Characterising the learning process in the Vector representation

	Problem Find the "target" when all that is known is the (scalar) quality of control. The vector components cannot be estimated, nor can the direction to the target.
	Approach to a solution (e-coli) Alter the vector {a1,...ak,bm,...bn} by a unit increment in a random direction. If control improves, alter further in the same direction. If it gets worse, make a new random choice of direction. (Colloquially, in PCT discussions, this is called "e-coli" learning, since it is based on the behaviour of the noxious bacterium).

The rationale for e-coli learning is that there is no a priori way of knowing how to act differently so as to improve control of any particular perceptual signal. It is a classic hill-climbing optimization problem in a high-dimensional space. The hill may not be monotonic, so the e-coli can get stuck in sub-optimum locations. But it is a simple mechanism and fairly robust against minor deviations from regularity in the hill-slope, because there is always going to be some movement away from wherever the e-coli finds itself, even when it is truly at the local (or global) optimum.

One problem with e-coli learning in a very high-dimensional space is that almost all directions are nearly orthogonal to the direction toward the target. This means that progress can be very slow for long periods, interrupted by short bursts of great change. (This is very reminiscent of the progress of evolution generally, with long periods of near stasis followed by bursts of change. It is called "punctate evolution" which has provided evolutionary theorists with considerable numbers of unnecessarily published papers).

Reorganization--The "Winter Leaf" effect

The side-effects of controlling a perception cannot reliably influence the intrinsic variables unless the perception itself can be reliably controlled. Accordingly, a reasonable surrogate "intrinisic variable" is the effectiveness of control in the environment in which the control unit finds itself. Poor control must be improved, which is what the e-coli mechanism does in a very simple way.

The e-coli mechanism is erratic. The only consistent thing about it is that when it is far from the target it tends to move long distances, but when it is close to the target it moves back and forth without going very far. Its behaviour is like that of a winte leaf in the wind. In open spaces, the leaf is blown along until it finds itself in a place shelteed from the wind, and thee it is likely to stay a while. As a result, winter leaves blow away from most spaces, to pile up in large localized drifts. In the same way, reorganizing control systems drift toward organizations in which they control better than they would in "neighbouring" organizational structures--and in such "good" organization, the chemical (real?) intrinisic variables are also well controlled.

On other grounds, Kauffman has shown that modularization improves the optimization of evolving interacting systems. In the context of control, this means that the space in which e-coli must work, there may not be a very large number of dimensions, and progress toward the target may be reasonably regular.