Everything Is Obvious (18 page)

So powerful is the appeal of a good story that even when we are trying to evaluate an explanation scientifically—that
is, on the basis of how well it accounts for the data—we can’t help judging it in terms of its narrative attributes. In a range of experiments, for example, psychologists have found that simpler explanations are judged more likely to be true than complex explanations, not because simpler explanations actually explain more, but rather
just because
they are simpler. In one study, for example, when faced with a choice of explanations for a fictitious set of medical symptoms, a majority of respondents chose an explanation involving only one disease over an alternative explanation involving two diseases, even when the combination of the two diseases was statistically twice as likely as the single-disease explanation.
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Somewhat paradoxically, explanations are also judged to be more likely to be true when they have informative details added, even when the extra details are irrelevant or actually make the explanation less likely. In one famous experiment, for example, students shown descriptions of two fictitious individuals, “Bill” and “Linda” consistently preferred more detailed backstories—that Bill was both an accountant and a jazz player rather than simply a jazz player, or that Linda was a feminist bank teller rather than just a bank teller—even though the less detailed descriptions were logically more likely.
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In addition to their content, moreover, explanations that are skillfully delivered are judged more plausible than poorly delivered ones, even when the explanations themselves are identical. And explanations that are intuitively plausible are judged more likely than those that are counterintuitive—even though, as we know from all those Agatha Christie novels, the most plausible explanation can be badly wrong. Finally, people are observed to be more confident about their judgments when they have an explanation at hand, even when they have no idea how likely the explanation is to be correct.
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It’s true, of course, that scientific explanations often start out as stories as well, and so have some of the same attributes.
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The key difference between science and storytelling, however, is that in science we perform experiments that explicitly test our “stories.” And when they don’t work, we modify them until they do. Even in branches of science like astronomy, where true experiments are impossible, we do something analogous—building theories based on past observations and testing them on future ones. Because history is only run once, however, our inability to do experiments effectively excludes precisely the kind of evidence that would be necessary to infer a genuine cause-and-effect relation. In the absence of experiments, therefore, our storytelling abilities are allowed to run unchecked, in the process burying most of the evidence that is left, either because it’s not interesting or doesn’t fit with the story we want to tell. Expecting history to obey the standards of scientific explanation is therefore not just unrealistic, but fundamentally confused—it is, as Berlin concluded, “to ask it to contradict its essence.”
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For much the same reason, professional historians are often at pains to emphasize the difficulty of generalizing from any one particular context to any other. Nevertheless, because accounts of the past, once constructed, bear such a strong resemblance to the sorts of theories that we construct in science, it is tempting to treat them as if they have the same power of generalization—even for the most careful historians.
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When we try to understand
why
a particular book became a bestseller, in other words, we are implicitly asking a question about how books in general become bestsellers, and therefore how that experience can be repeated by other authors or publishers. When we investigate the
causes
of the recent housing bubble or of the terrorist attacks of September 11, we are inevitably
also seeking insight that we hope we’ll be able to apply in the future—to improve our national security or the stability of our financial markets. And when we conclude from the surge in Iraq that it
caused
the subsequent drop in violence, we are invariably tempted to apply the same strategy again, as indeed the current administration has done in Afghanistan. No matter what we say we are doing, in other words, whenever we seek to learn
about
the past, we are invariably seeking to learn
from
it as well—an association that is implicit in the words of the philosopher George Santayana: “Those who cannot remember the past are condemned to repeat it.”
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This confusion between stories and theories gets to the heart of the problem with using common sense as a way of understanding the world. In one breath, we speak as if all we’re trying to do is to make sense of something that has already happened. But in the next breath we’re applying the “lesson” that we think we have learned to whatever plan or policy we’re intending to implement in the future. We make this switch between storytelling and theory building so easily and instinctively that most of the time we’re not even aware that we’re doing it. But the switch overlooks that the two are fundamentally different exercises with different objectives and standards of evidence. It should not be surprising then that explanations that were chosen on the basis of their qualities as stories do a poor job of predicting future patterns or trends. Yet that is nonetheless what we use them for. Understanding the limits of what we can explain about the past ought therefore to shed light on what it is that we can predict about the future. And because prediction is so central to planning, policy, strategy, management, marketing, and all the other problems that we will discuss later, it is to prediction that we now turn.

