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Problem solving and decision-making are important skills for business and life. Problem-solving often involves decision-making, and decision-making is especially important for management and leadership. There are processes and techniques to improve decision-making and the quality of decisions. Decision-making is more natural to certain personalities, so these people should focus more on improving the quality of their decisions. People that are less natural decision-makers are often able to make quality assessments, but then need to be more decisive in acting upon the assessments made. Problem-solving and decision-making are closely linked, and each requires creativity in identifying and developing options, for which the brainstorming technique is particularly useful. See also the free SWOT analysis template and examples, and PEST analysis template, which help decision-making and problem-solving. SWOT analysis helps assess the strength of a company, a business proposition or idea; PEST analysis helps to assess the potential and suitability of a market. Good decision-making requires a mixture of skills: creative development and identification of options, clarity of judgement, firmness of decision, and effective implementation. For group problem-solving and decision-making, or when a consensus is required, workshops help, within which you can incorporate these tools and process as appropriate. Here are some useful methods for effective decision-making and problem-solving: First a simple step-by-step process for effective decision-making and problem-solving.
See also the decision-making facilitative questions template.
And definitely see the ethical decision-making quick guide.
Decision-making maxims will help to reinforce the above decision-making process whether related to problem-solving or not, for example:
"We know what happens to people who stay in the middle of the road. They get run down." (Aneurin Bevan)
"In any moment of decision the best thing you can do is the right thing, the next best thing is the wrong thing, and the worst thing you can do is nothing." (attributed to Theodore Roosevelt - more maxims on the quotes page)
JFDI - Just Frigging Do it (polite version). The decision-maker's motto. There are usually several right answers when you are faced with a complex decision. When you've found the best solution you can find, get on with it, make it work, and it most probably will. (More useful rules, acronyms and training ideas on the acronyms page)
Another simple process for decision-making is the pros and cons list.
Pro means 'for', and con means 'against'. In other words, advantages and disadvantages.
This method also applies to all sorts of problem-solving where issues and implications need to be understood and a decision has to be made.
Some decisions are a simple matter of whether to make a change or not, such as moving, taking a new job, or buying something, selling something, replacing something, etc. Other decisions involve number of options, and are concerned more with how to do something, involving a number of choices. Use the brainstorming process to identify and develop options for decision-making and problem-solving.
You will find that writing things down in this way will help you to see things more clearly, become more objective and detached, which will help you to make clearer decisions.
This example weighs the pros and cons of buying a new car to replace an old car.
The weighted pros and cons are purely examples - they are not in any way suggestions of how you should make such a decision. Our decision-making criteria depend on our own personal situations and preferences. And your criteria and weighting will change according to time, situation, and probably your mood too.
Use whatever scoring method you want to. The example shows low scores but you can score each item up to 10, or 20 or 100 - whatever makes sense to you personally. Or you can use an 'A/B/C' or three-star scoring method, whatever works for you.
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Should I replace my old car with a new one? |
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pros (for - advantages) |
score |
cons (against - disadvantages) |
score |
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better comfort |
3 |
cost outlay will mean making sacrifices |
5 |
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lower fuel costs |
3 |
higher insurance |
3 |
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lower servicing costs |
4 |
time and hassle to choose and buy it |
2 |
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better for family use |
3 |
disposal or sale of old car |
2 |
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better reliability |
5 |
big decisions like this scare and upset me |
4 |
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it'll be a load off my mind |
2 |
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total 6 pros |
20 |
total 5 cons |
16 |
In the above example, on the basis of the pros and cons and the weighting applied, there seems to be a a clear overall (and quantifiable) advantage in the decision to go ahead and buy a new car.
Notice that with this decision-making method it's even possible to include 'intangible' emotional issues in the pros and cons comparison, for example 'it'll be a load off my mind', and 'decisions scare and upset me'.
A decision-making pros and cons list like this helps remove the emotion which blocks clear thinking and decision-making. It enables objectivity and measurement, rather than reacting from instinct, or avoiding the issue altogether. Objective measurement helps in making a confident decision.
The total weighted scores are the main deciding factor rather than the total number of pros and cons, although there is not a scientific 'right' or 'wrong' way to consider the total number of pros and cons compared with the total weighted scores.
If the weighted scores are indicating a decision which makes you feel uncomfortable, then check your weightings, and also check that you've not missed out any factors on either side of the table.
If the decision makes you feel uncomfortable and this is not reflected in the table, then add it as a factor and give it a score.
Seeking feedback or input from a trusted neutral friend can be helpful in confirming your factors and their scores.
You should be able to cut and paste this template into a text editor or spreadsheet. Add more rows as required.
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question/decision/option: |
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pros (for - advantages) |
score |
cons (against - disadvantages) |
score |
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totals |
totals |
Note: The above methods are similar to - but not the same as - 'Force Field Analysis', an analytical theory developed by psychologist Kurt Lewin (1890-1947), originally to assess factors influencing group behaviour. The Lewin model is typically shown as a simplified diagram, with horizontal arrows alongside each factor pointing to the space between the columns. Explained above is an different and logically developed weighted decision-making method, not Lewin's Force Field Analysis.
