Luisi, 2014a).

In management and organizational thinking, the machine metaphor became especially prominent during the Industrial revolution. Work became specialized, siloed, and routinized in an effort to strive towards an increasingly precise, regular, reliable, and efficient vision of organizations. In this perspective, organizations are created and owned by external parties, its structures and goals are designed by management, and its policies are imposed in a top-down fashion through a bureaucracy. Max Weber — a 19th century sociologist — was one of the first observers to see the link between mechanization and bureaucratization of industry, and the routinization of human life, labour, and the erosion of meaning and purpose in work (Capra and Luisi, 2014b). Today, in the post-industrial knowledge era, the limitations and disadvantages of this mechanistic approach to organizations have become glaringly obvious, and require us to adopt a new theoretical perspective and set of conceptual tools (Uhl-Bien et al., 2007).

Complexity Theory — Origins, Principles, and Implications

While this multiplicity of influences presents a challenge in understanding its origins, complexity theory can also be understood generally as the study of complex adaptive systems (CAS). The word “complex” implies diversity, through a great number, and wide variety of interdependent, yet autonomous parts. “Adaptive” refers to the system’s ability to alter, change, and learn from past experiences. The “system” portion refers to a set of connected, interdependent parts; a network. While there are a great number of CAS existing at different scales, complexity theory reveals that there are common, interrelated principles which can be observed across all CAS (Zimmerman, Lindberg and Plsek, 2001).

1. CAS are embedded and nested within other CAS.

2. CAS benefit from diversity.

3. CAS exhibit distributed, rather than centralized control.

4. CAS exhibit emergent outcomes and behaviors.

5. CAS emphasize the quality of relationships of between parts rather than the properties of the parts themselves.

6. The behaviors and outputs of CAS can be non-linear, and highly dependent on its history, context, and initial conditions.

7. CAS thrive at the edge of chaos.

Reframing our understanding of human organizations as CAS rather than machines, tracing the initial conditions and history of an organization, and examining the qualities of relationships that individuals within the organization share, can allow us to better understand how organizational growth, learning, and innovation take place, and how organizational successes (and failures) may be replicated (Schneider and Somers, 2006). Complexity theory provides us with a powerful and flexible set of metaphors, mental models, and strategies that can guide our inquiry of organizations in settings as diverse as healthcare, business, and community-building (Zimmerman, Lindberg and Plsek, 2001).

Over the coming weeks, I will be undertaking this inquiry by examining an organization called Community Food Centres Canada and comparing its origins and development to the predictions of complexity theory. Stay tuned for an adventure through the world of community-building, social innovation, and complexity.


Capra, F., & Luisi, P. (2014b). Mechanistic social thought. In The Systems View of Life: A Unifying Vision (pp. 45–60). Cambridge: Cambridge University Press. doi:10.1017/CBO9780511895555.006

Schneider, M., & Somers, M. (2006). Organizations as complex adaptive systems: Implications of Complexity Theory for leadership research. The Leadership Quarterly, 17(4), 351–365.

Uhl-Bien, M., Marion, R., & McKelvey, B. (2007). Complexity Leadership Theory: Shifting leadership from the industrial age to the knowledge era. The Leadership Quarterly, 18(4), 298–318.

Zimmerman, B., Lindberg, C. and Plsek, P. (2001). A Complexity Science Primer. In Edgeware: Insights from Complexity Science for Health Care Leaders (pp.3–20). Irving, Tex.: VHA Inc.

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