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Chaos theory
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Chaos theory

In mathematics and physics, chaos theory deals with the behaviour of certain nonlinear dynamical systems that (under certain conditions) exhibit the phenomenon known as chaos, most famously characterised by sensitivity to initial conditions (see butterfly effect). Examples of such systems include the atmosphere, the solar system, plate tectonics, turbulent fluids, economies, and population growth.

Systems that exhibit mathematical chaos are deterministic and thus orderly in some sense; this technical use of the word chaos is at odds with common parlance, which suggests complete disorder. See the article on chaos for a discussion of the origin of the word in mythology, and other uses. When we say that chaos theory studies deterministic systems, it is necessary to mention a related field of physics called quantum chaos theory that studies non-deterministic systems following the laws of quantum mechanics.

Table of contents
1 Description of the theory
2 History
3 Mathematical theory
4 Other examples of chaotic systems
5 See also
6 References
7 External links

Description of the theory

A non-linear dynamical system can in general exhibit one or more of the following types of behaviour:

The type of behaviour may depend on the initial state of the system and the values of its parameters, if any.

The importance of chaos theory can be illustrated by the following observations:

Chaotic motion

The most famous type of behaviour is chaotic motion, a non-periodic complex motion which has given name to the theory. In order to classify the behaviour of a system as chaotic, the system must be:

Sensitivity on the initial conditions means that two such systems with however small a difference in their initial state eventually will end up with a finite difference between their states (however, two deterministic systems with identical initial conditions will remain identical).

An example of such sensitivity is the well-known butterfly effect, whereby the flapping of a butterfly's wings produces tiny changes in the atmosphere which over the course of time cause it to diverge from what it would have been and potentially cause something as dramatic as a tornado to occur. Other commonly-known examples of chaotic motion are the mixing of colored dyes and airflow turbulence.

Transitivity means that application of the transformation on any given Interval I1 stretches it until it overlaps with any other given Interval I2.

The fourth condition means that for any point in the system and any real number ε > 0 there is another point with distance d ≤ ε which is located on a periodic orbit.

Strange attractors

One way of visualizing chaotic motion, or indeed any type of motion, is to make a phase diagram of the motion. In such a diagram time is implicit and each axis represents one dimension of the state. For instance, a system at rest will be plotted as a point and a system in periodic motion will be plotted as a simple closed curve.

A phase diagram for a given system may depend on the initial state of the system (as well as on a set of parameters), but often phase diagrams reveal that the system ends up doing the same motion for all initial states in a region around the motion, almost as though the system is attracted to that motion. Such attractive motion is fittingly called an attractor for the system and is very common for forced dissipative systems.

While most of the motion types mentioned above give rise to very simple attractors, such as points and circle-like curves called limit cycles, chaotic motion gives rise to what are known as strange attractors, attractors that can have great detail and complexity. For instance, a simple three-dimensional model of the Lorenz weather system gives rise to the famous Lorenz attractor. The Lorenz attractor is perhaps one of the best-known chaotic system diagrams, probably because not only was it one of the first, but it is one of the most complex and as such gives rise to a very interesting pattern which looks like the eyes of an owl.

Strange attractors have fractal structure.

History

The roots of chaos theory date back to about 1900, in the studies of Henri Poincaré on the problem of the motion of three objects in mutual gravitational attraction, the so-called three-body problem. Poincaré found that there can be orbits which are nonperiodic, and yet not forever increasing nor approaching a fixed point. Later studies, also on the topic of nonlinear differential equations, were carried out by G.D. Birkhoff, A.N. Kolmogorov, M.L. Cartwright, J.E. Littlewood, and Stephen Smale. Except for Smale, who was perhaps the first pure mathematician to study nonlinear dynamics, these studies were all directly inspired by physics: the three-body problem in the case of Birkhoff, turbulence and astronomical problems in the case of Kolmogorov, and radio engineering in the case of Cartwright and Littlewood. Although chaotic planetary motion had not been observed, turbulence in fluid motion and nonperiodic oscillation in radio circuits had been encountered by experimentalists, without the benefit of a theory to explain what they were seeing.

Chaos theory progressed more rapidly after mid-century, when it first became evident for some scientists that linear theory, the prevailing system theory at that time, simply could not explain the observed behaviour of certain experiments like that of the logistic map. The main catalyst for the development of chaos theory was the electronic computer. Much of the mathematics of chaos theory involves the repeated iteration of simple mathematical formulas, which would be impractical to do by hand. Moore's law and the availability of cheaper computers has greatly increased the extent of chaos theory. Currently, chaos theory continues to be a very active area of research.

An early pioneer of the theory was Edward Lorenz whose interest in chaos came about accidentally through his work on weather prediction in 1961. Lorenz was using a basic computer to run his weather simulation. He wanted to see a sequence of data again and to save time he started the simulation in the middle of its course. He was able to do this by entering a printout of the data corresponding to conditions in the middle of his simulation which he had calculated last time.

To his surprise the weather that the machine began to predict was completely different to the weather calculated before. Lorenz tracked this down to only bothering to enter 3-digit numbers in to the simulation, whereas the computer had last time worked with 5-digit numbers. This difference is tiny and the consensus at the time would have been that it should have had practically no effect. However Lorenz had discovered that small changes in initial conditions produced large changes in the long-term outcome.

The term chaos as used in mathematics was coined by the applied mathematician James A. Yorke.

Mathematical theory

Mathematicians have devised many additional ways to make quantitative statements about chaotic systems. These include:

Minimum complexity of a chaotic system

Many simple systems can also produce chaos without relying on
partial differential equations, such as the logistic equation, which describes population growth over time.

Even discrete systems, such as cellular automata, can heavily depend on initial conditions. Stephen Wolfram has investigated a cellular automaton with this property, termed by him rule 30.

Other examples of chaotic systems

See also

References

Textbooks and technical works

Semitechnical and popular works

External links