For more complex decisions and problems involving more than two possible options you can use several pros and cons tables in conjunction, to compare the overall weight of each option.
In such cases the wording of the options is important, for example, if considering the best path for one's own career and work development the options might be:
A situation like this can be approached by completing three separate pros and cons tables and then comparing the net effects (difference between weighted pros and cons) of each one.
While this won't necessarily evaluate and compare all possible inter-related aspects of the whole situation, it will help to give great clarity and detached objectivity (detached as in unemotional), which can be very difficult to find when confronted with a complicated and big challenge offering several options.
Also consider that some decisions and challenges are difficult because you don't have the necessary knowledge or experience, in which case you need first to decide if the decision or challenge is actually appropriate and necessary for you at this stage.
Some decisions have to be made whether you are ready or not. Others might not be as pressing as you imagine.
Do not be forced into a change-based decision if having considered the implications carefully you decide that it's not the best thing to do.
The decision to do nothing different, in the right way for the right reasons, is often a perfectly good option.
Whatever you do - try to be as objective as you can be.
Well prepared decisions are easier to make and to implement, and generally produce the best results.
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Capitalism to Democracy
Associates: George B. Dantzig, Robin Hogarth, Charles R. Piott, Howard Raiffa, Thomas C. Schelling, Kennth A. Shepsle, Richard Thaier, Amos Tversky, and Sidney Winter.
Simon was educated in political science at the University of Chicago (B.A., 1936, Ph.D., 1943). He has held research and faculty positions at the University of California (Berkeley), Illinois Institute of Technology and since 1949, Carnegie Mellon University, where he is the Richard King Mellon University Professor of Computer Science and Psychology. In 1978, he received the Alfred Nobel Memorial Prize in Economic Sciences and in 1986 the National Medal of Science.
Reprinted with permission from Research Briefings 1986: Report of the Research Briefing Panel on Decision Making and Problem Solving © 1986 by the National Academy of Sciences. Published by National Academy Press, Washington, DC.
The work of managers, of scientists, of engineers, of lawyers--the work that steers the course of society and its economic and governmental organizations--is largely work of making decisions and solving problems. It is work of choosing issues that require attention, setting goals, finding or designing suitable courses of action, and evaluating and choosing among alternative actions. The first three of these activities--fixing agendas, setting goals, and designing actions--are usually called problem solving; the last, evaluating and choosing, is usually called decision making. Nothing is more important for the well-being of society than that this work be performed effectively, that we address successfully the many problems requiring attention at the national level (the budget and trade deficits, AIDS, national security, the mitigation of earthquake damage), at the level of business organizations (product improvement, efficiency of production, choice of investments), and at the level of our individual lives (choosing a career or a school, buying a house).
The abilities and skills that determine the quality of our decisions and problem solutions are stored not only in more than 200 million human heads, but also in tools and machines, and especially today in those machines we call computers. This fund of brains and its attendant machines form the basis of our American ingenuity, an ingenuity that has permitted U.S. society to reach remarkable levels of economic productivity.
There are no more promising or important targets for basic scientific research than understanding how human minds, with and without the help of computers, solve problems and make decisions effectively, and improving our problem-solving and decision-making capabilities. In psychology, economics, mathematical statistics, operations research, political science, artificial intelligence, and cognitive science, major research gains have been made during the past half century in understanding problem solving and decision making. The progress already achieved holds forth the promise of exciting new advances that will contribute substantially to our nation's capacity for dealing intelligently with the range of issues, large and small, that confront us.
Much of our existing knowledge about decision making and problem solving, derived from this research, has already been put to use in a wide variety of applications, including procedures used to assess drug safety, inventory control methods for industry, the new expert systems that embody artificial intelligence techniques, procedures for modeling energy and environmental systems, and analyses of the stabilizing or destabilizing effects of alternative defense strategies. (Application of the new inventory control techniques, for example, has enabled American corporations to reduce their inventories by hundreds of millions of dollars since World War II without increasing the incidence of stockouts.) Some of the knowledge gained through the research describes the ways in which people actually go about making decisions and solving problems; some of it prescribes better methods, offering advice for the improvement of the process.
Central to the body of prescriptive knowledge about decision making has been the theory of subjective expected utility (SEU), a sophisticated mathematical model of choice that lies at the foundation of most contemporary economics, theoretical statistics, and operations research. SEU theory defines the conditions of perfect utility-maximizing rationality in a world of certainty or in a world in which the probability distributions of all relevant variables can be provided by the decision makers. (In spirit, it might be compared with a theory of ideal gases or of frictionless bodies sliding down inclined planes in a vacuum.) SEU theory deals only with decision making; it has nothing to say about how to frame problems, set goals, or develop new alternatives.
