Chaos: A Very Short IntroductionOUP Oxford, 22/02/2007 - 200 páginas Chaos exists in systems all around us. Even the simplest system of cause and effect can be subject to chaos, denying us accurate predictions of its behaviour, and sometimes giving rise to astonishing structures of large-scale order. Our growing understanding of Chaos Theory is having fascinating applications in the real world - from technology to global warming, politics, human behaviour, and even gambling on the stock market. Leonard Smith shows that we all have an intuitive understanding of chaotic systems. He uses accessible maths and physics (replacing complex equations with simple examples like pendulums, railway lines, and tossing coins) to explain the theory, and points to numerous examples in philosophy and literature (Edgar Allen Poe, Chang-Tzu, Arthur Conan Doyle) that illuminate the problems. The beauty of fractal patterns and their relation to chaos, as well as the history of chaos, and its uses in the real world and implications for the philosophy of science are all discussed in this Very Short Introduction. ABOUT THE SERIES: The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable. |
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... scientists remained in denial? Great scientists and mathematicians had sufficient courage and insight to foresee the coming of chaos, but until recently mainstream science required a good solution to be well behaved: fractal objects and ...
... scientists remained in denial? Great scientists and mathematicians had sufficient courage and insight to foresee the coming of chaos, but until recently mainstream science required a good solution to be well behaved: fractal objects and ...
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... scientist Pierre Laplace elevated this determinism to a key place in science. A world is deterministic if its ... scientists were also meteorologists. Chaos and meteorology are closely linked by the meteorologists' interest in the ...
... scientist Pierre Laplace elevated this determinism to a key place in science. A world is deterministic if its ... scientists were also meteorologists. Chaos and meteorology are closely linked by the meteorologists' interest in the ...
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... Scientists often say that any uncertainty in an observation is due to noise, without really defining exactly what the noise is, other than that which obscures our vision of whatever we are trying to measure, be it the length of a table ...
... Scientists often say that any uncertainty in an observation is due to noise, without really defining exactly what the noise is, other than that which obscures our vision of whatever we are trying to measure, be it the length of a table ...
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... scientists. It was research into nonlinearity and chaos that clarified yet again how import this distinction remains. In Chapter 10, we will return to take a deeper look at how today's weather forecasters would have used insights from ...
... scientists. It was research into nonlinearity and chaos that clarified yet again how import this distinction remains. In Chapter 10, we will return to take a deeper look at how today's weather forecasters would have used insights from ...
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... a major impact on the sciences, forcing a close re-examination of what scientists mean by the words 'error' and 'uncertainty' and how these meanings change when applied to our world and our models. As Whitehead noted, it is dangerous 16.
... a major impact on the sciences, forcing a close re-examination of what scientists mean by the words 'error' and 'uncertainty' and how these meanings change when applied to our world and our models. As Whitehead noted, it is dangerous 16.
Índice
1 | |
2 Exponential growth nonlinearity common sense | 22 |
determinism randomness and noise | 33 |
4 Chaos in mathematical models | 58 |
5 Fractals strange attractors and dimensions | 76 |
6 Quantifying the dynamics of uncertainty | 87 |
7 Real numbers real observations and computers | 104 |
statistics and chaos | 112 |
does chaos constrain our forecasts? | 123 |
can we see through our models? | 132 |
11 Philosophy in chaos | 154 |
Glossary | 163 |
Further reading | 169 |
Index | 173 |
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Palavras e frases frequentes
21st-century demon Apprentice Map attractor average Baker’s Map behaviour bell-shaped distribution best models bits butterfly effect chaotic systems climate models components computer simulations Day storm decision support define digital computer dimension dynamical system Earth’s ECMWF ensemble forecast ensemble members equations error exponential growth fact fractal Full Logistic Map future Galton Board golf balls Hénon Hénon Map impact infinitesimal initial condition insight iteration least squares look Lorenz Lyapunov exponent mathematical chaos mathematical map mathematical models mathematical systems mathematicians mature pair measure model inadequacy Moore-Spiegel NAG Board nonlinear systems number of rabbits observational noise panel parameter values perfect model periodic loop physical systems physicist population predictability probability forecasts quantify question random number real world reflect scientists self-similar sensitive dependence shadow solar space statistician statistics stochastic study of chaos temperature Tent Map Theorem things today’s trajectory voles weather forecasting weather model zero