Humans love to make predictions—whether about the movements of the stars, the gyrations of the stock market, or the upcoming season’s hot color. Pick up the newspaper on any given day and you’ll immediately encounter a mass of predictions—so many, in fact, that you probably don’t even notice them. To illustrate the point, let’s consider a single news story chosen more or less at random from the front page of the
New York Times
. The story, which was published in the summer of 2009, was about trends in retail sales and contained no fewer than ten predictions about the upcoming back-to-school season. For example, according to one source cited in the article—an industry group called the National Retail Federation—the average family with school-age children was predicted to spend “nearly 8 percent less this year than last,” while according to the research firm ShopperTrak, customer traffic in stores was predicted to be down 10 percent. Finally, an expert who was identified as president of Customer Growth Partners, a retailing consultant firm, was quoted as claiming that the season was “going to be the worst back-to-school season in many, many years.”
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All three predictions were made by authoritative-sounding sources and were explicit enough to have been scored for accuracy. But how accurate were they? To be honest, I have no idea. The
New York Times
doesn’t publish statistics on the
accuracy of the predictions made in its pages, nor do most of the research companies that provide them. One of the strange things about predictions, in fact, is that our eagerness to make pronouncements about the future is matched only by our reluctance to be held accountable for the predictions we make. In the mid-1980s, the psychologist Philip Tetlock noticed exactly this pattern among political experts of the day. Determined to make them put their proverbial money where their mouths were, Tetlock designed a remarkable test that was to unfold over twenty years. To begin with, he convinced 284 political experts to make nearly a hundred predictions each about a variety of possible future events, ranging from the outcomes of specific elections to the likelihood that two nations would engage in armed conflict with each other. For each of these predictions, Tetlock insisted that the experts specify which of two outcomes they expected and also assign a probability to their prediction. He did so in a way that confident predictions scored more points when correct, but also lost more points when mistaken. With those predictions in hand, he then sat back and waited for the events themselves to play out. Twenty years later, he published his results, and what he found was striking: Although the experts performed slightly better than random guessing, they did not perform as well as even a minimally sophisticated statistical model. Even more surprisingly, the experts did slightly better when operating
outside
their area of expertise than within it.
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Tetlock’s results are often interpreted as demonstrating the fatuousness of so-called experts, and no doubt there’s some truth to that. But although experts are probably no better than the rest of us at making predictions, they are also probably no worse. When I was young, for example, many people believed that the future would be filled with flying cars, orbiting space cities, and endless free time. Instead, we drive
internal combustion cars on crumbling, congested freeways; endure endless cuts in airplane service, and work more hours than ever. Meanwhile, Web search, mobile phones, and online shopping—the technologies that have, in fact, affected our lives—came more or less out of nowhere. Around the same time that Tetlock was beginning his experiment, in fact, a management scientist named Steven Schnaars tried to quantify the accuracy of technology-trend predictions by combing through a large collection of books, magazines, and industry reports, and recording hundreds of predictions that had been made during the 1970s. He concluded that roughly 80 percent of all predictions were wrong, whether they were made by experts or not.
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Nor is it just forecasters of long-term social and technology trends that have lousy records. Publishers, producers, and marketers—experienced and motivated professionals in business with plenty of skin in the game—have just as much difficulty predicting which books, movies, and products will become the next big hit as political experts have in predicting the next revolution. In fact, the history of cultural markets is crowded with examples of future blockbusters—Elvis,
Star Wars, Seinfeld, Harry Potter, American Idol
—that publishers and movie studios left for dead while simultaneously betting big on total failures.
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And whether we consider the most spectacular business meltdowns of recent times—Long-Term Capital Management in 1998, Enron in 2001, WorldCom in 2002, the near-collapse of the entire financial system in 2008—or spectacular success stories like the rise of Google and Facebook, what is perhaps most striking about them is that virtually
nobody
seems to have had any idea what was about to happen. In September 2008, for example, even as Lehman Brothers’ collapse was imminent, Treasury and Federal Reserve officials—who arguably had the best information
available to anyone in the world—failed to anticipate the devastating freeze in global credit markets that followed. Conversely, in the late 1990s the founders of Google, Sergey Brin and Larry Page, tried to sell their company for $1.6M. Fortunately for them, nobody was interested, because Google went on to attain a market value of over $160 billion, or about 100,000 times what they and everybody else apparently thought it was worth only a few years earlier.
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Results like these seem to show that humans are simply bad at making predictions, but in fact that’s not quite right either. In reality there are all sorts of predictions that we could make very well if we chose to. I would bet, for example, that I could do a pretty good job of forecasting the weather in Santa Fe, New Mexico—in fact, I bet I would be correct more than 80 percent of the time. As impressive as that sounds compared to the lousy record of Tetlock’s experts, however, my ability to predict the weather in Santa Fe is not going to land me a job at the Weather Bureau. The problem is that in Santa Fe it is sunny roughly 300 days a year, so one can be right 300 days out of 365 simply by making the mindless prediction that “tomorrow it will be sunny.” Likewise, predictions that the United States will not go to war with Canada in the next decade or that the sun will continue to rise in the east are also likely to be accurate, but impress no one. The real problem of prediction, in other words, is not that we are universally good or bad at it, but rather that we are bad at distinguishing predictions that we can make reliably from those that we can’t.

In a way this problem goes all the way back to Newton. Starting from his three laws of motion, along with his universal
law of gravitation, Newton was able to derive not only Kepler’s laws of planetary motion but also the timing of the tides, the trajectories of projectiles, and a truly astonishing array of other natural phenomena. It was a singular scientific accomplishment, but it also set an expectation for what could be accomplished by mathematical laws that would prove difficult to match. The movements of the planets, the timing of the tides—these are amazing things to be able to predict. But aside from maybe the vibrations of electrons or the time required for light to travel a certain distance, they are also about the most predictable phenomena in all of nature. And yet, because predicting these movements was among the first problems that scientists and mathematicians set their sights on, and because they met with such stunning success, it was tempting to conclude that everything worked that way. As Newton himself wrote: