Chaos

[From Bruce Gregory (960314.1730 GMT)

Bill Powers 960314.0100

     I believe that this view of natural processes has been superseded since
     the chaotic nature of the world was recognized, and here I refer only to
     the weak form of chaos, the Butterfly Effect. There doesn't seem to be
     any process of the kind to which you (and Dennett) refer.

There is a test for the presence of chaos, just as there is for the
presence of control. The test is called sensitivity to initial
conditions. If two systems differing in initial conditions by
only a small delta begin to diverge exponentially, chaos is said
to apply. The amount of time it takes for such a divergence to
appear can be very long. Chaos in the solar system can be a matter
of hundreds of millions, if not billions, of years. By this test, it
would appear that there may be many non-chaotic systems in nature.

Regards,

Bruce Gregory

[from Chris Kitzke 960314.0850 EST]

(Bruce Gregory 960314.1730 GMT)

<<If two systems differing in initial conditions by
only a small delta begin to diverge exponentially, chaos is said
to apply.>>

Is this just a commonly held definition of chaos? The question that entered
my mind was: why does the divergene have to be exponential for there to be
chaos? If something diverges exponentially, is it not predictable (rather
than chaotic)?

Just a little curious,
Chris

[Martin Taylor 960314 17:55]

Chris Kitzke 960314.0850

<<If two systems differing in initial conditions by
only a small delta begin to diverge exponentially, chaos is said
to apply.>>

Is this just a commonly held definition of chaos?

It's a loose verbalization of a mathematical statement that does define
chaos. If you want to search it out in related literature, look for the
term "Lyapunov exponent" which makes it precise. It really applies only
in the very long term, so Bruce Gregory's use of the wording "begin to
diverge" is a bit misleading from a technical point of view. "Begin to
diverge" happens around a bifurcation in a non-chaotic system, as well.

If something diverges exponentially, is it not predictable (rather
than chaotic)?

Chaotic systems are precisely predictable, so that's not a distinction.

The problem of divergence is that the error of prediction grows beyond
any bound _eventually_ if there is any error _at all_ in the measurement
of the initial conditions. In a chaotic universe, control is the only
practical way to get things to go where you want them to. You have to
keep observing and correct for the divergence that keeps happening. "Fire
and forget" works only in the short run.

Martin

[Bruce Gregory 960315.1010 EST]

Chris Kitze 960314.0850
Martin Taylor 960314.1755

    It's a loose verbalization of a mathematical statement that does define
    chaos. If you want to search it out in related literature, look for the
    term "Lyapunov exponent" which makes it precise. It really applies only
    in the very long term, so Bruce Gregory's use of the wording "begin to
    diverge" is a bit misleading from a technical point of view. "Begin to
    diverge" happens around a bifurcation in a non-chaotic system, as well.

I thank Martin for shoring up my loose verbalizations. My
understanding is that the Lyapunov time is the e-folding time for the
separation of two systems with slight different starting conditions
(loose wording again). What little I know about chaos comes from
conversations with my colleague Mike Lecar, who studies chaos in the
solar system. A very accessible, if elementary, discussion of the
latter topic appears in Ivars Peterson's recent book, _Newton's
Clock: Chaos in the Solar System_. Manfred Schroeder's book
_Fractals, Chaos, and Power Laws_ is much more mathematical, but
still quite readable.

Bruce Gregory

[From Bruce Abbott (960315.1245 EST)]

Bruce Gregory 960315.1010 EST --

I thank Martin for shoring up my loose verbalizations. My
understanding is that the Lyapunov time is the e-folding time for the
separation of two systems with slight different starting conditions
(loose wording again). What little I know about chaos comes from
conversations with my colleague Mike Lecar, who studies chaos in the
solar system. A very accessible, if elementary, discussion of the
latter topic appears in Ivars Peterson's recent book, _Newton's
Clock: Chaos in the Solar System_. Manfred Schroeder's book
_Fractals, Chaos, and Power Laws_ is much more mathematical, but
still quite readable.

Another excellent source at the introductory level is Ian Stewart's book,
_Does God Play Dice? The Mathematics of Chaos_.

Bruce

[Martin Taylor 960315 14:30]

Bruce Abbott (960315.1245 EST)

Another excellent source at the introductory level is Ian Stewart's book,
_Does God Play Dice? The Mathematics of Chaos_.

And for those whose minds are pictorially inclined, a very good source,
which comes along with computer disks (for at least Macintosh, maybe others):
"Dynamics, the geometry of behavior" (4 volumes 8 1/2 x 11 paperback)
Abraham and Shaw, Aerial Press, 1984-88. The subtitles: Part 1, periodic
behavior; Part 2, Chaotic behavior; Part 3, Global behavior; Part 4,
Bifurcation behavior.