Prescriptive theories of choice such as SEU are complemented by empirical research that shows how people actually make decisions (purchasing insurance, voting for political candidates, or investing in securities), and research on the processes people use to solve problems (designing switchgear or finding chemical reaction pathways). This research demonstrates that people solve problems by selective, heuristic search through large problem spaces and large data bases, using means-ends analysis as a principal technique for guiding the search. The expert systems that are now being produced by research on artificial intelligence and applied to such tasks as interpreting oil-well drilling logs or making medical diagnoses are outgrowths of these research findings on human problem solving.
What chiefly distinguishes the empirical research on decision making and problem solving from the prescriptive approaches derived from SEU theory is the attention that the former gives to the limits on human rationality. These limits are imposed by the complexity of the world in which we live, the incompleteness and inadequacy of human knowledge, the inconsistencies of individual preference and belief, the conflicts of value among people and groups of people, and the inadequacy of the computations we can carry out, even with the aid of the most powerful computers. The real world of human decisions is not a world of ideal gases, frictionless planes, or vacuums. To bring it within the scope of human thinking powers, we must simplify our problem formulations drastically, even leaving out much or most of what is potentially relevant.
The descriptive theory of problem solving and decision making is centrally concerned with how people cut problems down to size: how they apply approximate, heuristic techniques to handle complexity that cannot be handled exactly. Out of this descriptive theory is emerging an augmented and amended prescriptive theory, one that takes account of the gaps and elements of unrealism in SEU theory by encompassing problem solving as well as choice and demanding only the kinds of knowledge, consistency, and computational power that are attainable in the real world.
The growing realization that coping with complexity is central to human decision making strongly influences the directions of research in this domain. Operations research and artificial intelligence are forging powerful new computational tools; at the same time, a new body of mathematical theory is evolving around the topic of computational complexity. Economics, which has traditionally derived both its descriptive and prescriptive approaches from SEU theory, is now paying a great deal of attention to uncertainty and incomplete information; to so-called "agency theory," which takes account of the institutional framework within which decisions are made; and to game theory, which seeks to deal with interindividual and intergroup processes in which there is partial conflict of interest. Economists and political scientists are also increasingly buttressing the empirical foundations of their field by studying individual choice behavior directly and by studying behavior in experimentally constructed markets and simulated political structures.
The following pages contain a fuller outline of current knowledge about decision making and problem solving and a brief review of current research directions in these fields as well as some of the principal research opportunities.
The development of SEU theory was a major intellectual achievement of the first half of this century. It gave for the first time a formally axiomatized statement of what it would mean for an agent to behave in a consistent, rational matter. It assumed that a decision maker possessed a utility function (an ordering by preference among all the possible outcomes of choice), that all the alternatives among which choice could be made were known, and that the consequences of choosing each alternative could be ascertained (or, in the version of the theory that treats of choice under uncertainty, it assumed that a subjective or objective probability distribution of consequences was associated with each alternative). By admitting subjectively assigned probabilities, SEU theory opened the way to fusing subjective opinions with objective data, an approach that can also be used in man-machine decision-making systems. In the probabilistic version of the theory, Bayes's rule prescribes how people should take account of new information and how they should respond to incomplete information.
The assumptions of SEU theory are very strong, permitting correspondingly strong inferences to be made from them. Although the assumptions cannot be satisfied even remotely for most complex situations in the real world, they may be satisfied approximately in some microcosms--problem situations that can be isolated from the world's complexity and dealt with independently. For example, the manager of a commercial cattle-feeding operation might isolate the problem of finding the least expensive mix of feeds available in the market that would meet all the nutritional requirements of his cattle. The computational tool of linear programming, which is a powerful method for maximizing goal achievement or minimizing costs while satisfying all kinds of side conditions (in this case, the nutritional requirements), can provide the manager with an optimal feed mix--optimal within the limits of approximation of his model to real world conditions. Linear programming and related operations research techniques are now used widely to make decisions whenever a situation that reasonably fits their assumptions can be carved out of its complex surround. These techniques have been especially valuable aids to middle management in dealing with relatively well-structured decision problems.
Most of the tools of modern operations research--not only linear programming, but also integer programming, queuing theory, decision trees, and other widely used techniques--use the assumptions of SEU theory. They assume that what is desired is to maximize the achievement of some goal, under specified constraints and assuming that all alternatives and consequences (or their probability distributions) are known. These tools have proven their usefulness in a wide variety of applications.
Operations research tools have also underscored dramatically the limits of SEU theory in dealing with complexity. For example, present and prospective computers are not even powerful enough to provide exact solutions for the problems of optimal scheduling and routing of jobs through a typical factory that manufactures a variety of products using many different tools and machines. And the mere thought of using these computational techniques to determine an optimal national policy for energy production or an optimal economic policy reveals their limits.
Computational complexity is not the only factor that limits the literal application of SEU theory. The theory also makes enormous demands on information. For the utility function, the range of available alternatives and the consequences following from each alternative must all be known. Increasingly, research is being directed at decision making that takes realistic account of the compromises and approximations that must be made in order to fit real-world problems to the informational and computational limits of people and computers, as well as to the inconsistencies in their values and perceptions. The study of actual decision processes (for example, the strategies used by corporations to make their investments) reveals massive and unavoidable departures from the framework of SEU theory. The sections that follow describe some of the things that have been learned about choice under various conditions of incomplete information, limited computing power, inconsistency, and institutional constraints on alternatives. Game theory, agency theory, choice under uncertainty, and the theory of markets are a few of the directions of this research, with the aims both of constructing prescriptive theories of broader application and of providing more realistic descriptions and explanations of actual decision making within U.S. economic and political institutions.
