[From Bill Powers (2010.09.14.0655 MDT)]
Dick Robertson,2010.09.14.0845CDT –
DR: I think Gavin Ritz has a
point when he raises his question about HPCT. That is his claim (if I
understand correctly) about the difficulty of describing a proper model
for higher levels of controlled variables.
BP: Gavin is, of course, right about the difficulties with the higher
levels in the HPCT model. But he has misidentified the problem. There are
really two main problems: first to define what the higher levels do, and
second to explain how they do it. I have focused almost entirely on the
question of what the higher levels do, with only occasional glimpses of
answers to the other question: how they have to be organized to do it.
But this doesn’t make my approach different from anyone else’s: nobody
knows even how configuration perception works, much less any higher
level. The nearest anyone has come to artificial perception of
configurations is to develop systems that can classify and name
configurations: this is called “apple,” that is called
“zebra.” But that doesn’t explain how a person can recognize
and control a cube balanced on one corner and spinning, which doesn’t
require classifications or names.
We don’t need to know how perceptual input functions work to experience
their outputs. I’m glad to see that you have arrived at the same
conclusions.
We all know what a configuration is, and I think there’s little argument
about the general definition; something made of different sensations at
Level Two, but which is not itself a sensation.
This is true of all the other levels, as far as I’ve been able to find
consistent and mutually-coherent definitions. I don’t think anyone can
argue that we don’t perceive sequentiality (ordering), or relationships,
or logical functions (one if by land, two if by sea), and so on. I
haven’t yet heard from anyone who simply doesn’t perceive the types of
perceptions I’ve defined. Well, maybe one or two have expressed
puzzlement about what I mean by “principles.”
That doesn’t mean I’m sure I’m right, and I never encourage anyone
to memorize the levels as I have defined them, or to try to find those
levels in applications like the Method of Levels. The idea of relative
level is enough for now.
DR: That and the other recent
thread highlighted the need for a definition of what a model is. My idea
derives from the very literal models we looked at in grade school, the
model of the solar system in which you could see the relationships
between the sun and planets. You could see why – as long as you thought
the earth the center – the planets would sometimes appear to move
backwards. You could see the phases of the moon as it moved around the
world (our model had a light bulb for the sun in the middle. There were
other things that helped define the concept of model for me, model
trains, for example. What all these “models” had in common was
that they were physical objects that corresponded in essential ways to
the larger objects – systems – to which they applied. I don’t recalll
seeing Gavin’s definition of a true model, but I assumed it might be
something like that too. So when he decried the lack of analogues to that
concept in HPCT I had to agree.
Perhaps a better word than model would be simulation. A simulation is a
working representation of the relationships among variables in a physical
system. To simulate Newton’s Laws of Motion, for example, we would start
with the famous F = MA, which states that Acceleration is directly
proportional to applied Force, the constant of proportionality being
called Mass. A signal representing force/mass can be fed into an
integrator to produce an output corresponding to velocity; a velocity
signal can be fed into a second integrator to produce an output
corresponding to position. So without trying to include irrelevant
variables like price, odor, color, size, or shape, we can simulate the
behavior of a free-floating object when it’s subjected to an applied
force. The simulation will obey Newton’s laws of motion, and from its
behavior we can predict the behavior of a real physical system. That’s
the object of modeling: to construct a simulation which behaves in the
relevant ways as much like the real system as possible. By seeing how the
simulation has to be organized to achieve this end, we learn how the real
system is organized.
So modeling is not just minaturizing. If size made a difference in the
behavior we would have to give the model the correct size, too (for
example, if we want to take air resistance into account). But we don’t
have to construct an actual physical duplicate of the system. We can do
all this with computations: we can use numbers for F, M, and A, and
mathematical functions such as F = MA to represent the relationships
among these variables that are actually observed, measured. We don’t have
to make the computation weigh 10 kilograms to compute the acceleration of
a 10-Kg mass by a force of 10 Newtons.
