Our civilization, now more than ever before, places a premium on the excavation of knowledge and the means by which that knowledge is excavated. What this all seems to imply is that this should propel our civilization onward to a better way of living, of governing ourselves and running our society. The more we know, and the more we apply our ways of knowing, the more advanced we should become. Nothing could be farther from the truth.

The reason for this is we haven't developed a criteria for deciding, in an objective fashion, what is *right*. The argument I propose is that for any given situation, a set of rules can be adopted which will determine the *proper* course of action to be taken. If these rules, having been determined to be the best course of action, are followed, then we can advance, if decisions are not based on such rules, then the wrong course of action is taken, and we fail to advance.

The difficulty then, is in determining the proper set of rules or criteria by which to act, while abandoning the improper ones. Such rules will undoubtably differ depending upon the situation for which they are formulated, but commonalities should run through all. Civilization, as it exists today, abounds with these rules; they tell us that nature acts in particular ways, which are seldom violated, and that we and the systems of government which rule us must act in particular ways, or else risk punishment or change.

However, these laws are not used to guide us, either as individuals, or as a society, in making decisions and determining plans of action.

What is used instead is the simple judgement of the individual, or the mass judgement of many individuals in the form of a vote. It is through these two means that our future as a civilization is determined. The problem is that we place greater faith in free will and personal judgement when the decision is to be made by the individual, and on the democratic process when the decision is to be made by a group, than on the rules.

Let us start at the level of the individual. Everyday, each of us faces numerous decisions, some of which are of little consequence, others which will change the course of our lives depending on their outcome.

How are these decisions made? Well, it appears that we think of all the possible actions which we could take, and then evaluate what the outcomes of these actions are. The outcomes are then evaluated in terms of those which are most beneficial to the organism.

One plan may save time, another money, another effort. The organism concludes, for example, that it would rather stick with one of the possible plans over another because it considers its outcome the most beneficial.

In order to illustrate this, an example is needed. Let us suppose that after breakfast, you consider what you plan on doing for the day. You know that you must study, go grocery shopping, and visit the bank, but are expecting an important call sometime late in the morning. What should you do?

A set of rules can be followed in such cases to make the correct decision, if all the possibilities are specified, and the outcomes, in terms of their beneficence to the organism are known. If we abbreviate studying S, groceries G, and bank, B, then the possibilities are as follows:

Figure 1. (1-best, 4-worst)
Possibility	Criteria Satisfied	Beneficiality
1) SGB	CE, not T	2
2) SBG	CTE	1
3) GBS	TE not C	2
4) GSB	neither CTE	4
5) BSG	T not C or E	3
6) BGS	TE not C	2

We next impose an order of beneficiality on the possibilities, by forming constraints. The first constraint we have already mentioned, and is the telephone call. The second is that going to the bank cannot be performed last, because it closes early. The third is that it is a waste of both effort and gas to leave the house, come back, and leave again.

If these are the only constraints and possibilites, making the correct decision becomes possible. We see that choice 2 is the best, because you stay in to receive the call, get to the bank on time, and waste neither gas nor effort in leaving and returning only once.

Choices 1, 3 and 6 are second best, because in each you satisfy two of the constraints, but not the third, time being sacrificed in 1, and the call in 3 and 6.

Choice 5 is third best, because only the time constraint is satified. 4 is our worst choice; none of our constraints are satisfied.

If we abbreviate our 3 constraints as C for making the call, T for having the time to get to the bank, and E for the effort, either of car or person, then which of these possibilities satisfy which of these constraints may be illustrated in the second column of figure 1.

Most people, in making such a decision would have decided which they thought constitued the most important of the criteria, and would have simply studied first, in giving the phone call priority, or gone to the bank first and came back if giving this ultimate priority.

The point of this example is that all the criteria can be satisfied and the best decision made if the possibilities and the criteria are known. In other words, the more we know about these important qualities of the decision, the better the decision we are able to make. Obviously, as decisions become more complex, so do the means of solving them efficiently. But this is just the point.

Most people, in performing even the simplest of decisions, fail to follow any such ideal process or rule, either giving one criteria ultimate importance, or not using any criteria at all, as when emotion or instinct form the basis for a decision.

The way in which theories are formed in science also fail to show any sort of systematicity, or rule-governed behavior. This is especially intriguing, because it is the job of the sciences to describe nature according to these very principles.

Scientific theories have traditionally been either accepted or rejected on the basis of inductionism and falsificationism. Inductionism is the process of reasoning from particular empirical results to more abstract, generalized ones. Falsificationism is the process of rejecting theories by proving them wrong, also only on the basis of empirical evidence.

Pursuing science in accordance with inductivism is profoundly damaging in that it leads to the acquisition of vast amounts of observational and experimental data devoid of any theoretical interest or importance, while falsificationism, because it only allows empirical evidence as grounds for falsifying a theory, excludes all non-empirical means, such as philosophical, metaphysical and methodological considerations from science.

(see Maxwell, 1976 and 1984, for a complete criticism of these methodologies and of the way in which science is conducted, also see Kuhn, 19?? for a good discussion of scientific progress)

Other problems exist in the sciences. One is that in trying to explain their field of study, scientists often fail to address large issues. After tackling a smaller problem in the field which they hope will shed light on the larger issues, they often become absorbed by these smaller issues, failing to relate them to the general issues of the field as a whole. This results in a fragmentation, in which scientists end up formulating models for particular phenomenon, without regard to the functioning of these phenomenon in relation to the larger systems of which they are a part, and the other systems with which they must interact.

Even worse, scientists have, in the past, decided the course with which science progresses through personal choice and popularity. A new theory, even a good one, is always slow in being accepted by the scientific community.

Frequently, older theories will continue to be relied on, even though newer, competing ones can better explain the data. A case in point is the development of Einsteinian physics during the early part of the 20th century.

Einstein's theories were scoffed at initially, because they were so different, but were eventually accepted because they were better able to explain the physical phenomenon. One wonders what would have occurred had the opinion of the scientific community been less in his favor. Thus we see that a true theory may die, because the scientific community as a whole, votes to support a different one.

This method of 'voting', where the majority of people favoring one issue decide the outcome in favor of that issue, constitutes the second means by which decisions are usually made. Individual scientists, in making their own decisions as to which theory they favor, may decide its future. Those with the greatest reputations play a greater stake in this, but the overall number in each of the opposing camps is just as important.

We have already seen that individuals are usually incapable of making correct decisions, because they fail to take into account all of the information, as well as the pros and cons of each piece of information, in order to perform the appropriate evaluations and conclusions. Are we to let science be run by the whims and decisions of a few people?

If one person is unlikely to make a correct decision, then increasing the number of people having to make the decision does nothing to increase the likelihood that the correct decision will be made, because more people will make correct decisions, but the number of people making incorrect decisions also increases, with the net result no more appoximating the truth.

In fact, the situation is made even worse when a number of people together vote on an issue by taking sides, because many individuals become swayed by the opinions of others.

This process of voting to make decisions is hardly limited to the realm of science. We see it everywhere. In the legal system, a person is proven innocent or guilty by a jury of 12 men and women, where the sum of their decisions determine the verdict.

In government elections, the sum of the decisions by the people determine who will run the country. In all these cases, decisions are made subjectively, through the pooled opinions and decisions of the many.

Clearly, something should be done about how decisions are made, such that mankind may benefit and progress. If we have learned anything at all in this information age, it should be how to use the vast amounts of information and problem-solving skills we have acquired, and apply them to these decision making processes. It is the decisions which we, as people, make which determine our lives and whether or not we ultimately progress as a civilization.

Therefore, what I propose is that we develop methods of decision making which will permit us to overcome these inadequacies.

To begin, personal decision-making could benefit from early instruction. Different methods of problem-solving could be taught to children and then practiced on in-class examples. In this way, more objective and logical evaluation skills could be learned and engrained early on, so that as adults, such thinking would come more easily.

Such training might emphasize the ways in which emotions might interfere with, or cloud our decisions, and ways in which to be aware of, and prevent such interference. Too often, the curricula in our schools emphasize the memorization of facts over the training of analytic and critical thinking.

Also, there have been proposed new methodological means for how science should go about its business. Dobson and Rose (1984) elaborate on a model which eliminates many of the previously mentioned problems of scientific advancement. Their proposal consists of the following stages:

1) Define the problem or phenomenon to be studied. If we are interested in studying the visual cortex, then a complete definition of what the visual cortex is and does, as well as its relations with other brain areas, needs to be accounted for. This all-inclusive definition must be agreed to by all those studying it.

2) Formulate an exhaustive range of functional theories to explain the phenomenon in question. Since all possible models should be built and tested, we need a way to prevent the numbers of these models from becoming infinitely large.

3) Discriminate among models starting first at the highest, or most abstract, level of explanation, and then work downwards on more specific models. For example, one theory might explain very well how we perceive a number of visual illusions, but less well the more general phenomenon of the visual cortex, such as pattern recognition, locomotion, etc.

4) After finding out which low-level specialized models are successful and which are not, the merits of higher-level solutions can be assessed and appraised. In this way, the success of more specific models can serve as feedback to determine which of the higher level models are best. This stage alone can eliminate the tendency for scientists to become focused on smaller issues.

1) Efficiency - again, sticking with the example of the visual cortex, the most efficient model would be one which would require a minimum amount of information processing, biochemical energy required to work it, and amount of gene space demanded to reproduce it.

2) Reliability - how well does the model function in the face of adverse, or difficult conditions? Here, we could build a connectionist model, introduce random informational 'noise' into the inputs or circuitry and then measure the extent to which the model's ability to perform its overall function deteriorates.

3) Simplicity - models should be no more complicated than neccesary. A model with fewer parameters should be favored over one with more.

4) Developmental coherence - can the system develop from previous stages? This is especially important when the theory is driven from an evolutionary or developmental standpoint.

5) Working coherence - do the subsystems which compromise the system work cooperatively, or 'pull in different directions'?

6) Logical coherence - does the sytem function in the same metaphysical state as other models of related systems? For example, does this model of visual processing work according to the same fundamental principles as the similiar model which specifies auditory processing?

7) Completeness - how much of the phenomenon in question does the model cover? It can explain orientation selectivity, sure, but can it explain spatial frequency selectivity, aftereffects, etc.

8) Empirical evidence - does the evidence obtained from experimental work support the theory?

The idea here is that we can come up with working models, which can themselves later be modified after we have learned more about them through use. These models may differ, depending upon their application.

For example, the methods for making personal decisions, deciding among scientific theories, determining guilt or innocence, electing government officials, running the government itself etc., will all differ, although they should contain some common elements. We have already seen that the following principles will play an important role:

1) Define the problem - if all that is needed is the performance of 3 tasks, as in the personal decision problem given at the outset of this paper, then the tasks themselves define the problem. In trying to discover how the visual cortex functions, though, defining the problem space is much more difficult.

2) Enumerate all the possibilites or theories - again, in working within a limited domain, as when given 3 tasks, the number of possibilites is mathematically specified, but when dealing with more complex issues, this number may become infinite. Even one theory may be split up into an almost infinite variety, if subtle changes are introduced.

3) Establish criteria by which to distinguish among the possibilities or theories - this is a tricky issue, because in some instances, or depending upon your theoretical viewpoint, some criteria become more important than others.

4) Discriminate among the possibilities or theories using the criteria to arrive at the single best or several best - if we arrive at a tie, then how do we decide what is ultimately the best?

What is needed is an objective system of decision-making free from subjective biases. This is the take home lesson that we should be receiving from this age of information and technology and method, but is one which we blatantly ignore.

REFERENCES:
Dobson & Rose. (19??). Models of the Visual Cortex.
Kuhn. (19??). The Structure of Scientific revolutions.
Maxwell, N. (1976). What's Wrong with Science? Bran's Head Books, Middlesex.
Maxwell, N. (1984). From Knowledge to Wisdom: A Revolution in the Aims and Methods of Science, Blackwell, Oxford.

The TECHNOCRATIC movement believed that ALL government should be run by Scientists and Engineers. This would ensure that all operations of Supply and Demand would be optimized to achieve their most efficient mode through the use of mathematics, cycles, statistics and all aspects of the sciences.

It was a most admirable system not only because it sought the greatest good without the acquisition of power or the inflation of ego, but even included members of the ministerial ranks to assist in decisions relating to moral issues. We have a very rare book about the movement which will someday result in a file detailing some of their proposed methods.