Distinguished Scholar Invited Essay Behavioral Decision Making

Abstract

The past few decades have witnessed greatly enhanced interest in behavioral decision theory. Unlike traditional decision theory, which is normative or prescriptive and seeks an optimal solution, behavioral decision theory (although it yields important practical implications) is inherently descriptive, seeking to understand how people actually make decisions. This article discusses rationality and its limits, approaches to examination of decision processes, and consequences of limits on rationality. Issues relating to the clinical-actuarial controversy and automatic decision making are then addressed. Two approaches to improving decision making–by use of statistical groups and prediction markets as well as by implementation of forms of paternalistic intervention–are examined. Implications for leadership and organizations are then offered.

Keywords

behavior decision making leadership

The past few decades have witnessed greatly enhanced interest in behavioral decision theory. Unlike traditional decision theory, which is normative or prescriptive and seeks to find an optimal solution, behavioral decision theory (although it yields important practical implications) is inherently descriptive, seeking to understand how people actually make decisions. I’ve taught a course on behavioral decision making (originally developed by George Huber) at the University of Wisconsin-Madison for about 30 years. Over that time, I’ve been pleased and, frankly, surprised to see the growth of interest in, and acceptance of, the relevance and power of behavioral decision making.

Long considered to be a fringe discipline, and perhaps simply a pesky nuisance to those advocating “economic decision making,” behavioral decision theory has emerged as an important and promising domain of research and practice. Two behavioral decision theorists—Herbert Simon and Daniel Kahneman—neither of them economists, won the Nobel Prize in Economics (in 1978 and 2002, respectively) for their work. Furthermore, Cass Sunstein, a leading writer on behavioral decision theory and an advocate of using “paternalistic intervention” to influence decision making, was appointed by President Obama to serve as Administrator of the White House Office of Information and Regulatory Affairs. In that role, his views have drawn applause and condemnation. Popular books such as Nudge (coauthored by Sunstein), Predictably Irrational: The Hidden Forces That Shape Our Decisions, and Thinking, Fast and Slow (authored by Kahneman) have introduced these issues to a broader audience. Behavioral decision theory has offered novel insights into disparate issues such as terrorism futures, road rage, whether to punt, bullet selection, divorce, and organ donation, as well as many management, finance, accounting, and marketing topics.

Rationality and Its Limits

In a seminal article, Herbert Simon (1955) presented the first major challenge to the concept of rational economic man. He did this not as an intended criticism of traditional economic perspectives but to complement them with a richer, more reality-based view.

The traditional “rational economic man” model of decision making views humans as capable of optimizing. Assumptions underlying that perspective include that the decision maker

has full knowledge of relevant aspects of the environment, including alternatives, events (states of nature), the probabilities of those events, and the outcomes associated with combinations of alternatives and events;

possesses a well-organized and stable set of preferences;

enjoys superb computational abilities capable of optimization;

capable of “cool” decision making, is not swayed by emotions or stress; and

has immediate access to costless information.

Simon viewed these assumptions as unrealistic in view of the many cognitive, perceptual, situational, and other constraints facing human decision makers. He presented his view of a “new rationality,” one that replaced “rational economic man” with “administrative man.” In this “new rationality”:

The classical view of rationality is replaced with “bounded rationality” in which the decision maker tries to find satisfactory solutions within many cognitive, perceptual, situational, and other bounds.

Aspiration levels are important and dynamic. Success and failure may result in changing levels of aspiration and thus changes in what is deemed acceptable or unacceptable.

Information acquisition and processing are time consuming, effortful, and costly. As such, the question of the best level of persistence in pursuit of a goal involves a trade-off between the potential costs and benefits of search.

Preferences are fluid. For example, preferences may change with time and maturation. In addition, consequences may change one’s payoff function. And, of course, we may simply not know our preferences because of lack of experience (and corresponding reluctance to explore alternatives).

Simon reasoned that, in view of the bounds on rationality and associated difficulties, the concept of human decision makers as optimizers is unrealistic. In its stead, Simon proposed that human decision makers satisfice rather than optimize. Whereas optimizers seek to determine the best possible alternative in the feasible set, satisficers seek the first acceptable alternative in that set.