These volumes consist largely of captioned pictures, and they brillinatly
clarify the complexities of the concepts. Equations are, for the most part,
relegated to appendices. Highly recommended.

Martin

[From Bruce Abbott (960316.1910 EST)]

Bruce Gregory (960314.1730 GMT) --

There is a test for the presence of chaos, just as there is for the
presence of control. The test is called sensitivity to initial
conditions. If two systems differing in initial conditions by
only a small delta begin to diverge exponentially, chaos is said
to apply. The amount of time it takes for such a divergence to
appear can be very long. Chaos in the solar system can be a matter
of hundreds of millions, if not billions, of years. By this test, it
would appear that there may be many non-chaotic systems in nature.

Many who have commented on chaos have focused on the fact that in the long
run, the behavior of chaotic systems is unpredictable. (As you note, in
some cases the "long run" may need to be very long indeed before this
property of chaotic systems becomes a problem.) But just as interesting is
the demonstration that irratic and apparently unpredictable variations over
time may be produced by very simple, completely determinate systems.
Looking at what appears to be a disorderly graph apparently denoting nothing
but "noise," one is amazed to discover that these variations result simply
from feeding the output (Y) of a simple nonlinear equation (try Y =
(2*X*X)-1) on iteration i back into its input variable (X) on iteration i +
1. This suggests that many examples of apparently complex natural variation
may be reducible to description by some surprisingly simple rules. Because
the precise state of the critical variables in some chaotic physical system
cannot be known with sufficient accuracy, knowledge of the underlying rules
will not confer long-term predictability of the system's states;
nevertheless, it can be satisying to know that such systems, for all their
apparently unsystematic variation, can be reduced to a simple mathematical
description.

Below is a Turbo Pascal program that demonstrates a chaotic variable's
changes over time using the equation Y = (2*x*x) - 1. It should compile in
Turbo or Borland Pascal version 5.0 or higher. Before compiling it, be sure
to set the BGIDIR constant given near the top of the program to the BGI
drive/directory that contains your Borland Graph unit (it's called the "bgi"
subdirectory by the program that installs Turbo Pascal).

Regards,

Bruce

···

---------------------------------------------------------------------------
program ChaosDem;
{ Demonstrates Chaotic variation over time.

  Bruce Abbott (960316)
}

uses crt, graph;

const
  BGIDIR = 'c:\bp\bgi'; { set this to your bgi drive\directory }

var
  ch: char;
  MaxX, MaxY: integer;
  graphdriver, graphmode: integer;
  Stop: boolean;

procedure InitGraphics; {ADAPTS TO HARDWARE}
begin
graphdriver := 0; graphmode := 0;
detectgraph(graphdriver,graphmode);
initgraph(graphdriver,graphmode, BGIDIR);
graphmode := getmaxmode;
setgraphmode(graphmode);
end;

procedure InitScreen;
begin
  ClrScr;
  InitGraphics;
  MaxX := GetMaxX; MaxY := GetMaxY;
end;

procedure Chaos;
var
  i: integer;
  x, y: real;
  X1, Y1: integer;

  procedure DoCalcs(var x, y: real; var Y1: integer);
  begin
    y := 2*x*x - 1;
    Y1 := MaxY div 2 - round(y * 50);
    x := y;
  end;
  procedure ClearGraph;
  begin
    ClearViewPort;
    outtextxy(20, MaxY-50, 'Press ESC key to quit . . .');
  end;
begin
  ClearGraph;
  x := 0.54321;
  X1 := 0;
  DoCalcs(x, y, Y1);
  MoveTo(X1, Y1);
  repeat
    inc(X1); inc(X1);
    DoCalcs(x, y, Y1);
    LineTo(X1, Y1);
    if X1 >= MaxX then
      begin
        X1 := 0;
        ClearGraph;
        MoveTo(X1, Y1);
      end;
    if keypressed then
      begin
        ch := readkey;
        if ch = #27 then Stop := true;
      end;
    delay(5);
  until Stop;
end;

begin
  InitScreen;
  Stop := false;
  Chaos;
  clrscr;
  CloseGraph;
end.

[From Bill Powers (961024.0750 MDT)]

Martin Taylor 961023 13:30 --

Now I am really confused. I thought that one example of a chaotic system
was a nonlinear oscillator with various degrees of positive feedback.

Yes, that's right, but it doesn't work quite the way you were suggesting.

...

What happens is not the phase
locking and unlocking of an increasing number of frequencies of oscillation.
In some systems, indeed that can happen, but they are not chaotic systems.
One can't see it in the temporal waveform, but if one did a long-duration
Fourier analysis, the multi-frequency oscillator would show several sharp
peaks with essentially zero energy between the peaks, whereas the chaotic
system that looks much the same in the time-waveform would have a continuum
spectrum, with broad peaks.

