Butterfly Effect
The butterfly effect refers to a concept that small causes can have large effects. Initially, it was used with weather prediction but later the term became a metaphor used in and out of science.
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Strange Attractors – The butterfly effect
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In chaos theory, the 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 name, coined by Edward Lorenz for the effect which had been known long before, is derived from the metaphorical example of the details of a hurricane (exact time of formation, 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.
The idea, that small causes may have large effects in general and in weather specifically, was used from Henri Poincaré to Norbert Wiener.Edward Lorenz‘s work developed the concept of instability of the atmosphere to a quantitative foundation and linked the concept to the properties of large classes of systems undergoing nonlineardynamics and deterministic chaos theory.
The butterfly effect is exhibited by very simple systems. For example, the randomness of the outcomes of throwing dice depends on this characteristic to amplify small differences in initial conditions—the precise direction, thrust, and orientation of the throw—into significantly different dice paths and outcomes, which makes it virtually impossible to throw dice exactly the same way twice.

The butterfly effect in the Lorenz attractor time 0 ≤ t ≤ 30 (larger) z coordinate (larger) These figures show two segments of the threedimensional evolution of two trajectories (one in blue, the other in yellow) for the same period of time in the Lorenz attractorstarting at two initial points that differ by only 10^{−5} in the xcoordinate. Initially, the two trajectories seem coincident, as indicated by the small difference between the zcoordinate of the blue and yellow trajectories, but for t > 23 the difference is as large as the value of the trajectory. The final position of the cones indicates that the two trajectories are no longer coincident at t = 30. An animation of the Lorenz attractor shows the continuous evolution.
Theory and mathematical definition
Recurrence, the approximate return of a system towards its initial conditions, together with sensitive dependence on initial conditions, are the two main ingredients for chaotic motion. They have the practical consequence of making complex systems, such as the weather, difficult to predict past a certain time range (approximately a week in the case of weather) since it is impossible to measure the starting atmospheric conditions completely accurately.
A dynamical system displays sensitive dependence on initial conditions if points arbitrarily close together separate over time at an exponential rate. The definition is not topological, but essentially metrical.
If M is the state space for the map , then displays sensitive dependence to initial conditions if for any x in M and any δ > 0, there are y in M, with distance d(. , .) such that and such that
for some positive parameter a. The definition does not require that all points from a neighborhood separate from the base point x, but it requires one positive Lyapunov exponent.
The simplest mathematical framework exhibiting sensitive dependence on initial conditions is provided by a particular parametrization of the logistic map:
which, unlike most chaotic maps, has a closedform solution:
where the initial condition parameter is given by . For rational , after a finite number of iterations maps into a periodic sequence. But almost all are irrational, and, for irrational , never repeats itself – it is nonperiodic. This solution equation clearly demonstrates the two key features of chaos – stretching and folding: the factor 2^{n} shows the exponential growth of stretching, which results in sensitive dependence on initial conditions (the butterfly effect), while the squared sine function keeps folded within the range [0, 1].
Examples
The butterfly effect is most familiar in terms of weather; it can easily be demonstrated in standard weather prediction models, for example.
The potential for sensitive dependence on initial conditions (the butterfly effect) has been studied in a number of cases in semiclassical and quantum physics including atoms in strong fields and the anisotropic Kepler problem. Some authors have argued that extreme (exponential) dependence on initial conditions is not expected in pure quantum treatments; however, the sensitive dependence on initial conditions demonstrated in classical motion is included in the semiclassical treatments developed by Martin Gutzwiller and Delos and coworkers.
Other authors suggest that the butterfly effect can be observed in quantum systems. Karkuszewski et al. consider the time evolution of quantum systems which have slightly different Hamiltonians. They investigate the level of sensitivity of quantum systems to small changes in their given Hamiltonians. Poulin et al. presented a quantum algorithm to measure fidelity decay, which “measures the rate at which identical initial states diverge when subjected to slightly different dynamics”. They consider fidelity decay to be “the closest quantum analog to the (purely classical) butterfly effect”. Whereas the classical butterfly effect considers the effect of a small change in the position and/or velocity of an object in a given Hamiltonian system, the quantum butterfly effect considers the effect of a small change in the Hamiltonian system with a given initial position and velocity. This quantum butterfly effect has been demonstrated experimentally. Quantum and semiclassical treatments of system sensitivity to initial conditions are known as quantum chaos.
The butterfly effect has also played a large role in many modern video games. There have been many instances of it being used, where a single/multiple choice(s) throughout gameplay may alter the entire ending of the game. A few examples are Heavy Rain, Beyond Two Souls, Until Dawn, and Life is Strange.
