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# Logistic map

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The logistic map is an archetypical example of how very complex, chaotic behaviour can arise from very simple non-linear dynamical equations. The map was popularized by the biologist Robert May[?] in 1976. It was originally made as a very simple model for the population numbers of species in the presence of limiting factors such as food supply or disease, containing two causal loops:

• due to reproduction the population will increase at a rate proportional to the current population
• due to starvation, the population will decrease at a rate proportional to the square of the current population. For example, if there are many foxes in one year, they will eat most of the rabbits, and the next generation of foxes will be small since there is insufficient food.

Mathematically this can be written as

xn+1 = r xn (1 - xn),

where:

• xn is a number between zero and one, and represents the population at year n
• x0 is a number between zero and one, and represents the initial population (at year 0)
• r is a positive number, and represents a combined rate for reproduction and starvation.

By varying the parameter r, the following behaviour is observed:

• With r between 0 and 1, the population will eventually die, independent of the initial population.
• With r between 1 and 2, the population will quickly stabilize on a single value; this value depends on r but does not depend on the initial population.
• With r between 2 and 3, the population will also eventually stabilize on a single value, but first oscillates around that value for some time. Again, the final value does not depend on the initial population
• With r between 3 and 1+√6 (approximately 3.45), the population will oscillate between two values forever. These two values are dependent on r but independent of the initial population.
• With r between 3.45 and 3.54 (approximately), the population will oscillate between four values forever; again, this behavior does not depend on the initial population.
• With r slightly bigger than 3.54, the population will oscillate between 8 values, then 16, 32, etc. The lengths of the parameter intervals which yield the same number of oscillations decrease rapidly; the ratio between the lengths of two successive such bifurcation intervals approaches the Feigenbaum constant δ = 4.669\cdots[/itex]. All of these behaviors do not depend on the initial population.
• At r = 3.57 (approximately) is the onset of chaos. We can no longer see any oscillations. Slight variations in the initial population yield dramatically different results over time, a prime characteristic of chaos.
• Most values beyond 3.57 exhibit chaotic behaviour, but there are still certain isolated of r that appear to show non-chaotic behavior; for instance around 3.82 there is a range of parameters r which show oscillation between three values, and for slightly higher values of r oscillation between 6 values, then 12 etc. There are other ranges which yield oscillation between 5 values etc.; all oscillation periods do occur. These behaviours are again independent of the initial value.
• Beyond r = 4, the values eventually leave the interval [0,1] and diverge for almost all initial values.

A bifurcation diagram summarizes this. The horizontal axis shows the values of the parameter r while the vertical axis shows the possible long-term values of x.

The bifurcation diagram is a fractal: if you zoom in on the above mentioned value r = 3.82 and focus on one arm of the three, say, the situation nearby looks just like a shrunk and slightly distorted version of the whole diagram. The same is true for all other non-chaotic points. This is an example of the deep and ubiquitous connection between chaos and fractals.

A GNU Octave script to generate bifurcation diagrams can be found at Logistic_map/Computer simulation.