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Chaos theory studies how small changes in starting conditions can lead to drastically different outcomes in motion systems. It is based on uncertainty in measurements and nonlinear behavior. Henri Poincaré’s theory of dynamic instability was confirmed by later scientists. Chaos theory shows that precise measurements do not always produce precise predictions. The butterfly effect refers to the snowball effect of minimal changes. Chaos theory is being studied in relation to global climate patterns, galaxy mass distribution, and population variation.
Chaos theory refers to the behavior of some motion systems, such as ocean currents or population growth, to be especially sensitive to small changes in starting conditions that result in drastically different outcomes. Contrary to what it colloquially implies, chaos theory does not mean that the world is metaphorically chaotic, nor does it refer to entropy, whereby systems naturally tend toward disorder. Chaos theory is based on the uncertainty inherent in measurements, the accuracy of predictions, and the nonlinear behavior of seemingly linear systems.
Before quantum mechanics, chaos theory was the first “strange” idea in physics. In 1900, Henri Poincaré thought about the relationship between values at different points in time of a system whose general behavior could be accurately predicted, such as an orbiting planet. He realized that a measurement, such as position, speed or time, can never be pinpointed exactly because any tool that could be developed would have a limit to its sensitivity. That is, no measurement is infinitely precise.
Poincaré knew that motion is described deterministically by a set of equations that can accurately predict things like where a ball will end up if it is rolled down a ramp. He theorized, however, that a small difference in initial conditions, based on almost insignificant changes in a measurement like mass, could lead to two completely different macroscopic results in the far, far future. This theory was called dynamic instability, and later scientists confirmed the veracity of his ideas.
Chaos theory, therefore, studies how stable, organized systems cannot always provide meaningful predictions for much later time, even if short-term behavior more closely follows expectations. In fact, any predictions it produces could be so wildly divergent that it isn’t a best-case scenario. It is counterintuitive that a more precise value would not produce a more precise output.
The snowball effect of a minimal change in influential circumstances is referred to as the butterfly effect. This metaphor suggests that a butterfly flapping its wings, an almost imperceptible influence, could contribute to the development of a hurricane on the other side of the globe. Edward Lorenz did the first computer simulations in the 1960s which demonstrated dynamic buckling with real equations and data.
Initial conditions cannot be inferred from subsequent conditions, nor vice versa, in several important systems, such as atmospheric pressure and ocean currents that contribute to weather and climate. This is simply not a real-life scenario, resulting from something like too few thermometers in the ocean. Chaos theory is a testable and mathematically consistent theory that shows that sometimes increasingly precise measurements plugged into equations do not produce ever more precise predictions, but rather divergent values so extreme that they are practically useless.
Some physicists are working on the connections between this apparent randomness and the large-scale structure. They are studying global climate patterns, the mass distribution of galaxies in superclusters, and population variation over a geological time scale. They posit that, at the macroscopic level, certain types of organization and consistency were only possible through the disorder and incoherence of chaos theory.
