The implications for how to make better decisions, though, are less clear. First-generation decision analysts such as Howard Raiffa and Ward Edwards recognized the flaws described by Kahneman and Tversky as real but thought the focus on them was misplaced and led to a fatalistic view of man as a “cognitive cripple.” Even some heuristics-and-biases researchers agreed. “The bias story is so captivating that it overwhelmed the heuristics story,” says Baruch Fischhoff, a former research assistant of Kahneman and Tversky who has long taught at Carnegie Mellon University. “I often cringe when my work with Amos is credited with demonstrating that human choices are irrational,” Kahneman himself wrote in Thinking, Fast and Slow. “In fact our research only showed that humans are not well described by the rational-agent model.” And so a new set of decision scholars began to examine whether those shortcuts our brains take are actually all that irrational.
When Heuristics Work
That notion wasn’t entirely new. Herbert Simon, originally a political scientist but later a sort of social scientist of all trades (the economists gave him a Nobel in 1978), had begun using the term “heuristic” in a positive sense in the 1950s. Decision makers seldom had the time or mental processing power to follow the optimization process outlined by the decision analysts, he argued, so they “satisficed” by taking shortcuts and going with the first satisfactory course of action rather than continuing to search for the best.
Simon’s “bounded rationality,” as he called it, is often depicted as a precursor to the work of Kahneman and Tversky, but it was different in intent. Whereas they showed how people departed from the rational model for making decisions, Simon disputed that the “rational” model was actually best. In the 1980s others began to join in the argument.
The most argumentative among them was and still is Gerd Gigerenzer, a German psychology professor who also did doctoral studies in statistics. In the early 1980s he spent a life-changing year at the Center for Interdisciplinary Research in the German city of Bielefeld, studying the rise of probability theory in the 17th through 19th centuries with a group of philosophers and historians. One result was a well-regarded history, The Empire of Chance, by Gigerenzer and five others (Gigerenzer’s name was listed first because in keeping with the book’s theme, the authors drew lots). Another was a growing conviction in Gigerenzer’s mind that the Bayesian approach to probability favored by the decision analysts was, although not incorrect, just one of several options.
When Gigerenzer began reading Kahneman and Tversky, he says now, he did so “with a different eye than most readers.” He was, first, dubious of some of the results. By tweaking the framing of a question, it is sometimes possible to make apparent cognitive illusions go away. Gigerenzer and several coauthors found, for example, that doctors and patients are far more likely to assess disease risks correctly when statistics are presented as natural frequencies (10 out of every 1,000) rather than as percentages.
But Gigerenzer wasn’t content to leave it at that. During an academic year at Stanford’s Center for Advanced Study in the Behavioral Sciences, in 1989–1990, he gave talks at Stanford (which had become Tversky’s academic home) and UC Berkeley (where Kahneman then taught) fiercely criticizing the heuristics-and-biases research program. His complaint was that the work of Kahneman, Tversky, and their followers documented violations of a model, Bayesian decision analysis, that was itself flawed or at best incomplete. Kahneman encouraged the debate at first, Gigerenzer says, but eventually tired of his challenger’s combative approach. The discussion was later committed to print in a series of journal articles, and after reading through the whole exchange, it’s hard not to share Kahneman’s fatigue.
Gigerenzer is not alone, though, in arguing that we shouldn’t be too quick to dismiss the heuristics, gut feelings, snap judgments, and other methods humans use to make decisions as necessarily inferior to the probability-based verdicts of the decision analysts. Even Kahneman shares this belief to some extent. He sought out a more congenial discussion partner in the psychologist and decision consultant Gary Klein. One of the stars of Malcolm Gladwell’s book Blink, Klein studies how people—firefighters, soldiers, pilots—develop expertise, and he generally sees the process as being a lot more naturalistic and impressionistic than the models of the decision analysts. He and Kahneman have together studied (见[相关阅读])when going with the gut works and concluded that, in Klein’s words, “reliable intuitions need predictable situations with opportunities for learning.”
Are those really the only situations in which heuristics trump decision analysis? Gigerenzer says no, and the experience of the past few years (the global financial crisis, mainly) seems to back him up. When there’s lots of uncertainty, he argues, “you have to simplify in order to be robust. You can’t optimize any more.” In other words, when the probabilities you feed into a decision-making model are unreliable, you might be better off following a rule of thumb. One of Gigerenzer’s favorite examples of this comes from Harry Markowitz, the creator of the decision analysis cousin known as modern portfolio theory, who once let slip that in choosing the funds for his retirement account, he had simply split the money evenly among the options on offer (his allocation for each was 1/N). Subsequent research has shown that this so-called 1/N heuristic isn’t a bad approach at all (见[相关阅读]).
The State of the Art
The Kahneman-Tversky heuristics-and-biases approach has the upper hand right now, both in academia and in the public mind. Aside from its many real virtues, it is the approach best suited to obtaining interesting new experimental results, which are extremely helpful to young professors trying to get tenure. Plus, journalists love writing about it.
Decision analysis hasn’t gone away, however. HBS dropped it as a required course in 1997, but that was in part because many students were already familiar with such core techniques as the decision tree. As a subject of advanced academic research, though, it is confined to a few universities—USC, Duke, Texas A&M, and Stanford, where Ron Howard teaches. It is concentrated in industries, such as oil and gas and pharmaceuticals, in which managers have to make big decisions with long investment horizons and somewhat reliable data. Chevron is almost certainly the most enthusiastic adherent, with 250 decision analysts on staff. Aspects of the field have also enjoyed an informal renaissance among computer scientists and others of a quantitative bent. The presidential election forecasts that made Nate Silver famous were a straightforward application of Bayesian methods.