Although the limits of human rationality were stressed by some researchers in the 1950s, only recently has there been extensive activity in the field of economics aimed at developing theories that assume less than fully rational choice on the part of business firm managers and other economic agents. The newer theoretical research undertakes to answer such questions as the following:
Theories of the firm that assume managers are aiming at "satisfactory" profits or that their concern is to maintain the firm's share of market in the industry make quite different predictions about economic equilibrium than those derived from the assumption of profit maximization. Moreover, the classical theory of the firm cannot explain why economic activity is sometimes organized around large business firms and sometimes around contractual networks of individuals or smaller organizations. New theories that take account of differential access of economic agents to information, combined with differences in self-interest, are able to account for these important phenomena, as well as provide explanations for the many forms of contracts that are used in business. Incompleteness and asymmetry of information have been shown to be essential for explaining how individuals and business firms decide when to face uncertainty by insuring, when by hedging, and when by assuming the risk.
Most current work in this domain still assumes that economic agents seek to maximize utility, but within limits posed by the incompleteness and uncertainty of the information available to them. An important potential area of research is to discover how choices will be changed if there are other departures from the axioms of rational choice--for example, substituting goals of reaching specified aspiration levels (satisficing) for goals of maximizing.
Applying the new assumptions about choice to economics leads to new empirically supported theories about decision making over time. The classical theory of perfect rationality leaves no room for regrets, second thoughts, or "weakness of will." It cannot explain why many individuals enroll in Christmas savings plans, which earn interest well below the market rate. More generally, it does not lead to correct conclusions about the important social issues of saving and conservation. The effect of pensions and social security on personal saving has been a controversial issue in economics. The standard economic model predicts that an increase in required pension saving will reduce other saving dollar for dollar; behavioral theories, on the other hand, predict a much smaller offset. The empirical evidence indicates that the offset is indeed very small. Another empirical finding is that the method of payment of wages and salaries affects the saving rate. For example, annual bonuses produce a higher saving rate than the same amount of income paid in monthly salaries. This finding implies that saving rates can be influenced by the way compensation is framed.
If individuals fail to discount properly for the passage of time, their decisions will not be optimal. For example, air conditioners vary greatly in their energy efficiency; the more efficient models cost more initially but save money over the long run through lower energy consumption. It has been found that consumers, on average, choose air conditioners that imply a discount rate of 25 percent or more per year, much higher than the rates of interest that prevailed at the time of the study.
As recently as five years ago, the evidence was thought to be unassailable that markets like the New York Stock Exchange work efficiently--that prices reflect all available information at any given moment in time, so that stock price movements resemble a random walk and contain no systematic information that could be exploited for profit. Recently, however, substantial departures from the behavior predicted by the efficient-market hypothesis have been detected. For example, small firms appear to earn inexplicably high returns on the market prices of their stock, while firms that have very low price-earnings ratios and firms that have lost much of their market value in the recent past also earn abnormally high returns. All of these results are consistent with the empirical finding that decision makers often overreact to new information, in violation of Bayes's rule. In the same way, it has been found that stock prices are excessively volatile--that they fluctuate up and down more rapidly and violently than they would if the market were efficient.
There has also been a long-standing puzzle as to why firms pay dividends. Considering that dividends are taxed at a higher rate than capital gains, taxpaying investors should prefer, under the assumptions of perfect rationality, that their firms reinvest earnings or repurchase shares instead of paying dividends. (The investors could simply sell some of their appreciated shares to obtain the income they require.) The solution to this puzzle also requires models of investors that take account of limits on rationality.
In economic, political, and other social situations in which there is actual or potential conflict of interest, especially if it is combined with incomplete information, SEU theory faces special difficulties. In markets in which there are many competitors (e.g., the wheat market), each buyer or seller can accept the market price as a "given" that will not be affected materially by the actions of any single individual. Under these conditions, SEU theory makes unambiguous predictions of behavior. However, when a market has only a few suppliers --say, for example, two--matters are quite different. In this case, what it is rational to do depends on what one's competitor is going to do, and vice versa. Each supplier may try to outwit the other. What then is the rational decision?
The most ambitious attempt to answer questions of this kind was the theory of games, developed by von Neumann and Morgenstern and published in its full form in 1944. But the answers provided by the theory of games are sometimes very puzzling and ambiguous. In many situations, no single course of action dominates all the others; instead, a whole set of possible solutions are all equally consistent with the postulates of rationality.
