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Qué (quién) es logistic$554069$ - definición
![Stable regions within the chaotic region, where a tangent bifurcation occurs at the boundary between the chaotic and periodic attractor, giving intermittent trajectories as described in the [[Pomeau–Manneville scenario]].](https://commons.wikimedia.org/wiki/Special:FilePath/Logistic map bifurcation diagram magnifications.png?width=200 "Stable regions within the chaotic region, where a tangent bifurcation occurs at the boundary between the chaotic and periodic attractor, giving intermittent trajectories as described in the [[Pomeau–Manneville scenario]].")
![heavier tails]] of the logistic distribution.](https://commons.wikimedia.org/wiki/Special:FilePath/Logistic-sigmoid-vs-scaled-probit.svg?width=200 "heavier tails]] of the logistic distribution.")
![standard Cauchy]]](https://commons.wikimedia.org/wiki/Special:FilePath/Comparison of log-pdf's- sigmoid-beta vs normal vs cauchy.svg?width=200 "standard Cauchy]]")
Wikipedia
The logistic map is a polynomial mapping (equivalently, recurrence relation) of degree 2, often referred to as an archetypal example of how complex, chaotic behaviour can arise from very simple nonlinear dynamical equations. The map was popularized in a 1976 paper by the biologist Robert May, in part as a discrete-time demographic model analogous to the logistic equation written down by Pierre François Verhulst. Mathematically, the logistic map is written
where xn is a number between zero and one, which represents the ratio of existing population to the maximum possible population. This nonlinear difference equation is intended to capture two effects:
- reproduction, where the population will increase at a rate proportional to the current population when the population size is small,
- starvation (density-dependent mortality), where the growth rate will decrease at a rate proportional to the value obtained by taking the theoretical "carrying capacity" of the environment less the current population.
The usual values of interest for the parameter r are those in the interval [0, 4], so that xn remains bounded on [0, 1]. The r = 4 case of the logistic map is a nonlinear transformation of both the bit-shift map and the μ = 2 case of the tent map. If r > 4, this leads to negative population sizes. (This problem does not appear in the older Ricker model, which also exhibits chaotic dynamics.) One can also consider values of r in the interval [−2, 0], so that xn remains bounded on [−0.5, 1.5].