DR: When we get above the level
of relationships, the controlled variable of interest is wholly in one’s
head. I believe. We do not automatically see the same categories as the
next person. We can train each other to a point of considerable
agreement.
BP: This is partially true, partially false. Consider how you manage to
enter a house through the front door. You reach out, grasp the knob, pull
the door open, and walk through. While this sequence may exist only in
your head, you will find that no other sequence will work; you can’t, for
example, walk through the door and then open it. While you can’t put your
finger on any actual material thing that corresponds to the sequence, it
nevertheless has an existence independent of you and you have to control
the perception of sequence in the right way if you want to enter the
house.
All of the levels I have defined have this nature: they are perceptions
that must be controlled in particular ways in order to have the effects
that are wanted. We have little choice in this: we learn the rules or we
fail to control. So there must be something objective about whatever
underlies these levels. That is what gives the definitions some degree of
reality. These levels are clues about our connections to whatever it is
that lies beyond the senses.
DR: So are we still without
models for higher orders?
BP: We are not without models of the behavior of higher orders of
control. You and David Goldstein used a model of self-concept in your
study – you used the principles of negative feedback control to predict
what a person would do when self-descriptions were contradicted, and
found that the predictions were nearly 100% correct.
What you didn’t have was a model of how a self-concept perception is
derived from lower-order perceptions. For that, you had to rely on your
own ability to perceive self-concepts in relationship to statements about
personal characteristics. You could perceive the lower-order elements and
you could perceive the effects on a higher-order perception, so even
though you couldn’t fill in the mechanism by which the higher-order
perception was generated, you could still predict from your own
experiences what the perception would be. In the same way, I can’t tell
you how a spinning cube is perceived in the midst of a huge collection of
changing sensations, but I can at least test to see if another person
perceives and controls the same thing under the same conditions in which
I perceive and control it. This shortcut seems, in fact, to work quite
well. You’ll notice that in LCS3, I predict quite confidently exactly how
a reader will behave in all the interactive demonstrations included in
the book – and I would be surprised if those predictions were ever
wrong.
DR: If these are models of
higher order systems, then, even if we haven’t yet found out how to
construct them on computers it is obvious that that will come. After all
robots are coming closer to simulating human behavior in many respects.
When they do so aren’t they being models?
BP: It’s not necessary to use a computer or build a robot to construct
and test models. The only real requirement is that the behavior of the
model follow strictly from the properties you give it, so you can’t reach
in and tweak it if it starts to misbehave. The main advantage of a
computer is that you can program it in advance so it can ONLY do what you
have specified that it does; this means you can’t cheat. But if you don’t
want to cheat, and really want an honest test of a model, all you have to
do is specify very carefully the properties you are giving the model,
using unambiguous language set down on paper so it can’t be changed after
you start to apply the model. You have to specify how to reason about the
model, how to make predictions. Then all you or anyone else needs to do
is apply the rules you have stated, without any deviations at all, even
when you can see that the predicted behavior is departing from the real
behavior.
That is really the hard part. Making a committment to a model sounds easy
until you start to have hopes that it will work. No model ever works
exactly as you thought it would the first time. You need the
self-discipline to avoid changing your premises or rules as you see
failure looming up. You have to avoid taking the easy way out by using
weasel-words whose meanings can be changed as needed without changing the
words, so it sounds as if you’re still making the same prediction as
before, only this time it’s right.
I’m pretty good at cheating like that, which is why I stick to computer
models. Deep down, I would really rather know the truth than be right.
Also, when you construct a computer model that you can’t possibly
interfere with once it’s running, there is no feeling in the world like
seeing the model do exactly what you thought it would do down to the last
significant digit. A few of us have experienced that; it doesn’t matter
that the models were simple, it’s still a miracle when you make contact
with actual reality in this way. It’s a rare experience and worth all the
effort to get there.
Best,
Bill P.