Complexity Theory

The umbrella term complexity theory is used to explain complex adaptive systems, which are systems composed of a large number of interacting components, or agents, whose aggregate activity is nonlinear (and thus not predictable) and out of which order emerges. The study of “complexity” recognizes that the way the individual parts of a system interact may give us more insight into the entire system than the study of its individual agents, thus the focus turns to the relationships between agents, as opposed to the agents themselves; in this way, the study of complexity may be closer to an approach than it is to a theory.

The study of complex systems and the tools developed to analyze such systems has roots in [Page 560]distributed artificial intelligence, in which entire societies of intelligent agents interact with one another to simulate the interaction of individuals in large populations. The geographer Michael Batty explains that a complex system is based on simple rules or interactions that give rise to unanticipated spatial outcomes. John Holland, a pioneer of complexity and nonlinear science, notes that a complex adaptive system (CAS) is a dynamic network of many agents (which may represent cells, species, individuals, firms, or nations) acting in parallel, constantly acting and reacting to what the other agents are doing. The control of a CAS tends to be highly dispersed and decentralized. If there is to be any coherent behavior in the system, it arises from competition and cooperation among the agents themselves, as opposed to being orchestrated by a higher-level authority or structure. The overall behavior of the system is the result of a large number of decisions made every moment by many individual agents. Thus, order in a complex system is understood as an emergent property of individual interactions at lower levels of interaction.

These fairly general definitions of complexity science bear an uncanny resemblance to those offered by general systems theory (GST), introduced to geography in the 1960s as part of the quantitative revolution to provide a framework in which to understand and theorize various disparate strands of work. GST was based on the work of Ludwig von Bertallanfy, who claimed that GST allowed for the transfer of principles discovered in one context to other situations. Although dismissed by Nicholas Chisholm in 1967 as an “irrelevant distraction,” the themes of GST continue to haunt geography, coming to the forefront once again in the dialogue between Jonathon Raper, David Livingston, and Doreen Massey in the late 1990s, as they discussed what Massey (1999) called the “commonalities between physical geography and human geography in emerging ways of conceptualizing space, time, and space-time” (p. 261). David O'Sullivan joined this conversation in 2004, when he brought it into the complexity arena, noting that “research in the complexity modus operandi can be useful, which brings us back to the potential for intra-disciplinary discussion provided by the complexity venture” (p. 283). The distinguishing element between general systems theory and complexity theory is the reconceptualized understanding of equilibria in complexity theory. Chaos theory, and later complexity theory, allows for small shifts in system parameters to produce positive feedback loops, pushing the system to new levels of equilibria as opposed to dampening it through negative feedback.

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