Butterfly Effect Essay

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The butterfly effect is colloquial language used to describe the idea within chaos theory called sensitive dependence on initial conditions. This principle became the cornerstone to the science of chaos theory. Scientists interested in the butterfly effect and chaos theory are concerned about initial conditions, determinism, uncertainty of measurement, dynamic instabilities, manifestations of chaos, and perceiving new order.

It is because of the constant butterfly effect that the world’s weather forecasts remain very imperfect. They are accurate for a few days, but beyond six or seven they become speculative. This is the butterfly effect in action. Tiny pieces of weather cascade into bigger and bigger effects. Likewise, mistakes and uncertainties in calculations create false assertions that, when expanded millions of times, result in false predictions.

Chaos theory, originating within mathematics and meteorology, posits that all systems, including complex and seemingly chaotic ones, are determined by underlying order, and tiny movements in a system can build to large events (the butterfly effect). The term chaos theory, however, can be misleading because the theory attempts to describe phenomena that appear to be random, periodic, and chaotic, but actually evolve in complex systems and interactions among systems according to exact and predictable rules.

Chaos theory is sometimes presented as esoteric knowledge, but many researchers illustrate the principles with very simple examples like swinging pendulums, bouncing balls, and pinball machines. Slightly different inputs can determine very different results.

Chaos theory is both a challenge to and an extension of the deterministic view of science. Determinism is historically a central belief in modern science. Newton thought scientists could know the future of the physical universe by measuring initial conditions and applying the physical laws determining its development. Newtonian science assumes that more precise measurements of a phenomenon will yield more accurate predictions about future events. The assumption was that an inability to make accurate predictions was related to problems in making accurate enough measurements. Discoveries in astronomical science, however, reveal that tiny errors in the measurement of initial states yield large and unpredictable outcomes. Two or more nearly identical states can yield vastly different outcomes. Even if measurements could be made thousands of times more accurately, the uncertainty of the outcome doesn’t decrease along with the refined starting measurement.

Edward Lorenz arrived at his chaos observations during his attempts to to create better meteorological predictions with early computers, which had the capacity to conduct vast mathematical problems with slight iterations. He observed in his models that changing the values in numeric systems at the level of the thousandths decimal led to different weather phenomena after many iterations. Slight differences in initial conditions created large differences in outcome. At the time, it was not believed such small differences were significant. Yet, the instruments used to measure weather were not even as accurate as Lorenz’s hypothetical models. Because perfect measurements of initial conditions, especially in large systems with many variables, are impossible, predictability is extremely problematic.

Initial conditions are also problematic to define, because one researcher’s initial conditions may be another’s conditions of midstream. Additionally, measuring initial conditions in any given moment will not give a full picture of the current processes, directions, and causes of the initial condition.

Sensitive dependence is also empirically difficult to measure, as it implies more than a relationship between two states. It implies that there are deterministic, dynamical systems. Dynamical systems have moments of near balance and instability, and small influences can have large consequences. Initial differences of one unit may increase a hundred times in one system and a million times in another, and the variable of time will differentiate systems even more.

Chaos theory maintains that things that appear chaotic are actually not chaotic at all. The central goal in chaos theory is development of a science and perception that can detect a pattern in a seemingly chaotic system. According to James Gleick, author of Chaos: Making a New Science, chaos theory is “a revolution not of technology, like the laser revolution or the computer revolution, but a revolution of ideas.” Chaos is actually orderly disorder, and the task is to perceive the order.

Many physicists claim chaos theory is about a description of process rather than a being or state. Chaos theory breaks across academic disciplines because it is interested in the holistic nature of systems. Mathematicians, physicists, chemists, biologists, ecologists, and economists are all interested in irregularity. Advocates of chaos theory say chaos theory and the recognition of the butterfly effect have turned back the reductionistic trend in science. The environmentally oriented philosophy known as deep ecology embraces the butterfly effect as it illustrates well the fragile dynamic relationship between seemingly disparate elements.


  1. James Gleick, Chaos: Making a New Science (Viking, 1997);
  2. Nina Hall, Exploring Chaos: A Guide to the New Science of Disorder (W.W. Norton, 1991);
  3. Douglas Kiel and Euel W. Elliott, Chaos Theory in the Social Sciences: Foundations and Applications (University of Michigan Press, 1997);
  4. Edward Lorenz, The Essence of Chaos (University of Washington Press, 1993);
  5. Garnett P. Williams, Chaos Theory Tamed (National Academies Press, 1997).

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