One game that has been studied extensively, both theoretically and empirically, is the Prisoner's Dilemma. In this game between two players, each has a choice between two actions, one trustful of the other player, the other mistrustful or exploitative. If both players choose the trustful alternative, both receive small rewards. If both choose the exploitative alternative, both are punished. If one chooses the trustful alternative and the other the exploitative alternative, the former is punished much more severely than in the previous case, while the latter receives a substantial reward. If the other player's choice is fixed but unknown, it is advantageous for a player to choose the exploitative alternative, for this will give him the best outcome in either case. But if both adopt this reasoning, they will both be punished, whereas they could both receive rewards if they agreed upon the trustful choice (and did not welch on the agreement).
The terms of the game have an unsettling resemblance to certain situations in the relations between nations or between a company and the employees' union. The resemblance becomes stronger if one imagines the game as being played repeatedly. Analyses of "rational" behavior under assumptions of intended utility maximization support the conclusion that the players will (ought to?) always make the mistrustful choice. Nevertheless, in laboratory experiments with the game, it is often found that players (even those who are expert in game theory) adopt a "tit-for-tat" strategy. That is, each plays the trustful, cooperative strategy as long as his or her partner does the same. If the partner exploits the player on a particular trial, the player then plays the exploitative strategy on the next trial and continues to do so until the partner switches back to the trustful strategy. Under these conditions, the game frequently stabilizes with the players pursuing the mutually trustful strategy and receiving the rewards.
With these empirical findings in hand, theorists have recently sought and found some of the conditions for attaining this kind of benign stability. It occurs, for example, if the players set aspirations for a satisfactory reward rather than seeking the maximum reward. This result is consistent with the finding that in many situations, as in the Prisoner's Dilemma game, people appear to satisfice rather than attempting to optimize.
The Prisoner's Dilemma game illustrates an important point that is beginning to be appreciated by those who do research on decision making. There are so many ways in which actual human behavior can depart from the SEU assumptions that theorists seeking to account for behavior are confronted with an embarrassment of riches. To choose among the many alternative models that could account for the anomalies of choice, extensive empirical research is called for--to see how people do make their choices, what beliefs guide them, what information they have available, and what part of that information they take into account and what part they ignore. In a world of limited rationality, economics and the other decision sciences must closely examine the actual limits on rationality in order to make accurate predictions and to provide sound advice on public policy.
During the past ten years, empirical studies of human choices in which uncertainty, inconsistency, and incomplete information are present have produced a rich collection of findings which only now are beginning to be organized under broad generalizations. Here are a few examples. When people are given information about the probabilities of certain events (e.g., how many lawyers and how many engineers are in a population that is being sampled), and then are given some additional information as to which of the events has occurred (which person has been sampled from the population), they tend to ignore the prior probabilities in favor of incomplete or even quite irrelevant information about the individual event. Thus, if they are told that 70 percent of the population are lawyers, and if they are then given a noncommittal description of a person (one that could equally well fit a lawyer or an engineer), half the time they will predict that the person is a lawyer and half the time that he is an engineer--even though the laws of probability dictate that the best forecast is always to predict that the person is a lawyer.
People commonly misjudge probabilities in many other ways. Asked to estimate the probability that 60 percent or more of the babies born in a hospital during a given week are male, they ignore information about the total number of births, although it is evident that the probability of a departure of this magnitude from the expected value of 50 percent is smaller if the total number of births is larger (the standard error of a percentage varies inversely with the square root of the population size).
There are situations in which people assess the frequency of a class by the ease with which instances can be brought to mind. In one experiment, subjects heard a list of names of persons of both sexes and were later asked to judge whether there were more names of men or women on the list. In lists presented to some subjects, the men were more famous than the women; in other lists, the women were more famous than the men. For all lists, subjects judged that the sex that had the more famous personalities was the more numerous.
The way in which an uncertain possibility is presented may have a substantial effect on how people respond to it. When asked whether they would choose surgery in a hypothetical medical emergency, many more people said that they would when the chance of survival was given as 80 percent than when the chance of death was given as 20 percent.
On the basis of these studies, some of the general heuristics, or rules of thumb, that people use in making judgments have been compiled---heuristics that produce biases toward classifying situations according to their representativeness, or toward judging frequencies according to the availability of examples in memory, or toward interpretations warped by the way in which a problem has been framed.
These findings have important implications for public policy. A recent example is the lobbying effort of the credit card industry to have differentials between cash and credit prices labeled "cash discounts" rather than "credit surcharges." The research findings raise questions about how to phrase cigarette warning labels or frame truth-in-lending laws and informed consent laws.
Finding the underlying bases of human choice behavior is difficult. People cannot always, or perhaps even usually, provide veridical accounts of how they make up their minds, especially when there is uncertainty. In many cases, they can predict how they will behave (pre-election polls of voting intentions have been reasonably accurate when carefully taken), but the reasons people give for their choices can often be shown to be rationalizations and not closely related to their real motives.
Students of choice behavior have steadily improved their research methods. They question respondents about specific situations, rather than asking for generalizations. They are sensitive to the dependence of answers on the exact forms of the questions. They are aware that behavior in an experimental situation may be different from behavior in real life, and they attempt to provide experimental settings and motivations that are as realistic as possible. Using thinking-aloud protocols and other approaches, they try to track the choice behavior step by step, instead of relying just on information about outcomes or querying respondents retrospectively about their choice processes.
