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Chaos 3.
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| 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) | |
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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.
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.
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