Patterns of Chaos
Overview
For centuries, scientists ignored or avoided chaotic, or nonlinear, data. Real but messy results were often ascribed to experimental error or "noise." In the 1960s, starting with the work of Edward Lorenz (1917- ), new approaches began to reveal the structures, dependencies, and patterns of nonlinear data. These data were found everywhere—in the price of cotton, the rise and fall of animal populations, and the shape of clouds and mountains, for example. Thanks largely to visual approaches that were supported by new, more powerful computers, general principles such as sensitivity to initial conditions and self-similarity were revealed. Chaos theory has given us a deeper understanding of nonlinear systems, both natural and artificial; pointed to solutions in communication, medicine, ecology, and other fields; it has even entered popular culture through images of fractals, the novel and film Jurassic Park, and popular science fiction stories.
Background
Linear equations, such as Newton's Laws, are well behaved. They provide exact answers and can be applied to practical, real-life problems. Much of engineering, for example, is based on linear equations. Nonlinear equations, on the other hand, contain infinite components and cannot be solved exactly; their results are unpredictable. For centuries, most scientists ignored and avoided nonlinear results, which they labeled useless "noise." Ignoring chaos, however, distorts our view of nature and blocks our understanding of many natural phenomena.
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