Applied ESP: Managing and problem solving with the aid of the ‘unconscious’
by John Mihalasky
Professor Emeritus of Industrial Engineering
Director, PSI Communications
New Jersey Institute of Technology
Newark,NJ
In the last few years it has become increasingly difficult for business and industry to stay competitive. Critics charge that there is too much reliance on “short-term thinking” and on the fear of taking risks.
With more data being generated by more and more computers, there has been a tendency to slip into a posture of “managing by the numbers.” The emphasis has been on the use of rationality and logic in problem solving and decision making – operations research, management science, modeling, and the development of computers that “think.”
Unfortunately, all this has given us more and more incorrect, invalid, and/or unreliable data, faster, to make decisions whose outcomes have been correct about as many times as when we made decisions by holding a wet finger up to the wind.
It is my contention that this state of affairs is due to the fact that not enough has been done to investigate the application of non-logical, non-rational, unconscious thinking. We have spent most of our time on rational, logical, conscious thinking and it is (has been for a long time) necessary to delve into the use of the unconscious.
The purpose of what follows is to explore the basis for the use of the unconscious – ESP, if you will – in the problem solving and decision making process.
Problem solving & decision making
A decision is loosely defined as a choice between alternatives, that results in an allocation of resources – money, personnel, time, etc.
There are good decisions – good in the sense that they were logically made – and there are also good outcomes. The assumption is that a good decision will lead to a good outcome.
Decisions are made from the considerations of objective data classed as certainties, and objective and subjective data classed as uncertainties. The manager – using the word in its broadest sense – is constantly faced with the integration of uncertainties with certainties, to make decisions.
The manager is thus, by profession, a decision maker, and is constantly faced with the integration of certainties with uncertainties. However, these professional decision makers are very frank about their inability to explain and analyze their acts of decision.
Former General Motors President Alfred Sloan, commenting on the company’s founder William C. Durant, characterized him as a man who “would proceed on a course of action guided solely, as far as I could tell, by some intuitive flash of brilliance. We never felt obliged to make an engineering hunt for the facts. Yet at times he was astonishingly correct in his judgments.”
Now, half a century later, in this age of computers with their realms of data, of impressive charts, graphs, decision trees, and probabilistic and statistical technology – of reliance on the quantitative – it is becoming more and more difficult for many executives to live with, much less acknowledge, the fact that a great many of their important decisions are, as Durant’s seemed to be, intuitive. There seems to them to be something unscientific, unprofessional, about “flying by the seat of one’s pants,” acting on what seems to be little better than hunches or instinct – at least, so these executives believe. Therefore, they do not often discuss it.
Yet, the higher a person rises and the more complex their decision process, the more incomplete the support evidence available to them is, and the more intuitive their decisions must become.
At positions low in the organization hierarchy the decisions are delineated, limited, and repetitive. At the highest levels, assuming the practice of the Exception Principle, decisions are very broad and have much more to do with future events. At the lower levels, the decisions are less complicated, based on more reliable and objective data, and less costly if a mistake occurs.
At the higher levels, they get very complicated, have to be based more on suspect, unavailable, and/or non-existent data, and often carry a large dollar sign on them. Therefore, the pressure is greater at the top to make profitable decisions.
The decision makers of the future will find themselves making decisions involving even more considerations than are present today.
Classifying decisions
To try to better understand the decision making process, H.A. Simon classified decisions into programmed and non-programmed.
The programmed decision is repetitive and routine, has a definite procedure worked out for it, and does not call for a novel approach each time a problem comes up.
The non-programmed decisions are novel, unstructured, have no “cut and dried” method for handling the problem, and call for intelligent adaptive and problem oriented action when solving the problem. He further notes that the traditional techniques for these Non-Programmed Decisions are judgment, creativity, and intuition.
C.I. Barnard categorizes decisions into logical and non-logical.
Logical decision making involves conscious thinking and reasoning, and the process is expressible in words or other symbols.
Non-logical decision making is not capable of being expressed in words, or as reasoning, and is made known only by the action itself. He feels that this may be because these decision making processes are unconscious ones.
The basis for decisions
The categorization of decisions raises the question of when each type is utilized.