Although satisficing may seem undesirable (because, for instance, a better alternative may be available than the first acceptable alternative and because it makes the decision maker a slave to the order in which alternatives are available), it recognizes information search and acquisition costs. Simon has equated satisficing with finding a needle in a haystack and optimizing with finding the sharpest needle in a haystack, a monumentally more difficult task. Sometimes, Simon reasoned, just a needle is needed.

Other scholars examined the nature and degree of constraints on rationality. For instance, George Miller (1956) wrote “The Magical Number Seven, Plus or Minus Two,” showing that for unidimensional stimuli, such as tones, taste intensities, visual position, loudness, and points on scales, humans are capable of a limited and narrow range of absolute judgments of about seven, plus or minus two. Thus, human capacity for making unidimensional judgments is limited and varies surprisingly little from one sense (e.g., hearing, sight, smell, and taste) to another.

In an early examination of the ability of human decision makers to serve as “intuitive statisticians,” Paul Slovic (1972) reviewed evidence relating to the validity of clinical judgment in finance, psychology, and medicine. Slovic sought not to denigrate human decision making but to better understand it. His view was that, if such an examination exposes “warts, prejudices, and twitches,” it is done in the belief that an understanding of human limitations would benefit the decision maker more than would naïve faith in the infallibility of his or her intellect. Slovic found that reliability and validity of expert judgment are often woefully poor and argued that they should never be taken for granted. He also came to the “quite disappointing” conclusion that, in general, clinician training and experience have little impact on validity but increase confidence in the decision and reduce willingness to accept external inputs—a potentially dangerous combination.

Daniel Schacter (1999) discussed memory limitations, writing of the “Seven Sins of Memory.” His premise was that, though often reliable, memory is also fallible. Schacter identified three “sins of omitting,” or types of forgetting. These include transcience, absentmindedness, and blocking. A second set, “sins of commission,” constitutes forms of distortion. These are misattribution, suggestibility, and bias. A final “sin” is the inability to erase intrusive recollections. Schacter argued that these “sins,” although troublesome, are by-products of otherwise adaptive features of memory.

Examining Decision Processes

Understanding the nature and consequences of behavioral decision making requires careful examination of human decision processes and outcomes. A variety of approaches are used for that purpose (Priem, Walters, & Li, 2011). In general, these are termed policy capturing (or input–output) approaches and process tracing approaches. With policy capturing, individuals are presented with a series of profiles giving the scores of alternatives on major dimensions. For example, a series of bicycles might be presented that vary on price, quality, wet braking as a percentage of dry braking, and weight. Individuals are instructed to give each profile an overall rating. Regression analysis, analysis of variance, or related procedures are then used to optimally relate inputs to outputs in order to “capture” the decision maker’s policies. As an alternative, decision process tracing is used to examine the decision process itself.

Recent management applications of policy capturing are for performance ratings (Spence & Keeping, 2010), strategic decision making (Pablo, 2007), gender differences in the pricing of professional services (Cron, Gilly, Graham, & Slocum, 2009), and workplace disputes (Klaas, Mahony, & Wheeler, 2006). Policy capturing permits examination of, for instance, decision makers’ combinatory models and cue usage (Einhorn, 1971). Many theories assume use of a particular combinatory model. For example, the Job Diagnostic Survey (Hackman & Oldham, 1975) assumes a conjunctive model, a noncompensatory model in which a poor score on any core task dimension leads to a low overall score. However, policy-capturing results question that assumption (Brief, Wallace, & Aldag, 1976). Conversely, the Job Descriptive Index (Smith, Kendall, & Hulin, 1969) assumes a linear additive model for job satisfaction, though that assumption is untested. This is important since different combinatory models offer markedly contrasting implications for practice. A linear additive model is compensatory; good levels on some dimensions can compensate for poor levels on others. With a conjunctive, noncompensatory model, on the other hand, a poor level of one dimension dominates, rendering improvement attempts based on manipulation of other dimensions futile.

