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One of the most important concepts to understand within the field of chaos theory, the “butterfly effect”—scientifically known as “sensitive dependence on initial conditions”—describes “the way small scales intertwined with large” (23). For example, small changes in weather in one region impact the forecast across a continent. Edward Lorenz determined that the butterfly effect was not only useful for understanding these changing patterns but also “necessary.” That is, without the workings of the butterfly effect, the planet would not have the rich and varied weather patterns that produce different species of flora and fauna. The flourishing of nature itself depends upon it.
The butterfly effect applies to other areas of chaos science too. In Mitchell Fiegenbaum’s theories of universality, for example, the “tiny changes in certain features [that] lead to remarkable changes in overall behavior” (178) are crucial to interpreting the equations across different scales. Likewise, within biological and ecological systems, the butterfly effect is almost always in play. It operates the same way everywhere: “[T]he laws of pattern formation are universal” (311). Ultimately, as the author emphasizes, “[s]ensitive dependence on initial conditions serves not to destroy but to create” (311), contrary to what popular disaster movies might imply. For example, snowflakes, whose patterns result from myriad and minute deviations in atmosphere and might well be infinite, are certainly objects of beauty and fascination.
Part of the Second Law of Thermodynamics, entropy indicates that everything in the universe tends toward disorder. It has been interpreted to mean that “[t]he universe is a one-way street” (308) wherein all leads to chaos rather than to pattern. Chaos science calls this interpretation into question. With regard to entropy’s application to heat and energy, the Law holds; however, when used to describe other systems—economic, social, political, etc.—it breaks down: “Somehow, after all, as the universe ebbs toward its final equilibrium in the featureless heat bath of maximum entropy, it manages to create interesting structures” (308). Thus, using entropy as a metaphorical measure of the disorder within a given system is difficult at best and inappropriate at worst. Chaos science reveals how nature tends toward patterns within that disorder. Entropy is another concept about which chaos prompts new perspectives.
Coined by Benoit Mandelbrot, the term “fractals” initially described his insights based on geometry: Nature does not develop in smooth and easily measurable Euclidean shapes; rather, it resolves itself in rough and irregular ways. Thus, fractal geometry measures the potentially infinite crags and crannies that form around the boundary of finite area—as in the coast of Great Britain or the branching arms of a snowflake: “In the mind’s eye, a fractal is a way of seeing infinity” (98). Mandelbrot uses mathematics and models to demonstrate his innovative ideas, and “[f]ractional dimension becomes a way of measuring qualities that otherwise have no clear definition: the degree of roughness or brokenness or irregularity in an object” (98). This insight eventually led to the Mandelbrot set, which “seems more fractal than fractals, so rich is its complication across scales” (221). Using Julia sets—mathematical models that defy the rules of Euclidean geometry—the Mandelbrot set reveals that each island branching off of the original is distinct and unique.
In addition, fractals illuminate the “Humpty-Dumpty Effect”: Once something is broken, it can never quite be put back together because “surfaces in contact do not touch everywhere. The bumpiness at all scales prevents that” (106). Once a teacup is broken, even when glued back together, it will have gaps, whether visible or not. Thus, fractals also describe the chaotic nature of surfaces.
One of the most important contributions made by the Dynamical Systems Collective out of the University of California, Santa Cruz, was information theory. Information itself describes bits transmitted across electronic lines, which now include the internet; it does not necessarily imply meaning or factual knowledge. Information connects the small scales and the large within a system, and as a system tends toward chaos, it creates more information. When these scientists “spoke of systems generating information, they thought about the spontaneous generation of pattern in the world” (261)—that is, order within disorder.
In addition, information theory links to other ideas within chaos science: “[T]he channel transmitting the information upward is the strange attractor, magnifying the initial randomness just as the Butterfly Effect magnifies small uncertainties into large-scale weather patterns” (261). As the author emphasizes in both the original book and the Afterword added in 2008, chaos itself could be considered information.
The strange attractor exists in phase space, as do other attractors, but the strange attractor behaves in a fractal manner. That is, points within the space may be close together at times and far apart at other times. The strange attractor illuminates chaotic behavior within a particular system in phase space. Phase space describes “the complete state of knowledge about a dynamical system at a single instant in time [when it] collapses to a point. That point is the dynamical system—at that instant” (134). In the next instant, that system fluctuates, and then it changes again. The strange attractor reveals the pattern within the chaos.
Unlike normal attractors, the strange attractor is fractal in nature: The points never converge, and the loops (as in an orbit) never cross paths. The strange attractor again reveals order within disorder and is “the trajectory toward which other trajectories converge” (150). In the early years of chaos theory, “the recognition of strange attractors fed the revolution […] by giving numerical explorers a clear program to carry out. They looked for strange attractors everywhere, wherever nature seemed to be behaving randomly” (152). Science later used strange attractors to explain systems from weather to the stock market to epidemiology.
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