This is a paper someone wrote that was a background paper for a decision making product, Which & Why. The technology or algorithm is now called ‘ebestmatch’ and used in the product “Ergo“. See United States Patent 6151565 “Decision support system, method and article of manufacture”. Copied as a blog post here since the paper does not seem to be authoritatively hosted anywhere.
White papers from original Ergo product:
A Brief History of Decision-Making
Decision making is not an exclusively human problem. Anyone who has watched a dog agonizing over whether or not to snatch an unguarded steak will understand that choices and consequences are being weighed inside that canine mind. However, the fact is that lower life-forms face fewer decisions. Their choices are limited, less complex, and usually “hardwired” into instinctual patterns and rituals.
A little further up the evolutionary ladder, human decision makers, with the mixed blessing of some capacity to think and choose, having been looking for support for their decisions since the beginning of recorded history,
In earlier times, societies consulted their elders for alternatives and experimental data about the probability of success for decision choices in similar situations. Then, at some point, this advisory function shifted to soothsayers, astrologers, and religious figures — the management consultants of the day.
Alexander the Great regularly consulted oracles and fortune tellers on the eve of great battles. Always a creative general, Alexander was not looking for innovative battle strategies from his advisers. What he needed was advice about the potential outcome of the untried strategies he already had. This information could only be provided by those who claimed to have a “window” on the future: fortune tellers, high priests, and the like.
THE BUSINESS OF PREDICTION
Oracles eventually tired of being at the whim of leaders like Alexander. They set themselves up in temples and hermitages, thus creating the first consulting houses. One of the most famous was at Delphi in ancient Greece, high on Mount Parnassus overlooking the Aegean. Advice seekers like Alexander were mainly looking for a glimpse into the future. They were impatient to see the consequences of a choice, rather than a way analyzing current data or alternatives. According to historical reports, most of the Delphi Oracle’s advice was sufficiently cryptic and vague to stand up to scrutiny, regardless of the outcome of the decision. With the rates charged and its great location, the Oracles guaranteed itself an exclusive market position with its clientele. This approach is still used by many business consultants.
The early Romans also had their oracles, but leaned heavily on interpretation of “hard” data. Their specialty was the explanation of natural phenomena such as where and how lightning would strike. The other data interpretation was done by Haruspicists. They were an organized guild dedicated to the inspection and analysis of animal entrails. The Romans also turned prediction into a “fast-fortune” business. The first coin-operated machine is said to have been a Roman oracle in which fortunes, written on parchment, were dispensed when a coin was deposited into a slot. There is no mention whether the advice-seeker’s weight was included.
LOOKING FOR STRUCTURE IN CHAOS
The Chinese, while equally fascinated by divination, were also searching for ways to integrate prophecy with a more systematic process of decision making. The result was I Ching, first developed in 3,000 BC. The I Ching integrated Chinese world views about the primeval forces of yin and yang; cycles of the calendar; and the interaction of the elements of water, earth, and fire.
The actual divination process involves asking the I Ching for the prognosis of a given decision. The answer consists of a generic evaluation of the situation as well as the potential risks and opportunities.
As a decision making tool, the I Ching has about the same real value as an Ouija board. However, as a decision making process, it does offer valuable lessons: proceed slowly, consider the alternatives, identify risks, and build contingency plans before choosing a course of action. This focus on careful research, data collection, and data analysis before making the decision is entirely consistent with modern decision making practices.
In summary, much of ancient decision making was haphazard, largely focused on guessing or sensing the outcome of a given choice, rather than generating creative choices and then systematically evaluating them. While elders were consulted for knowledge and predictions, fortune-tellers often recommended courses of action which changed the course of history — though not always as predicted.
THE AGE OF REASON AND BEYOND
Throughout the Middle Ages, the Roman Catholic establishment discouraged the practice of prophecy as well as research into many scientific areas. The official reason was that since all decisions would ultimately be affected by God’s will, human decision making is trivial and/or irrelevant.
