Chaos 3.

Back to the previous animation. Try this: put the dot down near the lower left, put the slider in the middle, and start it up. The rabbit population quickly increases, followed by a steady increase in the fox population, which causes the rabbit population to oscillate a little as things bounce back and forth and settle down eventually at about 50 foxes, and 400 rabbits (I didn't put a screen refresh button on this animation, but any time you want to clear the screen of intermediate dots, just open a folder window covering the plot, and this will get rid of old dots). So now slowly, bit by bit, slide the slider to the right. At a position about 60% of the way from the left you'll see the first bifurcation. The dots should spread out on either side of that single stable fixed point at (400, 50). At about 80% from the left another bifurcation occurs. And a very short distance past that yet another occurs, at which time the populations are bouncing between eight distinct points. I've managed to get one more bifurcation (16 points) before chaos commences. (Down)
In the case we looked at on page 11 we had one variable, and the plot of dots we made was of pairs of points (x old, x new), so the points necessarily fell on the parabola y = a(x - x2). Now we've got two variables, and our plot of dots is of pairs (R new, F new), where R new and F new are both functions of R old and F old in a very nonlinear way. Anyway, predicting what pattern of dots will appear in the chaotic region is now very much more more problematic.

This picture is the pattern of dots that occurs when the slider is placed all the way to the right. It's a strange region to which the dots are attracted, called a Strange Attractor. In particular, it highlights the fact that while the chaotic region may be complex and usually unpredictable, it is not random; the dots don't spread all over the chart without any discernable pattern.

We should end this section with a few qualitative notes. First, it should be apparent that very small changes in initial conditions can give rise to very different long term evolution and behavior. For example, let's suppose (we're just pretending now - don't be scared) that we as a species were stupid enough to change the CO2 and methane levels in our atmosphere to such an extent that it had a global effect on climate (again, please, I'm just pretending!). Well, the climate and the environment are extremely complicated nonlinear systems. We might hope that in making these pretend changes it would alter the climate and environment slowly enough that we could adapt (in the words of John Hume: ill habits gather by unseen degrees). If chaos theory teaches us anything, it is that the small changes we make can give rise to sudden and catastrophic consequences, such as outbreaks and wild oscillations (dogs and cats interbreeding, aliens from deep space intruding in an unfriendly fashion on the Milan fashion industry).

Another thing chaos theory teaches us is that it teaches us less than we'd like. Chaos theory is really neat mathematically, but in the real world chaotic regimes are very unpredictable. Part of the reason chaos theory has received as much attention as it has is that economies evolve nonlinearly, and people with money wanted mathematicians to develop ways of analysing economies to make it easier for them to make more money without having to work for it. Unfortunately for them, the best chaos theory can do is to predict unpredictability. This is fortunate for everyone else, for in the absence of anything like excessive control or tyranny societies can evolve in extraordinary and unpredictable ways, enriching us existentially. Tyrannies tend to take us too far from the edge of chaos, which reduces inherent flexibility and ultimately long term survivability - that is, they harbor the seeds of their own destruction. On the other hand, so too do societies that allow too much chaos. Biological systems rely on a fine balance of order and chaos (to be distinguished from randomness), and long term survivability of a species requires a fine mix of both, being right on the edge. It's sort of like surfing: too far out and there's nothing happening; too far in and it's all churn and foam; but dude, right on the face of a tubular wave, that's heaven.

Home.
Population Page 11.
Population Page 13.

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