The Test, Kids and Control, Behavioral Illusions

[From Rick Marken (960215.1400)]


I think you will see that continuous temporal variations in the applied
force/ disturbance will be completely effective, resulting in proportional
continuous temporal variations in partical position that are highly
correlated with variations in the disturbance.

Martin Taylor (960215 13:30) --

Then so will any true control system, for which:

p = d/(1+G) + rG/(1+G)

Yes, but note that variations in p (the controlled variable) will be 1/(1+G)
of the variations in d. For a normal control system (one with G greater than,
say, 100) the variations in the controlled variable would be less than
1/100th the size of the variations in the disturbance: this means that in
most cases, the variations in the controlled variable will be virtually
undetectable while varations in the disturbance are large and easily

For a cause-effect system, G is typically close to 0.0 so variations in the
controlled variable that result from variations in d would be about equal in
size to the variations in d.

It is really not difficult to tell, using the Test, whether or not a variable
is being caused or controlled.

In my previous discussion of the same example, I proposed several different
ways to provide continuously varying influences on the particle's position.

Great. Now all we have to do is run the simulations and measure the relative
amplitude of the variances of the waveforms of the disturbance and putative
controlled variable.

I would like to be given a clear discussion of how an observer can discover
which phenomenon [control or equilibrium] is occurring, if the mechanisms
for causing the observed effect are not physically clear or accurately

I think it has been given to you several times; you just don't seem to want
to take it;-)

It is obviously not necessary to know the mechanisms underlying a phenomenon
in order to know what phenomenon is occurring; how else would ancient people
have observed the phenomenon of planetary motion. You know that the
phenomenon of control is occurring when one variable (the disturbance) has
far less of an effect on another variable (the controlled variable) than is
expected based on a physical analysis of the situation; the Test reveals
whether or not a variable is under control. What could be clearer?

Maybe with enough thought you will be able to see the problem, but I don't
really mind if you don't, so long as you don't mind the rest of us examining
the possibilities it opens up.

I mind. But I can't seem to do anything about it;-)

Chris Cherpas (960215.0912 PT)--

Can you imagine how a 5-year old with a multi- media computer might
get started learning the underlying concepts of PCT?... Any ideas on what a
PCT curriculum that starts in grade school would look like?

What a GREAT question!

I will start thinking about this right away; I'll try to have some
suggestions by the weekend. I hope others on the Net post their ideas about
this. I can't think of anything more important than teaching our children
about the nature of human nature. We should have been asking ourselves this
years ago. Thanks, Chris.

Chris Cherpas (960215.1002 PT)--

Controlling for a concept of linear (IV-DV) causality is often cited
here as an impediment to seeing circular causality.

Yes. I believe that is _precisely_ what keeps people not only from seeing
circular causality but, more important, from understanding it.

The situation is perhaps analogous to the perceptual experiment in which a
picture looked at from one perspective appears to be a young woman ("wife")
but as an older woman ("mother-in-law") when "viewed differently." Both
views are "correct," but cannot be held at the same time.

Yes. We've discussed this before. The problem with this analogy is that
behavior is not the same as a line drawing. While the two views of the line
drawing are both "correct" (that is, equally supported by the evidence -- the
lines), the two views of behavior (cause- effect and circular causality) are
_not_. Only one view of behavior is correct: circular causality.