# Ignorance: pros and cons

**URL:** <http://discourse.iapct.org/t/ignorance-pros-and-cons/5517>\
**Category:** CSG1997\
**Created:** [November 26, 1997, 8:03am UTC](http://discourse.iapct.org/t/ignorance-pros-and-cons/5517 "1997-11-26T08:03:37Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![Oded\_Maler6](http://discourse.iapct.org/letter_avatar_proxy/v4/letter/o/5f9b8f/32.png) [@Oded\_Maler6](http://discourse.iapct.org/u/Oded_Maler6)\
**Post date:** [November 26, 1997, 8:03am UTC](http://discourse.iapct.org/t/ignorance-pros-and-cons/5517/1 "1997-11-26T08:03:37Z")

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[From Oded Maler 971127]

Posting in the middle of doing something else does not always result  
in the proper choice of words, so I would like to clarify what I said  
in my previous post. The background was a dispute between Hans (a  
control engineer) and Bill P. about "who is the teacher".  
Bill claimed that Hans is not really trying to learn PCT while  
Hans claimed that Bill is not trying to go beyond very basic and old  
genre of control theory. Now, the common usage (that is, everywhere  
outside this group) of the term "control theory" is for a body of  
mathematical and engineering techniques for making systems reach certain  
states. Incidently I have received an announcement of a web site with  
the abstracts of a major conference in the domain:  
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[http://master.ceat.okstate.edu/conference/cdc/cdc97.html](http://master.ceat.okstate.edu/conference/cdc/cdc97.html)

When I say that someone is ignorant in X, it is not meant to be a  
prejorative. Each of us is ignorant in 99% of the "things" in the  
world, and the only question is whether X is relevant to what one  
is doing. PCT, although concerned with the same phenomenon called  
"control", has some particular aspects:

1) It is interested in models that \*explain\* existing systems,  
not in building new systems.  
2) These systems have evolutionary, developmental and other particular  
hardware constraints.  
3) The theory is "sensor-centered", in that it emphasizes that from the  
point of view of the controller, the goal is to put \*perception\* in a certain  
state, rather than to control the "objective" state of the system. (This  
aspect is treated in control theory as observation functions, but the  
primary object \*is\* the objective state of the plant, and the change of focus  
is important).

In another, ideal world, Bill and other PCTers would have known the  
math, the techniques and the vocabulary developed in control theory  
(and in theoretical computer science) during the last 50 years, so  
that they could sort out the 99% which are irrelevant to PCT, and pick  
and use the rest. In this actual world everyone does with what he/she  
knows. The fact that PCT is put forward using old terminology and  
techniques is not a-priori an "objective" reason to reject (neither to  
accept it). It has its advantages in avoiding some complex models and  
red herrings which are irrelevant to the particularities of PCT, and  
disadvantages in, perhaps, missing some useful insights. On the  
subjective side, this restriction has effects: almost all the attempts  
to make control people look at PCT fail because they perceive it as  
naive. On the other hand, had the theory been more developed  
mathematically, the circle of PCTers who understand the technicalities  
would have become even smaller.

Le voila,

--Oded

---

<div class="post-metadata">

**Author:** ![Bill\_Powers1](http://discourse.iapct.org/letter_avatar_proxy/v4/letter/b/8e7dd6/32.png) [@Bill\_Powers1](http://discourse.iapct.org/u/Bill_Powers1)\
**Post date:** [November 26, 1997, 3:22pm UTC](http://discourse.iapct.org/t/ignorance-pros-and-cons/5517/2 "1997-11-26T15:22:47Z")

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[From Bill Powers (9711256.0744 MST)]

Oded Maler 971127 --

> Posting in the middle of doing something else does not always result  
> in the proper choice of words, so I would like to clarify what I said  
> in my previous post.

I haven't received your "previous post" yet, but this one is and excellent  
commentary on the problems that exist between MCT and PCT. As you say,

> Now, the common usage (that is, everywhere  
> outside this group) of the term "control theory" is for a body of  
> mathematical and engineering techniques for making systems reach certain  
> states.

This is correct; you can add "reach certain states by any means within the  
engineer's abilities." This includes the use of operations that require  
extensive quantitative knowledge of the environment as well as accurate  
models of the environment (in form, although the parameters are left for  
the system to adjust by itself). It also requires knowing what events in  
the environment are going to affect the variables under control. And it  
assumes that the system perceives the objective states of variables in the  
environment. Perhaps most important, it assumes that the control system can  
do mathematical computations of great complexity with perfect (symbolic)  
accuracy, such as computing the inverses of functions as they are being  
modified.