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Edward Norton Lorenz
Edward Norton Lorenz (May 23, 1917 – April 16, 2008) was an American mathematician, meteorologist, and a pioneer of chaos theory.^{[3]} He introduced the strange attractor notion and coined the term butterfly effect.
Biography
Lorenz was born in West Hartford, Connecticut. He studied mathematics at both Dartmouth College in New Hampshire andHarvard University in Cambridge, Massachusetts. From 1942 until 1946, he served as a meteorologist for the United States Army Air Corps. After his return from World War II, he decided to study meteorology.^{[2]} Lorenz earned two degrees in the area from theMassachusetts Institute of Technology where he later was a professor for many years. He was a Professor Emeritus at MIT from 1987 until his death.
During the 1950s, Lorenz became skeptical of the appropriateness of the linear statistical models in meteorology, as mostatmospheric phenomena involved in weather forecasting are nonlinear. His work on the topic culminated in the publication of his 1963 paper “Deterministic Nonperiodic Flow” in Journal of the Atmospheric Sciences, and with it, the foundation of chaos theory.
He states in that paper:
Two states differing by imperceptible amounts may eventually evolve into two considerably different states … If, then, there is any error whatever in observing the present state—and in any real system such errors seem inevitable—an acceptable prediction of an instantaneous state in the distant future may well be impossible….In view of the inevitable inaccuracy and incompleteness of weather observations, precise verylongrange forecasting would seem to be nonexistent.
His description of the butterfly effect followed in 1969. He was awarded the Kyoto Prize for basic sciences, in the field of earth and planetary sciences, in 1991, the Buys Ballot Award in 2004, and the Tomassoni Award in 2008. In his later years, he lived inCambridge, Massachusetts. He was an avid outdoorsman, who enjoyed hiking, climbing, and crosscountry skiing. He kept up with these pursuits until very late in his life, and managed to continue most of his regular activities until only a few weeks before his death. According to his daughter, Cheryl Lorenz, Lorenz had “finished a paper a week ago with a colleague.” On April 16, 2008, Lorenz died at his home in Cambridge at the age of 90, having suffered from cancer.
Awards
 1969 CarlGustaf Rossby Research Medal, American Meteorological Society.
 1973 Symons Gold Medal, Royal Meteorological Society.
 1975 Fellow, National Academy of Sciences (U.S.A.).
 1981 Member, Norwegian Academy of Science and Letters.
 1983 Crafoord Prize, Royal Swedish Academy of Sciences.
 1984 Honorary Member, Royal Meteorological Society.
 1989 Elliott Cresson Medal, The Franklin Institute
 1991 Kyoto Prize for ‘… his boldest scientific achievement in discovering “deterministic chaos” .’
 2000 International Meteorological Organization Prize from World Meteorological Organization
 2004 Buys Ballot medal.
 2004 Lomonosov Gold Medal
Work
Lorenz built a mathematical model of the way air moves around in the atmosphere. As Lorenz studied weather patterns he began to realize that the weather patterns did not always behave as predicted. Minute variations in the initial values of variables in his twelvevariable computer weather model (c. 1960, running on an LGP30 desk computer) would result in grossly divergent weather patterns.This sensitive dependence on initial conditions came to be known as the butterfly effect (it also meant that weather predictions from more than about a week out are generally fairly inaccurate).
Lorenz went on to explore the underlying mathematics and published his conclusions in a seminal work titled Deterministic Nonperiodic Flow, in which he described a relatively simple system of equations that resulted in a very complicated dynamical object now known as the Lorenz attractor.
Publications
Lorenz published several books and articles. A selection:
 1955 Available potential energy and the maintenance of the general circulation. Tellus. Vol.7
 1963 Deterministic nonperiodic flow. Journal of Atmospheric Sciences. Vol.20 : 130—141 link.
 1967 The nature and theory of the general circulation of atmosphere. World Meteorological Organization. No.218
 “Three approaches to atmospheric predictability” (PDF). Bulletin of the American Meteorological Society 50: 345–349. 1969.
 1972 Predictability: Does the Flap of a Butterfly’s Wings in Brazil Set Off a Tornado in Texas? link
 1976 Nondeterministic theories of climate change. Quaternary Research. Vol.6
 1990 Can chaos and intransitivity lead to interannual variability? Tellus. Vol.42A
 2005 Designing Chaotic Models. Journal of the Atmospheric Sciences: Vol. 62, No. 5, pp. 1574–1587.