Logistic map
The logistic map is a polynomial mapping (equivalently, recurrence relation) of degree 2, often cited 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,[1] in part as a discretetime demographic model analogous to the logistic equation written down by Pierre François Verhulst.[2] Mathematically, the logistic map is written

(1)
where x_{n} is a number between zero and one, that 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 (densitydependent 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 are those in the interval [0, 4], so that x_{n} remains bounded on [0, 1]. The r = 4 case of the logistic map is a nonlinear transformation of both the bitshift 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 x_{n} remains bounded on [−0.5, 1.5].[3]