Essential Elements For Intelligent Computer Systems
Thomas E. Berghage
The security analysis system of the future must be capable of operating freely in the dynamic hyperspace of economic markets. To do this it will have to include two key elements; a Cybernetic feedback capability and a Gestalt analysis capability. By Cybernetic feedback capability we mean that the analysis system must be capable of adjusting itself to stay in touch with the ever-changing market conditions. It also needs to be able to make up its own rules for discriminating between good and poor investment opportunities so it is not constrained by the human thought process. The system also needs to be capable of performing what we call a Gestalt analysis. An analysis that is not constrained by available data, but is capable of detecting what is known as emergent information, information that is based on relationships or patterns among data items that are far too complex for humans to detect or understand. The patterns that are present in the whole that are more than just the sum of the individual parts.
A Cybernetic Process
In 1948 Norbert Wiener published his now famous book, Cybernetics: or control and communication in the animal and the machine. Wiener’s book developed the notion that feedback was essential for the design of dynamic systems. Weiner’s ideas regarding feedback are important for future security selections systems for two reasons: first, as Weiner points out, feedback is essential if you are dealing with a dynamic problem such as market change; second, however, feedback is also important in detecting the hidden patterns to be found in the relationships in the data, the data Gestalt.
If you are dealing with a static problem where the rules and variable weighting do not change, technologies such as Expert Systems are just what you need. They allow for the consistent application of the same logic and analysis to problems that do not change. These systems produce appropriate results for fixed problems that are hard to beat. If, however, the problem is constantly changing and evolving you need some type of feedback in the system design that will adjust the rules and their weightings to keep the system in touch with the changing environment.
Perhaps one of the most familiar examples of a feedback system is the thermostat: it achieves a constant temperature in any enclosed environment by assessing the actual room temperature, comparing it to a desired temperature, and then responding - by turning the heater (or air conditioner) either on or off according to whether the existing temperature lies below or above the desired value. The word feedback describes how the process returns (feeds back) the results of the control action (the temperature) to the compensating mechanism. Cybernetics (the science of control) is a mathematical theory of information feedback. In essence, feedback mechanism are information processing devices that receive information and then make decisions based on it. Wiener speculated that all intelligent behavior is the consequence of feedback mechanisms; perhaps by definition, intelligence is the outcome of receiving, processing, and acting on information.
The other important aspect of feedback is it provides a mechanism whereby a system can change its structure. Genetic Algorithms (GA), Evolutionary Programming (EP), and Neural Networks (ANN) all use feedback to change their structure. The first two, GA’s and EP’s use performance feedback to eliminate under-performing systems while allowing surviving systems to generate new offspring. The disadvantage of this approach is that some information tends to get lost with the killing off of under-performing systems. Neural Networks on the other hand retain all of their information and restructure themselves by adjusting their weight structure. If history does tend to repeat itself, Neural Networks should be superior because they still have available all of the information that may have worked in the past. It just needs to reactivate the information by increasing its connection weights. The structure of the Neural Network with all of its interconnections among neurodes provides an almost infinite number of combinations of data items thus providing a very rich environment for the feedback mechanism to work with. The availability of all of these combinations of data items along with the feedback mechanism allows the network to perform a Gestalt analysis and discover emergent phenomena.
A Gestalt Analysis
There has been a shift taking place in science over the last few years. A shift from reductionism, the idea that understanding comes from reducing everything down to its smallest component parts, to the realization that the whole is often greater than the sum of its parts. This belief is now being incorporated into financial analysis. To describe this phenomena we would like to borrow a term from the early German psychologist, Max Wertheimer. He used the term, Gestalt, to describe the experience his subjects had during a perceptual experiment in which they reported movement of a light when no movement actually took place. This and several subsequent experiments demonstrated that the whole perceptual experience reported was more than the sum of the individual stimuli presented to the subject. The term Gestalt in German has two meanings; first, a Gestalt is an object which has shape, an entity in itself which has form, as a chair or table; second, a Gestalt is a property of things as in squareness or triangularity. Taken together then, Gestalt is both the object and the form characteristics of that object, its essence. Sometimes the words “configuration,” “structure,” and “whole” are used as English translations for Gestalt, but the un-translated term is preferable, since none of these words capture its complete meaning.
We experience Gestalts in everyday life all of the time. Motion pictures contain no real motion, but you would have a hard time understanding that in some of the new Cinamax theaters. When we perceive a piece of music we hear the melodic form, not a string of individual notes. The melody seems to emerge from the serial pattern of notes. The term emergent seems to be popping up more and more as people describe situations where the properties of the whole seem to be more than the sum of the individual parts.
In biochemistry many complex molecules have the same atoms but combine in more than one way (pattern of connection). The properties of the compound are found to depend on the relationships formed in combination; that is to say, the properties of the compound depend in part on the relationships which are shown in the structural chemical formula, a formula which can vary while the structural atoms remain the same. Since the relationships between elements exist only in the compound, it is plain that at least the relationships emerge in the whole, and that the whole is more than the sum of its parts because it is the parts in relation to each other that matters.
It does not take much imagination to take this concept a few steps further to suggest that the modern day corporation is more than its individual parts and that its analysis requires the ability to detect emergent patterns. No single measure or group of measures in isolation can tell you about a company’s current performance or future potential. You must be able to evaluate the whole and detect emergent patterns or the corporate Gestalt.