In the second half of the 16th century, England was the home of two of the most brilliant contributors to the study of decision making: Francis Bacon and William Shakespeare. Bacon’s contribution was to attempt development of the scientific method. Shakespeare’s efforts include many tragedies on the consequence of decisions, including Othello, King Lear, Romeo and Juliet, and others. The most profound was Hamlet which reflects on the agony and terrible consequences of psychological indecision.
A century and a half later, Benjamin Franklin turned his analytic mind to decision making. He is credited with developing the “balance sheet” approach, which gives a simple, workable way of structuring information for evaluation. Franklin recommended making a two-column list of the pros and cons of each alternative and then calculating a “middle line” value. His evaluation technique may seem naive by present-day standards, but his information documentation process is hard to fault. It lists not only what is known about a given choice, but also points out the information gaps that must be filled before a decision can be made. In more recent times, the authorities Wheeler and Janis have developed an updated version of Franklin’s balance sheet method as part of their own decision making model.
SYSTEMATIC DECISION MAKING
Modern technology and psychology have attempted to tame the great decision dilemma in the 20th century. Application of Bacon’s scientific method in the area of psychology has led to major revelations about how people make decisions, pointing out typical flaws in our interpretation of the data that influences our choices, and quantitative techniques to give value to what we feel.
With the advent of computing power, along with the development of more sophisticated statistical analysis techniques, an opportunity arose to overcome the decision maker’s prime obstacle: too much disparate data to handle at one time. Approaches therefore began to focus on the process of data collection and analysis to support, and even to replace, human decision making.
Going one step further, H.A. Simon set out to build the General Problem Solver, an algorithm capable of solving problems, including those of a decision making nature. While his laboratory is the computer room, his field studies have taken him from corporate boardrooms to clinical group therapy sessions.
For many, the decade of the fifties was the golden age of decision making as well as rock roll. Social and cognitive psychologists were establishing base line data on how individuals made decisions and solved problems. Scientists set to work studying how executives and management teams worked, and began building theories and models based on that data.
One of the hot-beds of group behavioral research was a US federally-funded project that went by the innocuous title of National Training Laboratories. It became a Mecca for social scientists to experiment and theorize about small group dynamics, and a number of major discoveries, as well as academic reputations, were made during its operation.
One of the outcomes of the N.T.L. work was the development of group activities to stimulate social interaction and thinking. The best known of these is “brainstorming.” First created to overcome the natural reluctance of people to participate openly and honestly in groups, brainstorming is a technique in which a group facilitator asks participants to offer a stream of alternative solutions for a given problem or issue. The rules of the process are that all participants must make a contribution, which the facilitator records verbatim. Other participants must then encourage and build on these suggestions without resorting to negativity. The objective is that at the end of the brainstorming session, a lot of creative data will have been recorded, as well as some of the subjective needs and concerns of the group.
When the technique was published in the influential journal “Developing Human Resources,” a number of facilitators were stymied about what to do with the accumulated data. Brainstorming continues to be a tool to generate and collect a large pool of potentially useful data, but it must still be edited, classified, and evaluated in an objective manner, something that the group itself is not necessarily qualified to handle.
KEPNER AND TREGOE
Based on work, done at the N.T.L. labs, Charles Kepner and Benjamin Tregoe developed a practical methodology for problem solving and its cousin, decision making. Using an analysis model that would have made sense to Francis Bacon, Kepner and Tregoe designed a business-friendly process to isolate problems, generate alternative solutions and to evaluate the best solution. And suddenly, “eureka!” The first complete problem-solving and decision-making system.
In essence, their process consists of a three-phase problem solving process, of which decision making is but one phase. One way to describe the process might be as a series of steps, which include:
- Define the problem.
- Formulate a complete decision objective.
- Generate criteria.
- Generate alternatives.
- Rate how well each of the criteria are met for each alternative.
- Compare the scores for the alternatives.
- Choose the alternative with the best score.
However, The Kepner-Tregoe process has not become the universal business methodology for decision making for a number of reasons. Reports indicate that the finely-constructed case studies that respond so well to the KT process in the classroom are not necessarily an accurate reflection of real world problems or the dynamics of people who make decisions.