The answer to the question is that it is the type of data or information available to the decision maker that determines what category of decision making will be used.
Referring again to Barnard, materials for decision making can be classed as precise information, hybrid information (data of poor quality or limited extent, doubtful validity, qualitative, etc.), and information of a speculative nature (data not susceptible to mathematical or probabilistic expression, has uncertainties, difficult to ascertain, based on impressions, etc.)
F.I. Shartle states: “Most decisions are made on the basis of incomplete evidence. Facts may not be available, or there may not be sufficient time or staff assistance to uncover or assemble them. Thus, a good executive must be a good guesser. He must piece together the fragments of facts he has, and act accordingly. Some executives have reputations for being uncanny in making the right decision without apparent evidence.”
Bernard was also of the opinion that the large part of the data for decision making was of the non-precise variety.
All through the decision maker’s life, he or she has been taught to depend on facts and logic; but yet, the data used for decision making does not lend itself to the logical processes. Psychologists also tell us that most decisions are not based on a logical approach, but on an emotional basis.
There has been so much emphasis on the dependence of reason that the decision maker becomes afraid to trust his own judgment.
Ray Brown also mentions this problem in his book. He stated that people tend to be suspicious of decisions that arise from a person’s individuality. Thus, decision makers try to make everything look objective.
In reality, they make the decision, then look for the facts to support it.
He further comments that if decisions spring from the facts, the decision maker would not be needed because he would be a transmitter and contribute nothing.
Simon also noted the lack of an objective basis for decisions by stating that:
1. Regarding incomplete knowledge: “In fact, knowledge of consequences is always fragmentary.”
2. Since consequences lie in the future, imagination must supply the lack of experienced feeling in attaching value of these consequences.
3. Very few of the possible alternatives are available.
When the basis for a decision is precise, reliable, and objective, the methodology of logic and reasoning or the scientific method can be used.
However, the above indicates that such decisions would probably be made at the lower levels of the organization.
John McDonald was of the opinion that business executives make mostly repetitive and routine decisions. Some executives estimated that their routine decisions comprised up to 90 percent of all the decisions they are confronted with. Therefore, the novel or “true” decisions are infrequently made.
With the advent of the management sciences, and of the computer, a great deal of work has been done with data that is not so precise, reliable, etc. This methodology, given the title of Decision Risk Analysis or Cost/Benefit Analysis, is an example of such an approach. It is an approach that has been helpful to modern day managers.
Unfortunately, the advent of the management sciences and the computer also brought with it a renewed attempt to quantify everything. This has led the decision maker to forget that these techniques and tools were designed to aid in the collection and analysis of data but will not make the final decisions for him.
Ernest Weinwurm, in his 1960 American Society of Mechanical Engineers Report discussing progress in Management Science, decried the overemphasis on quantification of data, and the lack of attention given the decision maker.
R.E. Brown speaks of a “data addict” who sits in front of the computer waiting for decisions to come out. He further states:
“The top billing being given to scientific methodology in decision making is causing administrators to adopt increasingly an ’embarrassed to know you’ attitude toward intuition. Because intuition occurs in the absence of had facts, or ahead of the facts, it is being written off as being in the same company as the divining rod of the well digger and the tea leaves of the soothsayer. The inability of intuition to identify the specific facts with which it associates is causing it to be increasingly considered intellectually disreputable. However, it is just this facility for producing ideas of nebulous origin that makes intuition such an extremely able adjunct of the administrator’s judgment.”
It seems that non-logical, non-programmed decisions have been ignored. A methodology for them is not being developed with the fervor reserved for decision making under certainty, and under uncertainty and risk. If one looks at the writings in the field, the back of the book may say a little about decision making based on data of a speculative nature. Intuitive, or precognitive decision making is “persona non grata.”
How executives make decisions
How do executives actually go about making their important decisions?
McDonald surveyed a few executives and found that “they are remarkably candid about their own inability to analyze the act of decision.”
A sample of the replies were:
Charles Cox, president of Kennecott Cooper says: “I do not think businessmen know how they make decisions. I know I do not.”
