Geography Complexity
by
Steven Manson
  • LAST MODIFIED: 27 July 2016
  • DOI: 10.1093/obo/9780199874002-0139

Introduction

Complexity theory is not a theory in the traditional sense of having a single and readily articulated focus. Instead, it is a collection of related concepts that are shared among disciplines ranging from the arts and humanities through the social, natural, and information sciences; and into health, policy, and management. Given this range of fields, terms including “complexity theory,” “complexity sciences,” and “complex systems” are used with differing views and research methods that form a broad body of work rather than a synthetic theory. We can parse complexity into three streams of research: algorithmic complexity, deterministic complexity, and aggregate complexity (see General Overviews). Algorithmic complexity sees a system in terms of the computational, mathematic, or heuristic processes that can describe the system or mimic system behavior. Deterministic complexity views a system in terms of nonlinear dynamics and chaos theory, most often seeking to specify system dynamics through sets of mathematical equations. Aggregate complexity centers on the ways seemingly complex systems arise from interactions among individual actors or entities, which can range from unthinking ones like atoms or cells through to fully adaptive agents that reason, learn, and plan. We can further examine several areas of complexity research held in common among fields, including how many complexity precepts can be traced back to early and diverse origins; how modeling is a key mode of inquiry in complexity science; how this modeling in turn is manifest in a broad array of methods; issues of scale, emergence, and networks that define complex systems; and finally, how complexity has from the start been interested in understanding equilibrium and change in complex systems.

General Overviews

Complexity science, as befitting a subject with great reach, has a number of useful overviews. Some are explicitly interested in defining complexity science, such as Lissack 2001. Almost all forms of complexity claim to consider complex systems, where a system is a set of entities (or actors or agents or any of a myriad other terms) that interact with each other and an external environment in such a way as to give rise to overall patterns and behaviors. Manson 2001 and O’Sullivan 2004 offer two of the earliest compressive articles on complexity theory in geography, with respective emphases on human geography and geography more generally. Complex systems range in scale from the interactions of atoms and molecules to households in an economy or plants in an ecosystem, to the scale of stars and nebulae in galaxies. Defining the nature and extent of a system is an ever-present challenge in complexity research. These systems are often social and human oriented, and as such, complexity is of interest across the social sciences, as discussed by Byrne 1998, and has extended well into the humanities via postmodernism per Cilliers 1998. Lewin 1992 and Waldrop 1992 offer two very accessible popular volumes spanning early, foundational work in complexity, and serve as a nice complement by the more recent work Mitchell 2009 (cited under Algorithmic Complexity), which offers a more scientifically focused but no less accessible treatment of the field’s evolution.

  • Byrne, D. Complexity Theory and the Social Sciences. London: Routledge, 1998.

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    This volume was among the first substantive engagements between complexity theory and the social sciences, arguing that complexity fits well with the modernist focus of social science and its search for reconciling micro and macro explanations along with structure and agency.

  • Cilliers, P. Complexity and Postmodernism: Understanding Complex Systems. New York: Routledge, 1998.

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    This volume captures many strands of complexity research and views them through the lens of postmodernism and other more recent theoretical turns. It offers a useful counterpoint to other overviews, which often come from the natural sciences or social sciences.

  • Lewin, R. Complexity: Life at the Edge of Chaos. New York: MacMillan, 1992.

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    Lewin offers a broad view on the development of complexity. While this is a general science book and can focus too much on personalities, it is also one of the single best introductions to many of the underlying ideas in complex systems, especially in how it weeds out some of the hyperbole in the claims made of complexity.

  • Lissack, M., ed. Special Issue: What Is Complexity Science? Emergence 3.1 (2001).

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    This special issue of one of the leading journals in complexity science offers a foundational set of articles on what defines complexity theory.

  • Manson, S. M. “Simplifying Complexity: A Review of Complexity Theory.” Geoforum 32.3 (2001): 405–414.

    DOI: 10.1016/S0016-7185(00)00035-XE-mail Citation »

    This article is one of the earliest comprehensive works on complexity science in geography and offers a framework that considers three branches of complexity, namely algorithmic, deterministic, and aggregate complexity.

  • O’Sullivan, D. “Complexity Science and Human Geography.” Transactions of the Institute of British Geographers 29.3 (2004): 282–295.

    DOI: 10.1111/j.0020-2754.2004.00321.xE-mail Citation »

    This article offers an excellent overview of complexity science because it engages braodly and deeply with the gamut of theoretical perspectives used across human geography.

  • Waldrop, M. M. Complexity: The Emerging Science on the Edge of Order and Chaos. New York: Simon and Schuster, 1992.

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    This volume offers one of the earliest broad takes on complexity, focusing on researchers at the Santa Fe Institute, established in 1984 to conduct multidisciplinary research on complex systems, particularly in physics, biology, and economics. This work is in the popular science vein, and as such focuses on personalities (and probably gives too much credit to the Sante Fe Institute for launching complexity science) but is nonetheless an important introduction to complexity.

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