Perhaps the most common method of empirical research in this field is still to ask people to respond to a series of questions. But data obtained by this method are being supplemented by data obtained from carefully designed laboratory experiments and from observations of actual choice behavior (for example, the behavior of customers in supermarkets). In an experimental study of choice, subjects may trade in an actual market with real (if modest) monetary rewards and penalties. Research experience has also demonstrated the feasibility of making direct observations, over substantial periods of time, of the decision-making processes in business and governmental organizations--for example, observations of the procedures that corporations use in making new investments in plant and equipment. Confidence in the empirical findings that have been accumulating over the past several decades is enhanced by the general consistency that is observed among the data obtained from quite different settings using different research methods.
There still remains the enormous and challenging task of putting together these findings into an empirically founded theory of decision making. With the growing availability of data, the theory-building enterprise is receiving much better guidance from the facts than it did in the past. As a result, we can expect it to become correspondingly more effective in arriving at realistic models of behavior.
The theory of choice has its roots mainly in economics, statistics, and operations research and only recently has received much attention from psychologists; the theory of problem solving has a very different history. Problem solving was initially studied principally by psychologists, and more recently by researchers in artificial intelligence. It has received rather scant attention from economists.
Human problem solving is usually studied in laboratory settings, using problems that can be solved in relatively short periods of time (seldom more than an hour), and often seeking a maximum density of data about the solution process by asking subjects to think aloud while they work. The thinking-aloud technique, at first viewed with suspicion by behaviorists as subjective and "introspective," has received such careful methodological attention in recent years that it can now be used dependably to obtain data about subjects' behaviors in a wide range of settings.
The laboratory study of problem solving has been supplemented by field studies of professionals solving real-world problems--for example, physicians making diagnoses and chess grandmasters analyzing game positions, and, as noted earlier, even business corporations making investment decisions. Currently, historical records, including laboratory notebooks of scientists, are also being used to study problem-solving processes in scientific discovery. Although such records are far less "dense" than laboratory protocols, they sometimes permit the course of discovery to be traced in considerable detail. Laboratory notebooks of scientists as distinguished as Charles Darwin, Michael Faraday, Antoine-Laurent Lavoisier, and Hans Krebs have been used successfully in such research.
From empirical studies, a description can now be given of the problem-solving process that holds for a rather wide range of activities. First, problem solving generally proceeds by selective search through large sets of possibilities, using rules of thumb (heuristics) to guide the search. Because the possibilities in realistic problem situations are generally multitudinous, trial-and-error search would simply not work; the search must be highly selective. Chess grandmasters seldom examine more than a hundred of the vast number of possible scenarios that confront them, and similar small numbers of searches are observed in other kinds of problem-solving search.
One of the procedures often used to guide search is "hill climbing," using some measure of approach to the goal to determine where it is most profitable to look next. Another, and more powerful, common procedure is means-ends analysis. In means-ends analysis, the problem solver compares the present situation with the goal, detects a difference between them, and then searches memory for actions that are likely to reduce the difference. Thus, if the difference is a fifty-mile distance from the goal, the problem solver will retrieve from memory knowledge about autos, carts, bicycles, and other means of transport; walking and flying will probably be discarded as inappropriate for that distance.
The third thing that has been learned about problem solving--especially when the solver is an expert--is that it relies on large amounts of information that are stored in memory and that are retrievable whenever the solver recognizes cues signaling its relevance. Thus, the expert knowledge of a diagnostician is evoked by the symptoms presented by the patient; this knowledge leads to the recollection of what additional information is needed to discriminate among alternative diseases and, finally, to the diagnosis.
In a few cases, it has been possible to estimate how many patterns an expert must be able to recognize in order to gain access to the relevant knowledge stored in memory. A chess master must be able to recognize about 50,000 different configurations of chess pieces that occur frequently in the course of chess games. A medical diagnostician must be able to recognize tens of thousands of configurations of symptoms; a botanist or zoologist specializing in taxonomy, tens or hundreds of thousands of features of specimens that define their species. For comparison, college graduates typically have vocabularies in their native languages of 50,000 to 200,000 words. (However, these numbers are very small in comparison with the real-world situations the expert faces: there are perhaps 10120 branches in the game tree of chess, a game played with only six kinds of pieces on an 8 x 8 board.)
One of the accomplishments of the contemporary theory of problem solving has been to provide an explanation for the phenomena of intuition and judgment frequently seen in experts' behavior. The store of expert knowledge, "indexed" by the recognition cues that make it accessible and combined with some basic inferential capabilities (perhaps in the form of means-ends analysis), accounts for the ability of experts to find satisfactory solutions for difficult problems, and sometimes to find them almost instantaneously. The expert's "intuition" and "judgment" derive from this capability for rapid recognition linked to a large store of knowledge. When immediate intuition fails to yield a problem solution or when a prospective solution needs to be evaluated, the expert falls back on the slower processes of analysis and inference.
