Leadership, Complex Theory of

Complex leadership theory is the study of the role of leadership in the emergence of ideas among neural-like networks of interactive, interdependent agents. It is premised on complexity theory, a science of emergent dynamics in interactive, adaptive networks.

Complexity theory itself derives from chaos theory, a science of nonlinear behavior in interactive, dynamically active systems (nonlinearity is defined as producing outcomes that are disproportional to inputs). Chaotic systems exhibit complicated, dynamic, deterministically derived order (thus they are distinguished from simple random systems that exhibit no order). Mathematically generated plots of the motion of such systems reveal convoluted—and hauntingly beautiful—patterns, the most famous of which looks like butterfly wings drawn with nonoverlapping, neverending lines (the well-known Lorenz attractor). Chaotic systems are nonlinear and consequently unpredictable; they are so highly changeable that they carry little information about their past or future behaviors. A number of turbulent or potentially turbulent activities, such as weather patterns or fluid flows, are described by the science of chaos.

Before chaos theory, it was assumed that dynamic behavior was either random (hence of little interest) or stable. Stable systems, such as a clock pendulum, repeat themselves eventually; thus their past and their future are predictable. Neither chaos nor stability describes what happens in human systems, however. One can see a bit of chaos and a bit of stability in social dynamics, but one can also identify characteristics (such as adaptability) that are not observed in either chaos or stability. Instead, social systems lie between chaos and stability but exhibit unique characteristics as well. This boundary is the realm of complexity theory.

Like chaotic systems, complex systems are nonlinear, but this nonlinearity manifests predominately as small change events rather than as the dramatic events characteristic of chaos. Like stable systems, complex systems are sufficiently unchanging to carry information about their past and their future; that is, they are predictable. However, information in complex systems is also dynamic (from its chaotic side), so memories and predictability are prone to dissipation. Unlike stable or chaotic systems, complex systems are adaptive, capable of solving problems (computer simulations of complex systems can solve difficult puzzles, for example), and are able to respond quickly and flexibly to satisfy their needs. Perhaps most important for leadership and organization, complex systems are also capable of reinventing themselves in surprising and creative ways.

Complex systems are networks of agents (individuals or groups) who are interactive, interdependent (the actions of one agent affects the fitness of other agents), and moderately coupled (directly affecting a limited number of other agents). The dynamics of complex systems are enhanced by the presence of catalysts, and indeed the system can generate its own catalysts. One important catalytic agent is the leader. Complex systems produce new structures or creativity with bottom-up rather than top-down activities; that is, their strength lies in the dynamics of their followers rather than the control of their leaders. Complex systems are typically coordinated by simple, local, minimally restrictive rules that govern the actions of agents. Because complex systems are adaptive and capable of rapidly producing creative surprises, they are ideally suited for knowledge-producing organizations in hypercompetitive environments such as the computer software industry, research and development systems, and, more topically, higher education institutions that value and promote research or public schools struggling to adapt rapidly and creatively to environmental demands.

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