It's certainly to the modern control theorist's credit that he or she can  
work out computational methods of this kind to produce control systems that  
can adapt to (slowly) changing environments and maintain good control.  
However, I find it extremely unlikely that a spinal-reflex control system  
would depend on such calculations, especially those that require taking  
inverses, and \_most\_ especially in cases where there are no unique  
inverses. I find it unlikely that a cockroach could perform those  
calculations, not just because it probably can't do symbolic mathematics,  
but because there is simply not enough neural material present to do all  
that would be required even to \_evaluate\_ the equations. And cockroaches  
locomote in much the same way that all legged organisms do.

> PCT, although concerned with the same phenomenon called  
> "control", has some particular aspects:
> 
> 1) It is interested in models that \*explain\* existing systems,  
> not in building new systems.  
> 2) These systems have evolutionary, developmental and other particular  
> hardware constraints.  
> 3) The theory is "sensor-centered", in that it emphasizes that from the  
> point of view of the controller, the goal is to put \*perception\* in a  
> certain state, rather than to control the "objective" state of the system.  
> (This aspect is treated in control theory as observation functions, but the  
> primary object \*is\* the objective state of the plant, and the change of  
> focus is important).

Very nicely said. The MCT approach uses a naive-realist epitemology: the  
variables in the objective system are really there, and the control system  
simply senses them directly. Optimization is simply a matter of the  
engineer's choosing objective criteria such as minimizing some function of  
squared errors, without regard to \_why\_ that is assumed to generate some  
optimum result. In PCT, we must use more indirect methods, based on the  
idea that controlling perceived variables does have objective (side-)  
effects on the organism, and that these effects are also under control (the  
idea of the reorganizing system). All criteria of optimality trace back to  
basic variables in the organism that must be maintained near specific  
states in order for the system to continue functioning. All criteria  
involved in performing or altering behavior, in other words, are internal  
to the organism.

> In another, ideal world, Bill and other PCTers would have known the  
> math, the techniques and the vocabulary developed in control theory  
> (and in theoretical computer science) during the last 50 years, so  
> that they could sort out the 99% which are irrelevant to PCT, and pick  
> and use the rest.

That is the core of the problem. Most of what I understand of MCT is  
irrelevant to the model that I think applies to organisms. If it were  
relevant, and I couldn't do the math, I could find someone who can do it  
and boil it down to something I can understand. The real problem is the  
\_architecture\_, in which a world-model of the environmental feedback  
function is used as a simulation of the environment and the short-term  
action of the system is always open-loop. The outcome of this approach is a  
system that is very heavy on the computation side, so much so that it  
starts to look impractical as soon as one considers any realistic environment.

> In this actual world everyone does with what he/she  
> knows. The fact that PCT is put forward using old terminology and  
> techniques is not a-priori an "objective" reason to reject (neither to  
> accept it). It has its advantages in avoiding some complex models and  
> red herrings which are irrelevant to the particularities of PCT, and  
> disadvantages in, perhaps, missing some useful insights. On the  
> subjective side, this restriction has effects: almost all the attempts  
> to make control people look at PCT fail because they perceive it as  
> naive. On the other hand, had the theory been more developed  
> mathematically, the circle of PCTers who understand the technicalities  
> would have become even smaller.

There is a real break-even point here which we haven't reached. If good  
mathematicians could be persuaded of the basic architectural soundness of  
PCT as a model of organisms, the mathematical development of PCT would  
rapidly become respectable. The problem with MCT is that is is based on one  
particular architecture which, once you buy into it, leads you along  
inevitable paths of development that become as complex and sophisticated as  
the thinking that goes into it. Once you decide that the problem is that of  
calculating the output that will produce the desired reference state, most  
of the rest follows. You \_have\_ to compute inverses; you \_have\_ to deduce  
the states of all possible disturbances; you \_have\_ to have an internal  
model of the environment. If you want to compute output in this way, you  
have no choice. This is very much the hard way to achieve control, yet if  
you have enough patience and mathematical skill, and fast enough computers,  
you can do it.

If the same resources of mathematical skill and ingenuity were applied to  
the PCT approach, I'm sure the picture would be very different. The Kalman  
Filter, after all, has been around since the early 1960s. What it it had  
been applied to the self-organization of perceptual systems and hierarchies  
of control, instead of to the problem of predicting and calculating  
outputs? If there were 20,000 references to it in that context, PCT would  
be on a very different footing.

Thanks for a most useful post.

Best,

Bill P.