The KT process was infinitely more complete and sophisticated than any previous attempt, but still did not address the issue that decision making must allow for the so-called “soft” factors. The subjective or affective domain plays an active role in establishing criteria for even the most mechanical of decisions. Whatever system is used must therefore allow all types of criteria to figure in the evaluation process. Furthermore, since all criteria are not of equal importance, the ideal process must provide a method for comparing and weighting the criteria in a way that reflects both their subjective and objective values.
As Carl Jung stated in his 1923 text “Psychological types,” “we should not pretend to understand the world only by intellect; we apprehend it just as much by feeling. Therefore, the judgment of the intellect is at best, only half of the truth, and must, if it is to be honest, also come to an understanding of its own inadequacy.”
FORMAL DECISION THEORY
Beginning in the sixties, and working at the fringes of statistical methods, a number of social scientists were attempting to build mathematical models of subjective reality. For example, Likert used a scaling technique for measuring what words meant to different people as a means of evaluating public opinion and measuring intercultural perceptions and prejudice.
A decade later, Thomas Saaty developed the Analytical Hierarchy Process to measure the subjective “distance” between criteria. Saaty used a pairwise comparison method in which each factor of the criteria is rated against every other factor to establish ranked values. While this approach had been tried before, the earlier mathematics seemed inappropriate and didn’t fit the problem. Furthermore, there was no way of evaluating the subjective consistency with which these alternatives were being compared.
As it happened, this consistency factor has emerged as one of the key issues for both decision researchers and decision makers alike. Users of the AHP have found that if they went through the pairwise comparison and found that it “didn’t feel right” there was inevitably a correspondingly weak consistency value.
Saaty’s method impressed a wide range of decision makers, including the US State Department, which used it to test alternative foreign policy scenarios for real and potential events in world affairs. In a much different application, co-authors have use it to resolve disagreements about how characters would react in film script plot situations.
Which & Why
After five years of development, we are proud to say that Which & Why Decision Valuation Software can legitimately be regarded as an effective culmination of the entire history of decision making so far.
From Bacon to Franklin, from Kepner-Tregoe to Saaty, Which & Why pulls from the entire pool of collected research and adds the genius of modern computing power to offer what might well be the simplest, quickest, and most precise methodology ever for decision making.
Brainstorming theory, for example, is used effectively in the first phase of the Which & Why process, model-building. Once the model is developed, the criteria factors must be given an importance ranking. In this phase, Which & Why uses the required matrix algebra to make the pairwise comparison methodology easy for anyone. In the third phase, the options under consideration are evaluated against the ranked criteria factors. Then finally, in the fourth phase of decision making, the results are analyzed by taking a leaf from the pages of Carl Jung. An innovative scoring method balances out the subjective and objective aspects of the evaluation to offer a combined Which & Why score.
But Which & Why is more than just a theoretical decision making tool. It has been designed to take into account the ever-present reality of cost as a crucial ingredient of the mix. When this price dynamic is added to the equation, Which & Why goes beyond decision making to value analysis and expenditure justification.
To provide this complete recommendation, Which & Why automatically computes the combined Which & Why score for each option against the fully established costs for that option. It then compares this value to analysis to announce the option that provides the best value under current circumstances. If those circumstances should change (for example, if a new option come to light, or a price is re-negotiated), just plug in the numbers and let Which & Why re-evaluate the recommendation instantaneously.
Finally, as an incentive to make full use of its capabilities, Which & Why has been designed to be as user-friendly and intuitive as possible, from its colorful graphics to its pull-down menus and mouse function.
We think the oracle at Delphi would have been proud.
Prepared by the research department of Arlington Software Corporation
Originally appeared as “http://www.arlingsoft.com/history.htm”, but it is no longer available on the Web.
- Archived page via the wayback machine: http://web.archive.org/web/20021113035240/http://www.arlingsoft.com/Home.asp?fromdomain=&fromurl=
- University of Bristol. “Cracking the semantic code: Half a word’s meaning is 3-D summary of associated rewards.” ScienceDaily, 13 Feb. 2013. Web. 16 Feb. 2013.