Empirically derived weights can also be compared to decision makers’ self-assessments of those weights to gauge self-insight (and thus the appropriateness of use of self-reports of cue weights). Policy capturing research shows that decision makers typically think they are using more cues than is the case. They also overestimate the weights they apply to minor cues and underestimate the weights they apply to major cues. Furthermore, while decision makers may agree on how cues should be weighted, they may nevertheless differ in their choices because of failure to use cues in the ways they intended (Valenzi & Andrews, 1973).

Policy capturing is essentially a “black box” approach. It offers a model of the relationship of inputs to outputs but that model may not reflect the decision maker’s actual decision processes. Decision process tracing (Patrick & James, 2004; Schweiger, Anderson, & Locke, 1985) focuses on the decision process itself. For instance, eye fixation analysis (Kuo, Hsu, & Day, 2009; Russo & Rosen, 1975) and information boards (Carroll & Johnson, 1990) permit inference of decision processes from subjects’ eye movements or overt information acquisition as they consider information regarding alternatives. With another process tracing approach, verbal protocol analysis (Payne, Braunstein, & Carroll, 1978; Robie, Brown, & Beaty, 2007), subjects are asked to “think aloud” as they make decisions. There are benefits and limitations of each approach, suggesting that they may be used as complements (Einhorn, Kleinmuntz, & Kleinmuntz, 1979; Lafond et al., 2009).

Decision process tracing approaches show, for instance, that decision makers often switch their decision processes on the way to choice. They may, for instance, first use a model that quickly screens out alternatives failing to survive a series of hurdles and then adopt a model that attempts to optimize from among the survivors.

Some Consequences of Limits on Rationality

The various constraints on rationality have been shown to result in use of a variety of heuristics and biases.

Heuristics

Heuristics are simplifying rules of thumb. One heuristic, satisficing, was noted earlier. Tversky and Kahneman (1974) proposed others. Important heuristics include the following.

Availability

Availability is the tendency to estimate the probability of an event on the basis of how easy it is to recall examples of the event. To illustrate: Is it more likely that a word in the English language starts with the letter r or that it has r as the third letter? Most people incorrectly guess that r is more common as the first letter. The reason for this is that we store information by the first rather than the third letter (witness the phone directory) and can thus retrieve it more easily on that basis. As another example, when people are asked whether strokes or all accidents cause more deaths in the United States each year, most people say “all accidents.” In fact, strokes cause almost twice as many deaths as all accidents. However, accidents are publicized in news reports whereas strokes are not. As a result of availability, we tend to overestimate the probabilities of events that are readily available in our memories.

Representativeness

Representativeness is the tendency to place something in a class if it seems to represent the class. Representativeness is essentially the flip side of stereotyping. Whereas stereotyping views those within a class as sharing common characteristics, representativeness views those who share common characteristics as falling within a class. So, if someone looks like an astronaut to us, we might classify that person as an astronaut, although there are very few astronauts. As such, representativeness may cause us to ignore prior probabilities. Representativeness can have serious consequences. For instance, if we believe that men “look like” executives and women “look like” administrative assistants, we may behave accordingly, resulting in hiring or promotion biases against women.

Anchoring and adjustment

Anchoring and adjustment is the tendency to use an early bit of information as an anchor and then use new information to adjust that initial anchor. We tend to give too little weight to new information, resulting in insufficient adjustment. Remarkably, this heuristic is evidenced even when decision makers are aware that the anchor was randomly assigned, such as by spinning a roulette wheel or by considering the last four digits of their Social Security numbers.

Default heuristic

We often simply accept the default presented to us, whether it is the default setting when installing computer software or the default presented on a form. As I’ll discuss later, the default heuristic can be a powerful and unobtrusive determinant of decisions.

Biases

Constraints on human decision making may also lead to a variety of systematic biases. These include (see, for instance, Bazerman, 1998; Hammond, Keeney, & Raiffa, 1998) the following.

Conservatism in information processing

When we get new information, we tend to underrevise our past estimates. For instance, if we initially believe the probability of an event is .5 and receive new information that should increase (according to Bayes theorem) the probability to .8, we are more likely to revise our estimate to only .6 or .7. Conservatism leads to inadequate response to changing situations.

