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  "body": "\n## Summary\n\nChaos theory studies deterministic [[dynamical systems|BBZP1R872N]] whose long-time behaviour looks random and cannot be predicted in practice.\nThe key feature is sensitive dependence on initial conditions, the [[butterfly effect|G0ZT1SDQGS]]: tiny differences in the starting state grow until they dominate.\nTypical tools are [[Lyapunov exponents|NJ1FJSNK6A]], [[strange attractors|9WKCZGG906]], symbolic dynamics and invariant measures, and a standard mathematical definition of a chaotic map is [[Devaney chaos|21B8AHPD4P]].\nClassical examples are the [[Lorenz system|WYRC41CQN9]] in meteorology and the [[logistic map|QN9X9BNKTX]] in population dynamics.\n\n## Explanations\n\n### L1 human\n\nA chaotic system follows exact rules, with no chance involved, and yet its future is practically unpredictable.\nThe reason is that any small error in the present, such as rounding a measurement, is magnified again and again.\nThe doubling map $x \\mapsto 2x \\bmod 1$ shows this well: it shifts the binary digits of $x$ one place to the left.\nTwo starting values that agree in their first $50$ binary digits give orbits that look unrelated after about $50$ steps.\nWeather is the most famous example, which is why forecasts become unreliable after a week or two.\n",
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