Challenge, Control and Purpose

[From Rick Marken (950916.915)]

James Culbertson (950915) --

Welcome to CSG-L. You certainly start of with some strong claims:

Indeed, the whole notion of purposive-behavior while somewhat useful,
seems to me more and more a human derivation without ultimate foundation.

The notion of purposeful behavior is not "useful"; it is a demonstrable
fact. Purpose is control: the maintainance of a variable in a predetermined
state against unpredictable disturbances. It is easy to demonstrate the
fact of control (purpose). PCT is an explanation of HOW purposeful behavior
occurs: it is, according to PCT, control of perception.

I defer to Humberto Maturana, Heinz Von Foerster, Gregory Bateson, and

That's up to you. In this group we defer only to god -- qua nature;-).

I would be interested in knowing if anyone is trying to integrate control
theory and chaos theory into their models and experimentation. That, it
seems to me, would be truer to life (and to "reality").

No one I know of is trying to do this; we have, thus far, seen no evidence
that demands that we "incorporate chaos theory in our models". But you
suggest that their is such evidence (you say that such incorporation
would make control theory more true to life).I would really like to see
this evidence.

Hans Blom (950914x) --

Are things clearer now?

Sort of. Your model definitely controls. But there are some funny
little problems. For example, in the calculation of dpre (dpre = 2*dnew-dold)
the 2 is crucial; a value other than 2 leads to loss of control rather
abruptly. The feedback function (k) is also limited to values <=1. Any
gain from environmental feedback seems to lead to loss of control. So this
control system seems to work only in a "dissipative" environment -- one
that leads to a decrease (or, at least no increase) in the effect of
u on x.

So I'll agree that your model does control. But it does so in more limited
environments than the simple control model (u := u + (r-p)) and it does this
more limited controlling using more complex machinery -- your control system
must have a precise measure of its own output (u) and it must be able to store
previous values of the estimates of disturbance (dold); and is must do
this storage precisely (while the integration done by the control model can
be relatively sloppy -- leaky).

So in one circumstance you model is more precise than the regular control
model; but it is also more complex (it needs precise measure of u and
precise storage of dold) and limited to a very narrow range of environments.

Rick, are you satisfied too?

Somewhat, yes. I'm sure glad you provided that model; I still need to study
it to see what's going on; even though it is simple it is very puzzling.

If we take the model at face value as a reasonable model of control, I guess
the next question is whether we can do an experiment with people to see
which of the two models (PCT vs model based control) gives the best
representation of their controlling.