Introduction
Much of what people do, with the exceptions of reflexive and habitual behavior, results from the cognitive processes of deciding. Even a minor decision, such as whether to drive a car, take the bus, or walk to work, involves the coordination of many complex processes. In making this choice, one might take into consideration one’s perception of the weather, guilt about contributing to air pollution, feeling of physical energy, goal of obtaining more exercise, memory of a recent bus trip, desire for company, or judged likelihood of working late. Even such a relatively minor decision can be difficult to make because there are numerous considerations, and some favor one alternative while remaining considerations favor other alternatives. In addition, the decision maker cannot know all relevant information, so there is uncertainty about the outcomes of important events.
A major goal of decision research is to understand the rules that people use in choosing from alternatives. This often means gaining insight into the decision processes that are used when no alternative is clearly preferred. To accomplish this, it is necessary to understand what is meant by a rule and to identify different potential rules for selecting one of a set of alternative courses of action. Some rules are heuristics, or strategies for simplifying choice that limit the evaluation of alternatives. Heuristics can be very efficient. In the transportation example above, if one used a heuristic, one might consider only the amount of time available for getting to work. Such a simplistic analysis of the problem can, however, lead to a poor decision. In other words, the employee might have more regrets after using this heuristic than would be the case if he or she had made a more careful analysis of the alternatives.
Decision Theories
Decision theory has a long history of identifying normative procedures for decision making. These procedures tell people what rules they should follow in making decisions. A standard rule is to take into account two dimensions for each decision alternative: likelihood and utility. This principle, which is embodied in subjective expected utility theory, is intended to maximize the personal value of one’s anticipated outcomes. A person may be given a choice between a 50 percent chance of winning $100 and a certain $2. The first alternative has an expected outcome of $50 (calculated by 50 percent of $100 = $50), since that is what one would expect to win on average if one played this game many times. The other alternative has an expected outcome of $2 (calculated by 100 percent of $2 = $2). Subjective expected utility theory indicates that one should choose the first alternative, the 50 percent chance of $100, because it has a higher expected outcome. This choice is called “rational” in the sense that it is the choice that is likely to maximize earnings.
The cognitive approach to decision making emphasizes an understanding of the ways in which various factors influence the choices that people make in reality, regardless of whether they follow normative principles. In contrast to the normative approach, the cognitive approach is focused on description of the actual processes that people use. A person may be given a 30 percent chance of $100 or a certain $20. Calculations based on likelihood and value dictate that one should choose the first alternative, since its anticipated outcome of $30 (30 percent of $100 = $30) is more than $20. Many people, however, simply do not want to take the risk of receiving nothing with the first alternative, preferring the security of knowing that they will receive $20 to the uncertainty of getting $100 or nothing. The possibility of an additional $10 is not worth the risk. This is not necessarily “irrational.” As this example shows, normative decision theory cannot predict what many or even most people will choose. For this reason, psychologists have become increasingly interested in examining the processes that people actually use to make decisions.
Illusions and Heuristics
Of particular interest in the cognitive approach to decision making are those factors that lead to miscalculations of likelihood or utility, since they will ultimately contribute to undesirable outcomes. Psychologists Amos Tversky and Daniel Kahneman revolutionized the field of decision making by identifying factors that contribute to poor decision making. Some of these may be called “cognitive illusions,” because they lead a decision maker to a judgment that is in fact a distortion of reality. One type of judgment that is often affected by such illusions concerns likelihood estimation, the chance of an event leading to a particular outcome.
Another type of judgment that is susceptible to illusionary distortion is the estimation of quantity or frequency. In making these estimations, people often use heuristics. In a heuristic for estimating quantity called anchoring and adjustment, one takes any available number as an initial starting point or anchor and then adjusts it to arrive at an estimate. For example, one might predict tomorrow’s temperature by taking today’s temperature and adjusting downward for forthcoming rainfall. Although heuristics can be more efficient than the careful and comprehensive analysis of relevant information, they can also be misleading.
Illusions and heuristics can be detrimental to good decision making because they lead the decision maker to a distorted view of the problem and available alternatives. It is often possible to develop procedures for improving the decision-making process. Elaborate technologies have been developed to assist people in making decisions in nearly every area. Sometimes it is instructive, however, merely to understand the processes that people use and to know their limitations. It must be kept in mind that evaluating the quality of decisions is difficult. One reason for this difficulty is that some decisions that are made with great care, thought, and objectivity can still have disappointing outcomes, while luck can operate to bring favorable outcomes despite poor decision processes. The ultimate key to improving human judgment and decision making is research that integrates normative and descriptive theories.
