INTRODUCTION
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.
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.