Wouldn't that just be a matter of the "Q" (reciprocal of energy losses per
cycle) of the oscillating circuit or system? A low-Q oscillator wouldn't
show sharp peaks, but if it were nonlinear, it would generate harmonics
which would participate in the positive feedback according to their
amplitudes and phases, which would change as the system is driven more or
less into the nonlinearities. Even the "Q" would change for the various
harmonics, because the dissipation per cycle would depend on the
nonlinearities. Your example of the dripping faucet sounds as though it
would fit these criteria.

What I'm trying to understand is what is physically different between an
oscillator that shows chaotic behavior (constant or in-and-out) and one that
doesn't.

Be,

Bill P.

[Martin Taylor 961024 17:15]

Bill Powers (961024.0750 MDT)

What I'm trying to understand is what is physically different between an
oscillator that shows chaotic behavior (constant or in-and-out) and one that
doesn't.

I don't know enough to give a clear distinction. But I can give a rough
approximation. Firstly, let me say that I don't know how to make a
multifrequency enharmonic oscillator, so that doesn't come into it.

Any oscillator needs at least two places to store energy, I think. They might
be the kinetic and potential energy of a ball, or the electric and magnetic
fields in a capacitor and a coil, or something else. This implies that
one can treat the oscillator in terms of the variable of energy stored in
one place or the other and how it changes over time. It could be that,
for example x(t) = a*t + b* (y(t-deltat))^2, y(t) = c*x(t-epst)^2, where
a, b, c are constants.

Those are equations just pulled out of the air, and don't mean anything
in particular.

Now, I don't know the criteria for when (or whether) the circuit described
by that equation would go chaotic, or if it would. But in a sampled system,
it seems that so long as there is a nonlinearity greater than a square
in the feedback loop, there will be parameter values for which the system
goes chaotic, and I would not be surprised if the same criterion applies
to the continuous case as well.

This is clearly an unsatisfactory answer, but I'd have to learn a bit more
to be able to define the criteria more exactly.

I guess another proper statement is that some systems are not sufficiently
nonlinear to go chaotic, no matter what values their parameters are set to,
whereas others will or will not be, depending on their parameters. There
are physical differences between the two kinds of systems, but not between
the chaotic and non-chaotic states of the systems that are affected by
their parameter values (such as gain).

One can't see it in the temporal waveform, but if one did a long-duration
Fourier analysis, the multi-frequency oscillator would show several sharp
peaks with essentially zero energy between the peaks, whereas the chaotic
system that looks much the same in the time-waveform would have a continuum
spectrum, with broad peaks.

Wouldn't that just be a matter of the "Q" (reciprocal of energy losses per
cycle) of the oscillating circuit or system?

I was assuming that the question was of a stable multifrequency oscillator.

Let me dredge up my long-rusted electronic memory, and ask whether it is
not the case that feedback around a filter enhances the apparent Q of the
filter, until at some point the virtual Q becomes infinite and oscillation
commences? I'm wondering whether the inherent Q of the filters in the
oscillator would affect the stability of the oscillator frequency in the
absence of thermal or other noise disturbances.

That's really beside the point you bring up, which is a good one. If the
oscillator is truly unstable in its fundamental frequency, and generates
harmonics...

which would participate in the positive feedback according to their
amplitudes and phases, which would change as the system is driven more or
less into the nonlinearities,...

would this be a chaotic oscillator rather than a multifrequency oscillator?

I'm afraid I don't know the answer to this question. I suspect, without
much strength of belief, that it might well be chaotic, because the
frequency variations of the fundamental would be driven by the feedback
around the nonlinearities. But don't go telling anyone I said so:-)

Your example of the dripping faucet sounds as though it would fit these
criteria.

Perhaps. But what I was trying to illustrate with the drippy tap was the
classical "period doubling" route to chaos. The oscillator keeps doubling
its period at discrete values of the control parameter (water flow). The
succession of values at which doubling happens gets closer and closer by
Feigenbaum's ratio, and at a very precise value of the control parameter,
the period becomes infinite. As the control parameter is increased, the
waveform may change, in the sense that different values of the oscillator
output vary their probability densities, but the period remains infinite,
until at some higher value of the control parameter, the waveform returns
to a short, finite periodicity. With further increases in the control
parameter, the period doubles and redoubles just as before, but quicker.
And so on...

That's the classical route to chaos, and your low-Q non-linear oscillator
might well follow it as you increased the loop gain. I really don't know.

Sorry not to be more precise.

Martin