An Introduction to Complexity Theory
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
Between 1500–1700, there was an important and dramatic shift in the way that people in Europe perceived and understood the world. Through the works of thinkers such as Francis Bacon, Galileo Galilei, Johannes Kepler, Rene Descartes, and Isaac Newton, the Western world underwent a scientific revolution. This resulted in a shift from a worldview governed by the Church and Christian theology and ethics, to that of an inanimate, machine-like material world governed by natural forces and exact mathematical rules (Capra and Luisi, 2014a).
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).
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
Complexity Theory and its related concepts emerged in the mid-late 20th century across multiple disciplines, including the work of Prigogine and his study on dissipative structures in non-equilibrium thermodynamics, Lorenz in his study of weather systems and non-linear causal pathways (i.e. the butterfly effect), Chaos theory and its new branch of mathematics, as well as evolutionary thinking informed by Lamarck’s perspectives on learning and adaptation (Schneider and Somers, 2006).
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.
Take for instance, cells; while they can act as independent agents, they can also congregate and self-organize to form more complex, multicellular life. Individual human beings, themselves CAS, can also congregate and self-organize in the form of groups, communities, and organizations (Capra and Luisi 2014a).
2. CAS benefit from diversity.
A diversity of components in CAS is essential in providing a source of information, novelty, and innovation as the system evolves and adapts to its environment. This can be demonstrated in the role that biodiversity plays in the resilience and adaptability forest ecosystems (Zimmerman, Lindberg and Plsek, 2001).
3. CAS exhibit distributed, rather than centralized control.
In CAS, there is no central mode of control; it is distributed throughout the system through its individual agents. This allows the system to react and adapt to a faster and much greater extent than if there was only one source of control (Zimmerman, Lindberg and Plsek, 2001).
4. CAS exhibit emergent outcomes and behaviors.
The outcomes of CAS emerge from a process of self-organization, rather than that of external design and control. This emergent outcome is a result of the interactions and synergies between individual agents, and cannot be predicted by studying the properties of the individual component alone. For instance, communities have often been observed to self-organize and respond in a coordinated fashion, without a formal leader or directive, in response to major natural disasters. Studying the skills and resources available to each member could not have predicted this emergent group behavior and outcome. (Zimmerman, Lindberg and Plsek, 2001).
5. CAS emphasize the quality of relationships of between parts rather than the properties of the parts themselves.
The strengths of CAS, principles (3) and (4) especially, are highly dependent on the relationships between individual agents and their ability to self organize. For this reason, the quality and strength of relationships between individual agents will often predict the success of a CAS, more than an analysis of the traits of the individual agents can (Zimmerman, Lindberg and Plsek, 2001).
6. The behaviors and outputs of CAS can be non-linear, and highly dependent on its history, context, and initial conditions.
CAS exhibit nonlinear behavior, meaning that the size of the outcome may not be related to the size or intended direction of the input. Furthermore, the characteristics of the CAS are highly dependent on its context, history, and initial conditions; an intervention or strategy that worked for one organization may not for another, and its outcomes are bounded by the history of how the organization came to be (Zimmerman, Lindberg and Plsek, 2001).
7. CAS thrive at the edge of chaos.
CAS thrive in areas of bounded instability, on the boundary between order and chaos. Here, there is enough stability to have repetitive and predictive elements in the system, but just enough instability to generate novelty without creating anarchy and dispersal (Zimmerman, Lindberg and Plsek, 2001).
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.
Citations
Capra, F., & Luisi, P. (2014a). The Newtonian world-machine. In The Systems View of Life: A Unifying Vision (pp. 19–34). Cambridge: Cambridge University Press. doi:10.1017/CBO9780511895555.004
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. https://doi.org/10.1016/j.leaqua.2006.04.006
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. https://doi.org/10.1016/j.leaqua.2007.04.002
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.