Over the past thirty years, there has been close teamwork between research in psychology and research in computer science aimed at developing intelligent programs. Artificial intelligence (AI) research has both borrowed from and contributed to research on human problem solving. Today, artificial intelligence is beginning to produce systems, applied to a variety of tasks, that can solve difficult problems at the level of professionally trained humans. These AI programs are usually called expert systems. A description of a typical expert system would resemble closely the description given above of typical human problem solving; the differences between the two would be differences in degree, not in kind. An AI expert system, relying on the speed of computers and their ability to retain large bodies of transient information in memory, will generally use "brute force"--sheer computational speed and power--more freely than a human expert can. A human expert, in compensation, will generally have a richer set of heuristics to guide search and a larger vocabulary of recognizable patterns. To the observer, the computer's process will appear the more systematic and even compulsive, the human's the more intuitive. But these are quantitative, not qualitative, differences.
The number of tasks for which expert systems have been built is increasing rapidly. One is medical diagnosis (two examples are the CADUCEUS and MYCIN programs). Others are automatic design of electric motors, generators, and transformers (which predates by a decade the invention of the term expert systems), the configuration of computer systems from customer specifications, and the automatic generation of reaction paths for the synthesis of organic molecules. All of these (and others) are either being used currently in professional or industrial practice or at least have reached a level at which they can produce a professionally acceptable product.
Expert systems are generally constructed in close consultation with the people who are experts in the task domain. Using standard techniques of observation and interrogation, the heuristics that the human expert uses, implicitly and often unconsciously, to perform the task are gradually educed, made explicit, and incorporated in program structures. Although a great deal has been learned about how to do this, improving techniques for designing expert systems is an important current direction of research. It is especially important because expert systems, once built, cannot remain static but must be modifiable to incorporate new knowledge as it becomes available.
In the 1950s and 1960s, research on problem solving focused on clearly structured puzzle-like problems that were easily brought into the psychological laboratory and that were within the range of computer programming sophistication at that time. Computer programs were written to discover proofs for theorems in Euclidean geometry or to solve the puzzle of transporting missionaries and cannibals across a river. Choosing chess moves was perhaps the most complex task that received attention in the early years of cognitive science and AI.
As understanding grew of the methods needed to handle these relatively simple tasks, research aspirations rose. The next main target, in the 1960s and 1970s, was to find methods for solving problems that involved large bodies of semantic information. Medical diagnosis and interpreting mass spectrogram data are examples of the kinds of tasks that were investigated during this period and for which a good level of understanding was achieved. They are tasks that, for all of the knowledge they call upon, are still well structured, with clear-cut goals and constraints.
The current research target is to gain an understanding of problem-solving tasks when the goals themselves are complex and sometimes ill defined, and when the very nature of the problem is successively transformed in the course of exploration. To the extent that a problem has these characteristics, it is usually called ill structured. Because ambiguous goals and shifting problem formulations are typical characteristics of problems of design, the work of architects offers a good example of what is involved in solving ill-structured problems. An architect begins with some very general specifications of what is wanted by a client. The initial goals are modified and substantially elaborated as the architect proceeds with the task. Initial design ideas, recorded in drawings and diagrams, themselves suggest new criteria, new possibilities, and new requirements. Throughout the whole process of design, the emerging conception provides continual feedback that reminds the architect of additional considerations that need to be taken into account.
With the current state of the art, it is just beginning to be possible to construct programs that simulate this kind of flexible problem-solving process. What is called for is an expert system whose expertise includes substantial knowledge about design criteria as well as knowledge about the means for satisfying those criteria. Both kinds of knowledge are evoked in the course of the design activity by the usual recognition processes, and the evocation of design criteria and constraints continually modifies and remolds the problem that the design system is addressing. The large data bases that can now be constructed to aid in the management of architectural and construction projects provide a framework into which AI tools, fashioned along these lines, can be incorporated.
Most corporate strategy problems and governmental policy problems are at least as ill structured as problems of architectural or engineering design. The tools now being forged for aiding architectural design will provide a basis for building tools that can aid in formulating, assessing, and monitoring public energy or environmental policies, or in guiding corporate product and investment strategies.
The very first steps in the problem-solving process are the least understood. What brings (and should bring) problems to the head of the agenda? And when a problem is identified, how can it be represented in a way that facilitates its solution?
The task of setting an agenda is of utmost importance because both individual human beings and human institutions have limited capacities for dealing with many tasks simultaneously. While some problems are receiving full attention, others are neglected. Where new problems come thick and fast, "fire fighting" replaces planning and deliberation. The facts of limited attention span, both for individuals and for institutions like the Congress, are well known. However, relatively little has been accomplished toward analyzing or designing effective agenda-setting systems. A beginning could be made by the study of "alerting" organizations like the Office of Technology Assessment or military and foreign affairs intelligence agencies. Because the research and development function in industry is also in considerable part a task of monitoring current and prospective technological advances, it could also be studied profitably from this standpoint.