Framing effects

The way information is framed can influence choices. For example, suppose a gas station charges five cents a gallon more for credit card purchases than for cash payments. That difference will be viewed differently if it is presented as a “cash discount” than if it is called a “credit card surcharge.” Furthermore, Kahneman and Tversky (1979) proposed prospect theory to explain some consequences of framing. Prospect theory posits (among other things) that we evaluate alternatives in terms of changes from the status quo rather than of absolute values. So, for example, we may behave differently if we think we have gained 20 pounds and will have difficulty losing them than if we “start anew” and think in terms of gaining or losing pounds from our current weight.

Hindsight bias

Hindsight bias (or “Monday morning quarterbacking”) is the “I knew it all along” phenomenon. This is the tendency for people who learn of the outcome of an event to believe falsely that they would have predicted the reported outcome. One explanation for hindsight bias is “creeping determinism,” a fast and unconscious process in which outcome information is immediately and automatically integrated into a person’s knowledge of events preceding the outcome (Hawkins & Hastie, 1990). Hindsight bias results in distorted views of the accuracy of past decisions. For example, with his team, the New England Patriots, leading by six points and just over 2 minutes left in a 2009 game, Bill Belichik chose to try for a first down on fourth down on his own side of the field. The offense failed to get the first down, and the Indianapolis Colts promptly drove for a touchdown. Although Belichik’s decision was widely criticized as one of the worst in football history, it was actually correct from a statistical perspective (Everson & Albergotti, 2009). Nevertheless, when the decision’s outcome was poor, critics saw that outcome as virtually inevitable and the decision itself as egregious.

Confirmation bias

Confirmation bias (Nickerson, 1998) is our tendency to seek, interpret, and recall information in ways that confirm our preconceptions. Especially for emotionally significant issues and established beliefs, we prefer information sources that favor our preconceptions and avoid disconfirming sources. Furthermore, we interpret ambiguous situations to favor our views and are more likely to remember confirming than disconfirming information.

Overconfidence bias

Hindsight bias, confirmation bias, and other factors result in overconfidence. Overconfidence occurs when people’s subjective confidence in their judgments is greater than their objective accuracy (Einhorn, 1980; Moore & Healy, 2008). For instance, in some quizzes people who report they are 99% sure their answer is correct are wrong 40% of the time.

Illusory correlation

Illusory correlation (Tversky & Kahneman, 1974) is the tendency to “see” relationships between variables that do not in fact exist, perhaps because of our stereotypes or expectations. For instance, if we believe that Friday the 13th is unlucky, we are likely to be especially aware of bad events that occur on that date, without considering the good events that occur on Friday the 13th or the bad events that occur on other dates.

Escalation of commitment

Escalation of commitment, or the sunk cost fallacy, is the tendency to “throw good money after bad.” That is, while the decision to continue to invest in a course of action should be made on the basis of future benefits and costs, we tend to justify further, escalating investment on the basis of sunk costs, such as the money or lives that have already been expended. This is perhaps to justify our initial investment or to avoid admitting error (Staw, 1981).

Probability neglect

Traditional economic theory views decision makers as weighing both the magnitudes and probabilities of alternate outcomes. However, when individuals are faced with the possibility of especially noxious outcomes, such as receiving a severe shock or being shot by a sniper, they often focus just on the magnitude of outcomes, ignoring probabilities (Sunstein, 2003).

Much of the early research on heuristics and biases involved one-time decisions. Such decisions—whether to have chemotherapy or surgery, whether or not to mount an attack on an enemy, or whether to marry one’s childhood sweetheart, are common and often critical. Nevertheless, many real-world decisions are in continuous environments, characterized by regular, often redundant, feedback from the environment and the opportunity to make incremental adjustments. In such environments, some heuristics and biases may be less troublesome (Hogarth, 1981). For instance, conservatism in information processing (and anchoring and adjustment) may not be a serious problem in continuous environments since there are many opportunities to revise.

