Unlike the companion paper which considers certain questions in
depth, this paper presents a survey of the scope of our work in
self-organizing systems and is not intended to be profound.
The approach we have followed may be called phenomenological (Figure
1). That is, the desired behavior (self-organization) was defined,
represented mathematically, and a mechanism(s) required to yield the
postulated behavior was synthesized using mathematical techniques. One
advantage of this approach is that it avoids assumptions of uniqueness
of the mechanism. Another advantage is that the desired behavior, which
is after all the principal objective, is taken as invariant. An obvious
disadvantage is the requirement for the aforementioned synthesis
technique; fortunately in our case a sufficiently general technique had
been developed by the author of the companion paper.
From the foregoing and from the definition of self-organization we
employ (see conceptual model), it would appear that our research does
not fit comfortably within any of the well publicized approaches to
self-organization (2). Philosophically, we lean toward viewpoints
expressed by Ashby (3), (4), Hawkins (5), and Mesarovic (6) but with
certain reservations. We have avoided the neural net approach partly
because it is receiving considerable attention and also because the
brain mechanism need not be the unique way to produce the desired
behavior.
[Illustration: Figure 1—Approach used in Nortronics research on
self-organizing systems]
Nor have we followed the probability computer or statistical decision
theory approach exemplified by Braverman (7) because these usually
require some sort of preassigned coordinate system (8). Neither will
the reader find much indication of formal logic (9) or heuristic (10)
programming. Instead, we view a self-organizing system more as a mirror
whose appearance reflects the environment rather than its own intrinsic
nature. With this viewpoint, a self-organizing system appears very
flexible because it possesses few internal constraints which would tend
to distort the reflection of the environment and hinder its ability to
adapt.