Charles Dickey, chairman of the executive committee of J.P. Morgan & Company says: “There are no rules.”
Benjamin Fairless, ex-chairman of U.S. Steel: “You do not know how you do it.”
John McCaffrey, president of International Harvester: “It is like asking a pro baseball player to define the swing that has always come natural to him.”
Dwight Joyce, president of Glidden Company: “If a vice president asks me how I was able to choose the right course, I have to say, ‘I am damned if I know’.”
TheNew Yorkreal estate and theater wizard Roger Stevens, reversing Thomas J. Watson’s famous maxim, says: “Whenever I think, I make a mistake.”
Fletcher L. Byrom, president and chief executive officer of Koppers Company, giving top executives advice, said:
“I have found that some of the most horrible mistakes we have made came after I ignored my intuition under the pressure of what looked, at the time, like unshakable evidence.”
In a personal letter to the author, the manager of the corporate economic planning division of a major oil company, who has been in the operations research field for about 12 years, wrote:
“I have been getting the unsettling feeling that many decisions are, and perhaps have to be, based on what I would call ‘gut feel’ or intuition.”
The Wall Street Journal of November 3, 1971, began a front page article on Charles G. Abbot, the retired secretary of the Smithsonian Institution, with the following words:
“Charles Greeley Abbot dreams things up.
“At two o’clock on the morning in July 1965, for example, Mr. Abbot awoke in his suburban bedroom to find that he had been dreaming about something that had not been invented yet. So he invented it. Then he went back to sleep.
“Things happen that way with Mr. Abbot, inventor and scientist and sage. Some of his best ideas came as dreams in the middle of the night.”
Based on both this and other written material, as well as a great deal of oral material gathered form discussions with executives, it does seem that executives do use intuition or precognition in their decision making process.
Today’s managers are paid to find and define problems. Analyze them, develop alternate solutions for them, decide upon the best of these alternates for each problem, and, finally, act upon these decisions.
In the main, these decisions have to turn out to be profitable ones, or the decision maker is forced to vacate his position.
The PSI Communication Project at the Newark College of Engineering has been conducting research into the phenomenon of precognition, and the nature of the precognitive maker.
There is now evidence to suggest that the successful “hunch player” may have something more solid going for him or her than the odds of chance.
Experiments that the author has conducted indicate that what the texts call “non-logical” (and what managers privately call “lucky”) decisions have some scientific – that is observable, dependable, and explainable – support.
The research project strongly supports the idea that some executives have more “precognitive” ability than others – that is, they are better able to anticipate the future intuitively rather than logically and thus, when put in positions where strong data support may not always exist, will make better decisions.
Moreover, a valid test has been developed for determining which people do, and which do no, have this ability.
The test consists of asking the participants to guess at a 100 digit number not currently in existence. Each of the 100 digits can take on any value from 0 to 9. The target that each participant guesses at is later generated by a computer using random number techniques. Each participant has his or her own specific target to guess at.
As expected, some people guess above the chance level of 10 correct guesses out of the 100 digits, while others guess at, or below, the chance level.
That some people score above chance on this test would, by itself, not prove they have precognitive ability. But the research has revealed some interesting and significant relationships between high scores on this simple guessing game and other kinds of data.
For example, participants are asked to rank their preferences among five metaphors (i.e. “a motionless ocean,” “a dashing horseman”) that have been adapted from a psychological test.
Based on their choices, the subjects are divided into “dynamic” and “non-dynamic” types. Admittedly, this is not a very sophisticated classification. But invariably, those classified “dynamic” by this relatively simple means, also tend to score above chance in predicting the computer’s random numbers.
In tests on 27 different groups, ranging from four members to 100, “dynamic” men outscored “non-dynamic” ones in 22 of the groups. Statistically, the chances of this happening by accident are fewer than 5 in 1,000. Many other groups were tested since this initial 27 groups.
But what does “dynamic” executive mean? Whatever it connotes, it also must be measured somehow by performance.
So four groups of chief operating officers of corporations, all of them in their present jobs at least five years, were asked to take the tests. These men had held office long enough to be responsibility for the reliability of their decisions and the recent performance of their companies.