Although the overwhelming emphasis on heuristics and biases has been on their dangers, some heuristics, if used consciously and appropriately, may be functional. For example, Gert Gigerenzer (2008) has championed use of “fast and frugal heuristics” in an “adaptive toolbox.” He notes, for example, that Harry Markowitz, who won the 1990 Nobel Prize in Economics for his work on optimal asset allocation, did not use his award-winning optimization technique for his own retirement investments. He relied instead on a simple heuristic, the 1/N rule, which states, “Allocate your money equally to each of N funds.” Research showed the 1/N heuristic to outperform 12 optimal asset strategies. The optimization models performed better at fitting past data than did the simple 1/N heuristic, but they did worse at predicting the future.

Clinical and Actuarial Approaches, Improper Linear Models, and Clinical Synthesis

In view of the many heuristics and biases influencing human decision makers, a controversy—termed the clinical–actuarial controversy—relates to how accuracy of human (clinical) decision makers compares to that of actuarial (statistical) models. Research strongly supports the view that actuarial models consistently outperform unaided clinical judgment (Dawes, Faust, & Meehl, 1989). Dawes (1979) wrote that, “A search of the literature fails to reveal any studies in which clinical judgment has been shown to be superior to statistical prediction when both are based on the same codable input variables” (p. 573). That is still the case.

Nevertheless, it is sometimes infeasible to develop a model that optimally related predictors to outcomes (such a model is termed proper). There may, for instance, be too few observations to permit such development, or there may be no measurable criterion. In such cases, use of an improper linear model—one with which the weights of the predictor variables are obtained by some nonoptimal method—such as set to be equal, or determined at random—may be useful. For instance, marital happiness was significantly predicted by subtracting instances of arguments from instances of love making, though neither variable was itself a significant predictor (Dawes, 1979). Unit weighting was also used to select an optimal bullet for use by the Denver Police Department (Hammond & Adelman, 1976).

One intriguing form of improper linear model is a model of man (Goldberg, 1970) with which policy capturing is used to develop a linear model of an individual’s decision process. That model is then used to make decisions in place of the individual (this is called bootstrapping). A model of man has the important benefit of perfect reliability, enhancing validity. Remarkably, every properly executed study comparing the validity of decisions of individuals with those of their models of man has found the models to be superior (in an apparent exception, a study by Libby, 1976, data were analyzed incorrectly).

Despite the overwhelming evidence in support of use of proper and improper linear models, their adoption has been fiercely resisted, even by statistically trained decision makers (McCauley, 1991; Neuringer, 1993). Dawes (1979) has identified, and attempted to refute, primary causes for such resistance. For example, some critics argue that use of a statistical model rather than, for instance, an interview to choose from among job candidates or a doctor’s judgment to diagnose a patient is unfair and dehumanizing. Dawes responds that clinical judgments are seriously flawed and may be self-fulfilling (such as the case of a server who labels some customers as probable poor tippers, subsequently gives them poor service and is “confirmed” when they do not tip well.) Dawes notes that some of the worst doctors spend a great deal of time talking with their patients, read no medical journals, order few or no tests, and grieve at the funerals. As another example, since the accuracy of statistical models can be assessed and is often low (though higher than that of clinical judges), critics may object to their “proven low validities.”

In the face of such opposition to replacement of clinical decision making with use of actuarial models, an alternative—sometimes called clinical synthesis—has been proposed in which output of actuarial models is provided to individuals as input to their decisions.

The evidence on clinical synthesis is clear: individuals’ decisions are improved when they receive outputs from actuarial models, but not as good as if they simply used that output without modification (Arkes, Dawes, & Christensen, 1986; Peterson & Pitz, 1986). In addition, individuals with greater experience and perceived expertise are more reluctant to use such outputs than are novices. This sometimes results in cases in which, when experts and novices are presented with the outputs of models, novices are more willing to use the outputs and subsequently outperform the experts.

Automatic Information Processing

Research also shows that much decision making, rather than being a conscious deliberative process, occurs automatically at nonconscious levels (e.g., Kahneman, 2011). This is seen in literature on the nature of scripts and schemata (Erasmus, Boshoff, & Rousseau, 2002; Gioia & Poole, 1984), intuition (Behling & Eckel, 1991; Dane & Pratt, 2007), and implicit theories (Sternberg, 1985; Uleman, Saribay, & Gonzalez, 2008). For example, when we have repeatedly faced a decision situation we may develop scripts. Scripts are models held in memory that specify behaviors or event sequences that are appropriate for specific situations, such as steps in a performance appraisal. Scripts may be effective when dealing with routine situations but may cause problems in novel situations.

Behavioral decision making research has addressed the benefits and costs of automatic processing, the consequences of conflict between automatic and conscious processing (Denes-Raj & Epstein, 1994), and the manner and consequences of switching from one mode (automatic or conscious) to another (Louis & Sutton, 1991). The overall implication of automatic information processing literature is that individuals often behave without conscious consideration of elements of the rational economic model.

Statistical Groups and Prediction Markets

Many suggestions for overcoming human decision making limitations have been presented (e.g., Arkes, 1991; Swets, Dawes, & Monahan, 2000; Weick, 1984).) One promising approach is use of statistical groups and prediction markets (Milkman, Clugh, & Bazerman, 2009; Sunstein, 2005, 2006). While there is substantial evidence that statistical models of decision makers (i.e., models of man) outperform the decision makers, those models perform at a level equivalent to that achieved by averaging judges’ inputs. This is because averaging across many judges sharply reduces unreliability, one primary benefit of models of man. This suggests that use of statistical groups (i.e., averaging group members’ judgments) may be useful. In a classic example, Francis Galton examined a competition in which contestants attempted to judge the weight of a fat ox at a regional fair in England. The ox weighed 1198 pounds; the average guess, from the 787 contestants, was 1197 pounds (Sunstein, 2005). More recently, in 2004, members of the Society for American Baseball Research were asked to predict the winners of the baseball playoffs. In each round of the playoffs, the favored choice of the expert group was correct 100% of the time (Zajc, 2004).

A logical extension of statistical groups is use of prediction markets (Arrow et al., 2008; Tziralis & Tatsiopoulos, 2007). Prediction markets pool individual judgments to forecast the probabilities of events. In such markets, individuals bid on contracts that pay a certain amount if an event occurs. For instance, a contract may pay $1 if sales of a particular company are above a certain level. If the market price for the contract is $0.60, the market “believes” that sales have a 60% chance of exceeding that level. Such markets are being used internally at Google, Hewlett-Packard, IBM, and elsewhere (Leigh & Wolfers, 2007). Prediction markets, such as the Iowa Electronic Markets and InTrade, have been remarkably successful in predicting such outcomes as winners of elections and Oscars. The most controversial of these markets (rejected following vitriolic response) was proposed to predict terrorist activities (Hanson, 2006). Critics, including Hillary Clinton, labeled the proposal as “incredibly stupid” and “a futures market in death.” Nevertheless, following the attempt of Umar Farouk Abdulmutallab, the “underwear bomber,” to detonate plastic explosives on a Northwest Airlines flight, security experts close to the situation said a prediction market, with its ability to integrate diverse information, could have prevented him from boarding the flight.

Paternalistic Intervention

A recent, important controversy relates to the efficacy and desirability of using knowledge about human cognitive limitations and tendencies to “nudge” people toward “desirable” behaviors, a process termed paternalistic intervention (or, to recognize its attempts to alter behavior through changes in the situation, choice architecture). Two recent books—Nudge (Thaler & Sunstein, 2009) and Predictably Irrational (Ariely, 2009)—and numerous articles (e.g., Ratner et al., 2008) have advocated the use of such “nudges.” The controversy revolves primarily around the ethics of use of unobtrusive “nudges” and the question of who determines what actions are “desirable.”

Advocates say nudges are most needed when decisions are difficult and rare, when there is no prompt feedback, in the case of “investment goods” (such as exercise and dieting) for which costs occur now but aren’t seen until later, and “sinful goods” (such as smoking and eating fatty foods) for which pleasure is immediate but negative consequences are not suffered until later.

