Science Fair Project Encyclopedia
Chaos theory, in mathematics and physics, deals with the behavior 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.
Description of the theory
A non-linear dynamical system can, in general, exhibit one or more of the following types of behavior:
- forever at rest
- forever expanding (only for unbounded systems)
- periodic motion
- quasi-periodic motion
- chaotic motion
The type of behavior may depend on the initial state of the system and the values of its parameters, if any.
The most famous type of behavior is chaotic motion, a non-periodic complex motion which has given name to the theory. In order to classify the behavior of a system as chaotic, the system must exhibit the following properties:
- it must be bounded
- it must be sensitive to initial conditions
- it must be transitive
- its periodic orbits must be dense
Sensitivity to 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 so-called "butterfly effect", whereby the flapping of a butterfly's wings is imagined to create 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.
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 wings of a butterfly. Another such attractor is the Rössler Map, it experiences period-two doubling route to chaos, like the logistic map.
Strange attractors have fractal structure.
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 behavior 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. Electronic computers made these repeated calculations practical. One of the earliest electronic digital computers, ENIAC, was used to run simple weather forecasting models.
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, a Royal McBee, 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 the computer printout. The printout rounded variables off to a 3-digit number, but the computer 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.
Moore's law and the availability of cheaper computers broadens the applicability of chaos theory. Currently, chaos theory continues to be a very active area of research.
Chaos theory has been used (and misused) in popular science fiction books and movies, according to which its importance can be illustrated by the following observations:
- In popular terms, a linear system is exactly equal to the sum of its parts, whereas a non-linear system can be more than the sum of its parts. This means that in order to study and understand the behavior of a non-linear system one needs in principle to study the system as a whole and not just its parts in isolation.
- It has been said that if the universe is an elephant, then linear theory can only be used to describe the last molecule in the tail of the elephant and chaos theory must be used to understand the rest. Or, in other words, linear systems in nature are relatively rare, and almost all interesting real-world systems are described by non-linear systems.
Everyday predictable non-chaotic deterministic systems (like good billiard tables) might seem boring because, in most cases, scientists discovered exactly how they work centuries ago, and nobody who knows how they work will ever be very surprised by them. A non-chaotic system is generally better understood than a chaotic system and therefore perhaps less interesting as a plot device in science fiction. For example, chaos theory is used as a plot device in the novel Jurassic Park or the book that best shows it, The Sound of Thunder . It has also entered into popular culture as reflected in video games such as Tom Clancy's Splinter Cell: Chaos Theory and with the "chaos" emeralds in Sonic the Hedgehog.
Chaos Washing Machines
Goldstar Co. created a "chaotic washing machine" in 1993. It was the world's first consumer product to exploit "chaos theory", which holds that there are identifiable and predictable movements in nonlinear systems. This washing machine is supposed to produce cleaner and less tangled clothes. The key to the chaotic motion is a small pulsator (which stirs the water) that rises and falls randomly as the main pulsator rotates. When released to the world market, it was expected to push Goldstar's share of the annual 1.5-million-unit washing machine market to 40% in 1993, compared to 39% for Samsung and 21% for Daewoo (Goldstar's major competitors). However, marketing is fierce in South Korea and Daewoo argues that Goldstar "was not the first" to commercialize chaos theory. Daewoo also built a "bubble machine" in 1990 which also used chaos theory that was the result of "fuzzy logic circuits." Fuzzy circuits make choices between zero and one, and between true and false. These factors control the amount of bubbles, the turbulence of the machine, and even the wobble of the machine.
Criticisms of chaos theory
One criticism of chaos theory is that it focuses on behavior that is of peripheral importance in real-world engineering. For example, passive electrical circuits are linear. Most signals used in signal theory have finite energy and are linear. This is a requirement in order for the majority of study in signal theory to be valid. Control theory is also linear. Mechanics, Dynamics and Statics are all based on linear equations that describe the world. Such equations are the basis for the simulations used to launch satellites into space, build solid bridges, etc. These are all important real-world systems.
A response to this criticism is that the study of non-linear behavior is still relevant to the design of linear systems because even systems designed to be linear and stable may become non-linear and unstable in certain conditions (see Tacoma Narrows Bridge and London Millennium Bridge).
Mathematicians have devised many additional ways to make quantitative statements about chaotic systems. These include:
- fractal dimension of the attractor
- Lyapunov exponents
- recurrence plots
- Poincaré maps
- bifurcation diagrams
- Transfer operator
Minimum complexity of a chaotic system
Many simple systems can also produce chaos without relying on differential equations, such as the logistic map, which is a difference equation (recurrence relation) that describes population growth over time.
Other examples of chaotic systems
- dynamical system
- Benoit Mandelbrot
- Mandelbrot set
- Julia set
- Mitchell Feigenbaum
- edge of chaos
Textbooks and technical works
- Chaotic and Fractal Dynamics, by Francis C. Moon, ISBN 0471545716
- Chaos in Classical and Quantum Mechanics, by Martin Gutzwiller, ISBN 0387971734
- Chaos: an introduction to dynamical systems, by K. T. Alligood, T. Sauer and J. A. Yorke, ISBN 0387946772
- Chaotic dynamics, by J. P. Gollub and G. L. Baker, ISBN 0521476852
- Chaos, Scattering and Statistical Mechanics, by P.Gaspard, ISBN 0521395119
- Nonlinear Dynamics and Chaos, by Steven H. Strogatz, ISBN 0738204536
- Chaos Theory in the Social Sciences : Foundations and Applications, by L. Douglas Kiel (Editor), Euel W. Elliott (Editor), ISBN 0472084720
Semitechnical and popular works
- The Beauty of Fractals, by H.-O. Peitgen and P.H. Richter
- Chance and Chaos, by David Ruelle
- Computers, Pattern, Chaos, and Beauty, by Clifford A. Pickover
- Fractals, by Hans Lauwerier
- Fractals Everywhere, by Michael Barnsley
- Order Out of Chaos, by Ilya Prigogine and Isabelle Stengers
- Chaos and Life, by Richard J Bird
- Does God Play Dice?, by Ian Stewart
- The Science of Fractal Images, by Heinz-Otto Peitgen and Dietmar Saupe, Eds.
- Explaining Chaos, by Peter Smith
- Chaos, by James Gleick
- Complexity, by M. Mitchell Waldrop
- Chaos, Fractals and Self-organisation, by Arvind Kumar
- Chaotic Evolution and Strange Attractors, by David Ruelle
- Chaos Theory and Education
- Chaos Theory: A Brief Introduction
- Manus J. Donahue's Chaos Theory & Fractal Geometry Project
- Linear and Nonlinear Dynamics and Vibrations Laboratory at the University of Illinois
- The Chaos Hypertextbook. An introductory primer on chaos and fractals.
- Chaos Theory in the Social Sciences edited by L Douglas Kiel, Euel W Elliott (Google Print)
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