Chaos

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Introduction¹

Introduction to Chaos (Cornell Math Explorers’ Club)
Chaos Theory for Beginners; An Introduction (Abarim Publications)

Dictionary

chaos : the inherent unpredictability in the behavior of a complex natural system — Webster See also OneLook, Free Dictionary, Wiktionary, Urban Dictionary

Chaos (CompLexicon, Exploratorium)

Thesaurus

Roget’s II (Thesaurus.com), Merriam-Webster Thesaurus, Visuwords

Encyclopedia

Chaos may refer to any state of confusion or disorder. — Wikipedia

Portal

Chaos & Fractals (Larry Bradley)

Search

WolframAlpha

Theory

Chaos Theory is the field of study in mathematics that studies the behavior and condition of dynamical systems that are highly sensitive to initial conditions—a response popularly referred to as the butterfly effect. Small differences in initial conditions (such as those due to rounding errors in numerical computation) yield widely diverging outcomes for such dynamical systems, rendering long-term prediction impossible in general. This happens even though these systems are deterministic, meaning that their future behavior is fully determined by their initial conditions, with no random elements involved. In other words, the deterministic nature of these systems does not make them predictable. This behavior is known as deterministic chaos, or simply chaos. The theory was summarized by Edward Lorenz as:

Chaos: When the present determines the future, but the approximate present does not approximately determine the future.

Chaotic behavior exists in many natural systems, such as weather and climate. This behavior can be studied through analysis of a chaotic mathematical model, or through analytical techniques such as recurrence plots and Poincaré maps. Chaos theory has applications in several disciplines, including meteorology, sociology, physics, computer science, engineering, economics, biology, and philosophy. A traffic model was developed showing that the system dynamics can pass under certain conditions to chaos. — Wikipedia

Butterfly Effect is the sensitive dependence on initial conditions in which a small change in one state of a deterministic nonlinear system can result in large differences in a later state.

The term, coined by Edward Lorenz, is derived from the metaphorical example of the details of a tornado (the exact time of formation, the exact path taken) being influenced by minor perturbations such as the flapping of the wings of a distant butterfly several weeks earlier. Lorenz discovered the effect when he observed that runs of his weather model with initial condition data that was rounded in a seemingly inconsequential manner would fail to reproduce the results of runs with the unrounded initial condition data. A very small change in initial conditions had created a significantly different outcome.

Though Lorenz gave a name to the phenomenon, the idea that small causes may have large effects in general and in weather specifically was earlier recognized by French mathematician and engineer Henri Poincaré and American mathematician and philosopher Norbert Wiener. Edward Lorenz’s work placed the concept of instability of the earth’s atmosphere onto a quantitative base and linked the concept of instability to the properties of large classes of dynamic systems which are undergoing nonlinear dynamics and deterministic chaos. — Wikipedia