The first two groups of chief executives who met the above criteria were divided into two classes – those who had at least doubled profits in the past five years, and those who had not, including some who had lost money.
Of the 12 men whose companies met the deliberately extreme test of double profits, 11 scored above the chance level on the computer guessing game. One scored at the chance level. Not a single one fell below chance.
Of the 13 who had not doubled profits, seven scored below chance, one scored at chance, five scored above chance. This last five had improved profits by 50 to 100 percent.
Of the seven who scored below chance, five had improved profits less than 50 percent. Only two of those who scored below chance had improved profits more substantially than that.
The chief executives who had more than doubled their companies’ profits in five years had an average score of 12.8.
Those who had not met this criteria scored an average of 8.3, well below what they should have achieved even on a random basis.
To give one striking example of the difference between the two groups:
Over a five year period, one president had increased his company’s annual profit from $1.3 million to $19.4 million. His test score: 16.
Another had been able to increase his profit by only $374,000. His score: Eight.
A third group of presidents, 41 members of the Steel Distributors Association, participated in an experiment done at the Princess Hotel inAcapulco,Mexico. Of the 41, 11qualified for the category of “company president of at least five years of tenure.” Of the 11, nine had at least doubled their company profits over the last five years. Eight of the nine scored above chance. Their average was 11.44 percent. The remaining two presidents who had not at least doubled their company profits averaged 9.5 percent, with both scoring at chance or less.
The fourth group was composed of 20 Canadians. Of the 20, six “qualified” as company presidents who had been on the job for at least five years. Five of the six at least doubled the profits, while one fell in to the 50 to 95 percent improvement class. Of the five doublers, three scored above chance, while the other two were below chance. The sixth person scored at chance level.
Here is a summary of the results of the four groups:
PRESIDENT’S SCORES
Percent increase in profits over the last five years
Increase: |
Above Chance |
Chance |
Below Chance |
Greater than 100% |
81.5% |
25% |
27.3% |
50% to 99% |
18.5% |
50% |
18.2% |
Below 50% |
0% |
25% |
54.5% |
This finding has interesting implications for selection of executives for the “top spot.”
Given a group of people who have the usual traits needed for such a position, which one should be selected?
This author feels that it should be the person with the extra something, in this case the ability to make good decisions under conditions of uncertainty.
In the groups of presidents who were tested, had the selection been made on the basis of their scores, there would have been an 81.5 percent chance of choosing a person who at least doubled the company profits, while if a below chance scorer had been chosen, there would have been only a 27.3 percent chance of choosing a person who would have doubled the company profits.
The engineer and problem solving
The function of the engineer is to solve problems. To do this, he must come up with ideas that result in new methods, techniques, processes, products, or equations.
However, the engineer does not merely solve “text book” problems that call for selection of the necessary facts or formulas from the appropriate handbook. On the contrary, the vast number of problems that the engineer confronts daily are of the type that do not have just one answer. Several solutions are possible.
Logically, if the many possible alternative solutions could be generated and analyzed, an optimum, or least a satisfactory, solution would be reached.
In solving problems, the engineer will also have to delve into areas beyond the confines of established technical knowledge. The engineer has to consider variability and uncertainty.
In the area of Risk Decision Analysis, the Department of Defense has studied the facets of uncertainty and come up with a breakdown of uncertainty into “known unknowns,” where you are aware of your areas of uncertainty, and “unknown unknowns,” where you are not aware of your areas of uncertainty.
The engineer has to deal with these uncertainties, when designing a product for future use, where the characteristics of the user and the environment may only be guessed at. In addition the engineer has to deal with these uncertainties when making decisions as to which alternative to pursue. Should method A be chosen over method B? Is material A better than material B? Which of several possible product developments should time and effort be spent on?
A large west coast based electronics company founder recently related to the author how he decided to “go” with a newly developed product, still in its infancy, contrary to the advice of five experts, and of his partner, who, as a result, left him; and he succeeded in building a multi-million dollar corporation around the device.
The variability, uncertainty, and diversity of the components of a technical problem can easily upset the most painstaking engineering calculations.
Besides technical know-how, the engineer has also to have what is usually called engineering judgment.
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