Chaos theory refers to the behavior of certain systems of motion, such as ocean currents or population growth, to be especially sensitive to tiny changes in starting conditions that result in drastically different outcomes. Unlike what it implies colloquially, chaos theory doesn't mean the world is metaphorically chaotic, nor does it refer to entropy, by which systems naturally tend toward disorder. Chaos theory relies on the uncertainty inherent in measurements, the precision of predictions, and the non-linear behavior of seemingly linear systems.

Before quantum mechanics, chaos theory was the first "weird" idea of physics. Back in 1900, Henri Poincaré thought about the relationship between values at different time points of a system whose general behavior could be accurately predicted, such as a planet in orbit. He realized that we can't ever exactly pinpoint a measurement, like position, speed, or time, because every instrument we could possibly develop would have a limit on its sensitivity. That is, no measurement is infinitely precise.

Poincaré knew that motion is deterministically described by a series of equations that can accurately predict things like where a ball will end up if it is rolled down a ramp. However, he theorized that a tiny difference in initial conditions, based on almost insignificant variations in a measurement like mass, could result in two completely different macroscopic outcomes far, far in the future. This theory was called Dynamical Instability, and later scientists confirmed the veracity of his ideas.

Therefore, chaos theory studies how organized, stable systems cannot always yield meaningful predictions for a much later time, even though short-term behavior more closely follows expectations. In fact, any predictions it does yield might be so wildly divergent that they are no better than guesses. It is counterintuitive that a more precise value would not yield a more precise output.

The snowball effect of a minute change in influential circumstances is referred to as the Butterfly Effect, whereby 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 that demonstrated dynamical instability with actual equations and data.

We cannot infer the initial conditions from later conditions, nor vice versa, in several important systems, such as atmospheric pressure and ocean currents that contribute to weather and climate. This is not merely a real-life scenario, resulting from something like too few thermometers in the ocean. Chaos theory is a verifiable, mathematically consistent theory that shows that sometimes increasingly precise measurements plugged into equations do not yield increasingly precise predictions, but rather such extreme diverging values that they are practically useless.

Some physicists are working on connections between this seeming randomness and large-scale structure, investigating patterns in global climate, mass distribution of galaxies in superclusters, or population variation on a geologic time scale. They hypothesize that on a macroscopic level, certain kinds of organization and consistency have only been made possible through the disorder and inconsistency of chaos theory.