Chaos and Complexity Theory

Science defines chaos theory along technical lines as the study of unstable, or a periodic, behavior in complex, nonlinear, and dynamic systems accounted for by relatively few rules. In other disciplines, chaos can be defined more simply as the structures and regularities underpinning irregular systems or behaviors. Such systems unfold over time in a manner different from predictably linear, cause-andeffect change. These systems tend not to repeat themselves exactly. An example is the phenomenon of being deafened by the screech of positive feedback from amplified music. Tiny adjustments may result in clearer amplification, or in making the screech louder, so that future actions cannot be plotted from past behavior. In nonlinear systems minuscule variations at the input stage, in what chaos terms “sensitivity to initial conditions,” may lead to disproportionately different outcomes later.

Public relations features many examples of perceptions of an organization altering, and crises arising, with dramatic suddenness through seemingly insignificant events. The Pentium chip's infinitesimal mathematical errors stirred up enough of an initial Internet storm among specialists, and then through to the mainstream media, that Intel lost substantial share value. As a postcrisis rule organizational systems do revert to order, whether a return to the status quo, as Intel did by gradually restoring investor confidence and a high share value, or to a revised status quo, as Enron did by crashing from its US$60 billion book value in the wake of 24 days of Wall Street Journal reporting. Ian I. Mitroff has generalized from his study of crisis management that “signals go off all the time in organizations, but because there is no one there to recognize them or record them, or attend to them, then for all practical reasons the signals are ‘not heard’” (2001, p. 109). His associated four ubiquitous signals of crisis—internal and external people, and internal and external technical—suggest chaos-inspired future research into their finer gradations. By highlighting the potential power of small regularities, akin to those four ubiquitous signals at the outset, chaos theory holds out the promise of identifying simplified sets of interactions, or consistent patterns of neglect, before issues escalate into crises.

While running computer weather predictions, meteorologist Edward Lorenz discovered that trivial rounding errors, at the outset of an experiment, lead to huge variations in long-range forecasts. In 1973, Lorenz posed his discovery in the memorable question “Does the Flap of a Butterfly's Wings in Brazil Set off a Tornado in Texas?” (1993, p. 181). Despite the intractable problems his question raised, it catalyzed activity across scientific and nonscientific disciplines alike. It loosened the meaning of chaos from its common language use, as “completely unstructured confusion,” to allow for the more technically influenced sense of “structured, and therefore only apparently random, movements between order and disorder” (1993, p. 181) The so-called butterfly effect influenced the study of dramatic change arising out of tiny variations, from biology and physics to business and sociology, and it demonstrated both visually, since one of Lorenz's graphs took the shape of a butterfly, and practically, that computers could generate virtual research capable of informing empiric studies.

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