When the late Apple CEO Steve Jobs first appeared in public after receiving a liver transplant from a victim of a car crash, he said, “I wouldn’t be here without such generosity” and added that he hoped many other people would become organ donors. Most states, and many other countries, use an “opt-in” or “explicit consent” form in which people must take a concrete action, such as mailing in a form, to declare they want to be donors. In several European countries, an “opt-out” rule, also called “presumed consent,” is used, in which citizens are presumed to be consenting donors unless they register their unwillingness. Traditional economics would argue that if it is easy to register as a donor or nondonor the options should lead to similar results. However, in Germany, with an opt-in system, just 12% give their consent, whereas in Austria, with an opt-out system, 99% do (Abadie & Gay, 2006). This is one example of the power of a “nudge,” in this case manipulation of the default heuristic.

As noted earlier, this position has met with both acclaim and approbation. Advocates of paternalistic intervention argue that failure to use paternalistic intervention to improve personal, organizational, or societal outcomes is irresponsible. Opponents (some of them, such as conservative TV and radio commentator Glenn Beck, extremely vociferous) view such intervention as intrusive and unethical.

Implications for Leadership and Organizations

This necessarily cursory review of the behavioral decision making literature offers a variety of prescriptions and proscriptions for leadership and organizations.

Implications for Leadership and Motivating

Be cautious about accepting self-reported cue weights and combinatory models. For many reasons, individuals may be unaware of how they are using information, or may be unwilling to accurately report such use.

Challenge assumed combinatory models. Simply positing that variables combine in linear compensatory, conjunctive, or other ways without empirical support may lead to misguided prescriptions.

Recognize that much behavior is script based. Interventions focused solely on conscious cognitive processing may ignore the fact that people may be “thinking fast,” relying on automatic information processing.

Frame communications to be consistent with your goals. Framing can influence risk taking and risk aversion, as well as satisfaction with the decision.

When attempting to exhibit transformational leader behaviors, recognize the importance of availability and use vivid, memorable, case examples in communications.

Recognize that models based on subjective expected utility maximization, such as expectancy theory and path–goal theory, may require complements. Although clearly valuable, such models cannot capture the many deviations from the rational economic man model.

Recognize the power of heuristics. Be aware of the potential problems associated with specific heuristics. Try to understand your own heuristic usage. Seek objective data when possible to test whether, for instance, your probability estimates are accurate.

When setting goals, especially in joint goal setting, or when negotiating, recognize the power of the anchor.

Consider nudging toward a desired behavior through use of the default heuristic or other forms of paternalistic intervention.

Implications for Organizing and Strategizing

Consider modeling of key decision makers. Such modeling may provide important information about cue usage, may be useful input to others trying to understand or emulate those decision makers, and may reveal sources of disagreement among decision makers.

Be careful when suggesting or responding to initial anchors. Recognize that such anchors, even if randomly generated, may play powerful roles.

Consider alternative frames when developing and presenting strategies and challenge proposed frames.

Recognize the dangers of overconfidence bias and employ dissonance-inducing mechanisms (e.g., devil’s advocate, dialectical inquiry, and second-chance meetings) to counter overconfidence.

Consider use of proper and improper models, at least as inputs to strategy formulation and evaluation. Although such models may sometimes seem simplistic and crude, they offer valuable information.

When feasible, use synthetic judges (statistical groups). That is, if it is possible to combine judgments of knowledgeable individuals, do so.

Consider use of internal or external prediction markets. Capture the knowledge of the masses.

Conclusion

The primary lesson of behavioral decision making is that real-world decision making differs in important, systematic, and often predictable ways from the classical view of rational economic man. I was trained as a mechanical engineer and worked in the late 1960s and early 1970s as a heat transfer engineer on a variety of early aerospace projects such as the Lunar Roving Vehicle (LRV) and the passive seismometer (designed to detect moonquakes). Perhaps as a result, my bias has been to favor “rational,” subjective-expected-utility maximizing approaches for understanding, predicting, and influencing attitudes and behaviors in organizations. In fact, those approaches have been extremely valuable. Nevertheless, they have been limiting. Recognition of lessons of behavioral decision making provides a richer, if less comforting, view of human decision making, as well as unique lessons for leadership and organizations.

Footnotes

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The author received no financial support for the research, authorship, and/or publication of this article.

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