The way in which problems are represented has much to do with the quality of the solutions that are found. The task of designing highways or dams takes on an entirely new aspect if human responses to a changed environment are taken into account. (New transportation routes cause people to move their homes, and people show a considerable propensity to move into zones that are subject to flooding when partial protections are erected.) Very different social welfare policies are usually proposed in response to the problem of providing incentives for economic independence than are proposed in response to the problem of taking care of the needy. Early management information systems were designed on the assumption that information was the scarce resource; today, because designers recognize that the scarce resource is managerial attention, a new framework produces quite different designs.
The representation or "framing" of problems is even less well understood than agenda setting. Today's expert systems make use of problem representations that already exist. But major advances in human knowledge frequently derive from new ways of thinking about problems. A large part of the history of physics in nineteenth-century England can be written in terms of the shift from action-at-a-distance representations to the field representations that were developed by the applied mathematicians at Cambridge.
Today, developments in computer-aided design (CAD) present new opportunities to provide human designers with computer-generated representations of their problems. Effective use of these capabilities requires us to understand better how people extract information from diagrams and other displays and how displays can enhance human performance in design tasks. Research on representations is fundamental to the progress of CAD.
Nothing has been said so far about the radical changes that have been brought about in problem solving over most of the domains of science and engineering by the standard uses of computers as computational devices. Although a few examples come to mind in which artificial intelligence has contributed to these developments, they have mainly been brought about by research in the individual sciences themselves, combined with work in numerical analysis.
Whatever their origins, the massive computational applications of computers are changing the conduct of science in numerous ways. There are new specialties emerging such as "computational physics" and "computational chemistry." Computation--that is to say, problem solving--becomes an object of explicit concern to scientists, side by side with the substance of the science itself. Out of this new awareness of the computational component of scientific inquiry is arising an increasing interaction among computational specialists in the various sciences and scientists concerned with cognition and AI. This interaction extends well beyond the traditional area of numerical analysis, or even the newer subject of computational complexity, into the heart of the theory of problem solving.
Physicists seeking to handle the great mass of bubble-chamber data produced by their instruments began, as early as the 1960s, to look to AI for pattern recognition methods as a basis for automating the analysis of their data. The construction of expert systems to interpret mass spectrogram data and of other systems to design synthesis paths for chemical reactions are other examples of problem solving in science, as are programs to aid in matching sequences of nucleic acids in DNA and RNA and amino acid sequences in proteins.
Theories of human problem solving and learning are also beginning to attract new attention within the scientific community as a basis for improving science teaching. Each advance in the understanding of problem solving and learning processes provides new insights about the ways in which a learner must store and index new knowledge and procedures if they are to be useful for solving problems. Research on these topics is also generating new ideas about how effective learning takes place--for example, how students can learn by examining and analyzing worked-out examples.
Opportunities for advancing our understanding of decision making and problem solving are not limited to the topics dealt with above, and in this section, just a few indications of additional promising directions for research are presented.
The time dimension is especially troublesome in decision making. Economics has long used the notion of time discounting and interest rates to compare present with future consequences of decisions, but as noted above, research on actual decision making shows that people frequently are inconsistent in their choices between present and future. Although time discounting is a powerful idea, it requires fixing appropriate discount rates for individual, and especially social, decisions. Additional problems arise because human tastes and priorities change over time. Classical SEU theory assumes a fixed, consistent utility function, which does not easily accommodate changes in taste. At the other extreme, theories postulating a limited attention span do not have ready ways of ensuring consistency of choice over time.
In applying our knowledge of decision making and problem solving to society-wide, or even organization-wide, phenomena, the problem of aggregation must be solved; that is, ways must be found to extrapolate from theories of individual decision processes to the net effects on the whole economy, polity, and society. Because of the wide variety of ways in which any given decision task can be approached, it is unrealistic to postulate a "representative firm" or an "economic man," and to simply lump together the behaviors of large numbers of supposedly identical individuals. Solving the aggregation problem becomes more important as more of the empirical research effort is directed toward studying behavior at a detailed, microscopic level.
Related to aggregation is the question of how decision making and problem solving change when attention turns from the behavior of isolated individuals to the behavior of these same individuals operating as members of organizations or other groups. When people assume organizational positions, they adapt their goals and values to their responsibilities. Moreover, their decisions are influenced substantially by the patterns of information flow and other communications among the various organization units.
Organizations sometimes display sophisticated capabilities far beyond the understanding of single individuals. They sometimes make enormous blunders or find themselves incapable of acting. Organizational performance is highly sensitive to the quality of the routines or "performance programs" that govern behavior and to the adaptability of these routines in the face of a changing environment. In particular, the "peripheral vision" of a complex organization is limited, so that responses to novelty in the environment may be made in inappropriate and quasi-automatic ways that cause major failure.
Theory development, formal modeling, laboratory experiments, and analysis of historical cases are all going forward in this important area of inquiry. Although the decision-making processes of organizations have been studied in the field on a limited scale, a great many more such intensive studies will be needed before the full range of techniques used b
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