Those who argue that rational, optimizing decision making shouldn’t be the ideal are a more scattered lot. Gigerenzer has a big group of researchers at the Max Planck Institute for Human Development, in Berlin. Klein and his allies, chiefly in industry and government rather than academia, gather regularly for Naturalistic Decision Making conferences. Academic decision scholars who aren’t decision analysts mostly belong to the interdisciplinary Society for Judgment and Decision Making, which is dominated by heuristics-and-biases researchers. “It’s still very much us and them, where us is Kahneman-and-Tversky disciples and the rest is Gerd and people who have worked with him,” says Dan Goldstein, a former Gigerenzer student now at Microsoft Research. “It’s still 90 to 10 Kahneman and Tversky.” Then again, Goldstein—a far more diplomatic sort than his mentor—is slated to be the next president of the society.
There seems to be more overlap in practical decision advice than in decision research. The leading business school textbook, Judgment in Managerial Decision Making, by Harvard’s Max Bazerman (and, in later editions, UC Berkeley’s Don Moore), devotes most of its pages to heuristics and biases but is dedicated to the decision analyst Howard Raiffa and concludes with a list of recommendations that begins, “1. Use decision analysis tools.” There’s nothing inconsistent there—the starting point of the whole Kahneman-and-Tversky research project was that decision analysis was the best approach. But other researchers in this tradition, when they try to correct the decision-making errors people make, also find themselves turning to heuristics.
One of the best-known products of heuristics-and-biases research, Richard Thaler and Shlomo Benartzi’s Save More Tomorrow program, replaces the difficult choices workers face when asked how much they want to put aside for retirement with a heuristic—a commitment to automatically bump up one’s contribution with every pay raise—that has led to dramatic increases in saving. A recent field experiment (见[相关阅读])with small-business owners in the Dominican Republic found that teaching them the simple heuristic of keeping separate purses for business and personal life, and moving money from one to the other only once a month, had a much greater impact than conventional financial education. “The big challenge is to know the realm of applications where these heuristics are useful, and where they are useless or even harm people,” says the MIT economist Antoinette Schoar, one of the researchers. “At least from what I’ve seen, we don’t know very well what the boundaries are of where heuristics work.”
This has recently been a major research project for Gigerenzer and his allies—he calls it the study of “ecological rationality.” In environments where uncertainty is high, the number of potential alternatives many, or the sample size small, the group argues, heuristics are likely to outperform more-analytic decision-making approaches. This taxonomy may not catch on—but the sense that smart decision making consists of a mix of rational models, error avoidance, and heuristics seems to be growing.
Other important developments are emerging. Advances in neuroscience could change the decision equation as scientists get a better sense of how the brain makes choices, although that research is in early days. Decisions are increasingly shunted from people to computers, which aren’t subject to the same information-processing limits or biases humans face. But the pioneers of artificial intelligence included both John von Neumann and Herbert Simon, and the field still mixes the former’s decision-analysis tools with the latter’s heuristics. It offers no definitive verdict—yet—on which approach is best.
Making Better Decisions
So, what is the right way to think about making decisions? There are a few easy answers. For big, expensive projects for which reasonably reliable data is available—deciding whether to build an oil refinery, or whether to go to an expensive graduate school, or whether to undergo a medical procedure—the techniques of decision analysis are invaluable. They are also useful in negotiations and group decisions. Those who have used decision analysis for years say they find themselves putting it to work even for fast judgments. The Harvard economist Richard Zeckhauser runs a quick decision tree in his head before deciding how much money to put in a parking meter in Harvard Square. “It sometimes annoys people,” he admits, “but you get good at doing this.”
A firefighter running into a burning building doesn’t have time for even a quick decision tree, yet if he is experienced enough his intuition will often lead him to excellent decisions. Many other fields are similarly conducive to intuition built through years of practice—a minimum of 10,000 hours of deliberate practice to develop true expertise, the psychologist K. Anders Ericsson famously estimated. The fields where this rule best applies tend to be stable. The behavior of tennis balls or violins or even fire won’t suddenly change and render experience invalid.
Management isn’t really one of those fields. It’s a mix of situations that repeat themselves, in which experience-based intuitions are invaluable, and new situations, in which such intuitions are worthless. It involves projects whose risks and potential returns lend themselves to calculations but also includes groundbreaking endeavors for which calculations are likely to mislead. It is perhaps the profession most in need of multiple decision strategies.
Part of the appeal of heuristics-and-biases research is that even if it doesn’t tell you what decision to make, it at least warns you away from ways of thought that are obviously wrong. If being aware of the endowment effect makes you less likely to defend a declining business line rather than invest in a new one, you’ll probably be better off.
Yet overconfidence in one’s judgment or odds of success—near the top of most lists of decision-making flaws—is a trait of many successful leaders. At the very cutting edge of business, it may be that good decision making looks a little like the dynamic between Star Trek’s Captain Kirk and Mr. Spock, with Spock reciting the preposterously long odds of success and Kirk confidently barging ahead, Spock still at his side.
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