Expectancy, Utility, and Motivation
The principles of subjective expected utility theory have been applied in a wide variety of problem areas. A distinction between expectancy and utility can be quite useful. For example, two people who choose to continue to smoke may do so for different reasons. One may truly believe that he or she has a high chance of developing a serious disease such as lung cancer; however, that person may anticipate great medical advances and expect that lung cancer will be only a mild problem by the time he or she is diagnosed with it. Though the expectation of a negative outcome is high, the outcome is not particularly negative to this individual. Another person may be convinced that lung cancer will continue to be a painful, expensive, deadly disease within his or her lifetime. Despite the fact that this outcome has great negative utility for this person, he or she may continue to smoke due to an expectation that he or she will not develop lung cancer. Each of these individuals is influenced by different factors. Understanding how the decision to smoke or to quit is made can assist health advocates—and tobacco advertisers—in influencing these decisions.
One of the areas in which subjective expected utility principles have been highly influential is that of motivation. While early theories of motivation viewed behavior as the result of basic drives or personality traits, subsequent theories emphasized the ways in which people thought about their options. From this perspective, it is meaningless to label someone “unmotivated.” Everyone is motivated, in the sense that all people have time and effort to give to activities. People choose how much time and effort to give to each of the various options open to them: work, leisure, and family activities. Employees who do little or no work do not necessarily lack “drive” or have flawed personalities. They have simply decided to spend their time and effort on other things. This does not excuse or overlook the workers’ lack of productivity, but it does suggest methods to alter their lack of performance. The key is to understand how they judge the utilities of outcomes that result from working and their perceived likelihood of obtaining these outcomes by choosing to put time and effort into work activities. Thus, the study of decision making is important to organizational efforts to enhance productivity.
Biases in Decision Making
In one sense, it is easy to observe instances of illusions and heuristics that lead to biases in decision making in real life. Bad decisions seem to be everywhere. As noted, however, decisions that turn out badly may sometimes result from badly made decisions. People are accustomed to judging the actions of others and will label them irrational if it appears that they are choosing alternatives with inferior outcomes for themselves. During the Persian Gulf War of 1991, the American media frequently concluded that Saddam Hussein was “irrational” because he chose not to withdraw from Kuwait by the United Nations deadline. Though it is tempting to label an enemy irrational, it is wise to keep in mind a serious problem in determining irrationality in a decision maker: it is exceedingly difficult to assess the utility of any alternative for the decision maker. By American standards, it would have been better for Iraqi forces to withdraw from Kuwait before suffering enormous loss of life and eventually being forced to withdraw anyway, so it seemed that Hussein could not possibly be evaluating the alternatives realistically. Either he did not understand the potential magnitude of the human and economic losses that would result from a failure to withdraw or he did not understand the virtual certainty of losing the war. Hussein may, however, have understood both perfectly and simply have attached different utilities to the outcomes anticipated from withdrawal versus war. Perhaps from Hussein’s perspective, the loss of life could be offset easily by the opportunity to show himself to the Arab world as someone who stood up to the international community, if only briefly.
Scientific investigations of biases in decision making require that the investigator prove that a given alternative is superior to the one that is chosen by most people. This is often done by means of mathematics or statistics. In one demonstration of the representativeness bias, people are given a brief personality sketch of “Linda” and asked to determine how likely it is that Linda is a member of various categories. Most people tend to judge Linda as more likely to be a bank teller and a feminist than merely a bank teller. In fact, however, there must be at least as many bank tellers as there are feminist bank tellers, since the category “bank teller” will contain all feminist bank tellers as well as all nonfeminist bank tellers. The illusion comes from the erroneous conclusion that because Linda’s personality traits represent both the occupation of a bank teller and the political perspective of a feminist, she is more likely to be both than one or the other. Research such as this helps to determine how people can jump to conclusions and misjudge someone. Overestimating the likelihood that a person belongs to two categories diminishes one’s ability to estimate appropriately the expected utilities of alternatives for decisions about that person.
Decisions in the Workplace and Daily Life
Numerous forces have come together to fuel the study of human decision making as a cognitive process. One of these is the coming of the information age. With the transition from a production economy to a service economy, workers are no longer seen as people who engage in only physical work; rather, workers at all levels deal with information and decisions. It is no longer possible to attribute all the difficulty of making decisions to insufficient information, as decision makers are often overloaded and overwhelmed by information. The real problem they face is knowing which information to select and how to integrate it into the decision-making process.
Within psychology, two areas of study that have had a great impact on behavioral decision making are perception and quantitative psychology. Both Kahneman and Tversky did extensive work in the area of perception before becoming interested in studying the cognitive processes in human judgment and decision making. Many other behavioral decision researchers began their studies in quantitative psychology or statistics. The primary objective in this area is to learn how to make decisions under uncertainty using the laws of probability. Because this is what people routinely face in the course of their work and daily lives, there are many intriguing parallels between statistics and behavioral decision making.
Advances in understanding the rationality of human decision making were furthered, ironically, by
economic theories that assumed rationality on the part of human decision makers. Psychologists who conducted empirical studies of people had data to show that many choices that people make do not follow rational economic models. For example, standard economic theory predicts that people will choose the option that maximizes their own payoff. Yet people often prefer a plan that they deem fair to everyone over one that is financially superior for themselves. Behavioral decision theory attempts to understand the way in which people actually make decisions, not the way that formal models say that they should.
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