Regardless of the person and the context, in other words—sex, politics, religion, families, crime, cheating, trading, and even editing Wikipedia entries—the point that Levitt and Dubner keep returning to is that if we want to understand why people do what they do, we must understand the incentives that they face, and hence their preference for one outcome versus another. When someone does something that seems strange or puzzling to us, rather than writing them off as crazy or irrational, we should instead seek to analyze their situation in hopes of finding a rational incentive. It is precisely this sort of exercise, in fact, that we went through in the last chapter with the ultimatum game experiments. Once we discover that the Au and Gnau tradition of gift exchange effectively transforms what to us looks like free money into something that to them resembles an unwelcome future obligation, what was previously puzzling behavior suddenly seems as rational as our own. It is just rational according to a different set of premises than we were familiar with before. The central claim of Freakonomics is that we can almost always perform this exercise, no matter how weird or wonderful is the behavior in question.
As intriguing and occasionally controversial as Levitt and Dubner’s explanations are, in principle they are no different from the vast majority of social scientific explanations. However much sociologists and economists might argue about the details, that is, until they have succeeded in accounting for a given behavior in terms of some combination of motivations, incentives, perceptions, and opportunities—until they have, in a word, rationalized the behavior—they do not feel that they have really understood it.7 And it is not only social scientists who feel this way. When we try to understand why an ordinary Iraqi citizen would wake up one morning and decide to turn himself into a living bomb, we are implicitly rationalizing his behavior. When we attempt to explain the origins of the recent financial crisis, we are effectively searching for rational financial incentives that led bankers to create and market high-risk assets. And when we blame soaring medical costs on malpractice legislation or procedure-based payments, we are instinctively invoking a model of rational action to understand why doctors do what they do. When we think about how we think, in other words, we reflexively adopt a framework of rational behavior.8
THINKING IS ABOUT MORE THAN THOUGHT
The implicit assumption that people are rational until proven otherwise is a hopeful, even enlightened, one that in general ought to be encouraged. Nevertheless, the exercise of rationalizing behavior glosses over an important difference between what we mean when we talk about “understanding” human behavior, as opposed to the behavior of electrons, proteins, or planets. When trying to understand the behavior of electrons, for example, the physicist does not start by imagining himself in the circumstances of the electrons in question. He may have intuitions concerning theories about electrons, which in turn help him to understand their behavior. But at no point would he expect to understand what it is actually like to be an electron—indeed, the very notion of such intuition is laughable. Rationalizing human behavior, however, is precisely an exercise in simulating, in our mind’s eye, what it would be like to be the person whose behavior we are trying to understand. Only when we can imagine this simulated version of ourselves responding in the manner of the individual in question do we really feel that we have understood the behavior in question.
So effortlessly can we perform this exercise of “understanding by simulation” that it rarely occurs to us to wonder how reliable it is. And yet, as the earlier example of the organ donors illustrates, our mental simulations have a tendency to ignore certain types of factors that turn out to be important. The reason is that when we think about how we think, we instinctively emphasize consciously accessible costs and benefits such as those associated with motivations, preferences, and beliefs—the kinds of factors that predominate in social scientists’ models of rationality. Defaults, by contrast, are a part of the environment in which the decision maker operates, and so affect behavior in a way that is largely invisible to the conscious mind, and therefore largely absent from our commonsense explanations of behavior.9 And defaults are just the proverbial tip of the iceberg. For several decades, psychologists and, more recently, behavioral economists have been examining human decision-making, often in controlled laboratory settings. Their findings not only undermine even the most basic assumptions of rationality but also require a whole new way of thinking about human behavior.10
In countless experiments, for example, psychologists have shown that an individual’s choices and behavior can be influenced by “priming” them with particular words, sounds, or other stimuli. Subjects in experiments who read words like “old” and “frail” walk more slowly down the corridor when they leave the lab. Consumers in wine stores are more likely to buy German wine when German music is playing in the background, and French wine when French music is playing. Survey respondents asked about energy drinks are more likely to name Gatorade when they are given a green pen in order to fill out the survey. And shoppers looking to buy a couch online are more likely to opt for an expensive, comfortable-looking couch when the background of the website is of fluffy white clouds, and more likely to buy the harder, cheaper option when the background consists of dollar coins.11
Our responses can also be skewed by the presence of irrelevant numerical information. In one experiment, for example, participants in a wine auction were asked to write down the last two digits of their social security numbers before bidding. Although these numbers were essentially random and certainly had nothing to do with the value a buyer should place on the wine, researchers nevertheless found that the higher the numbers, the more people were willing to bid. This effect, which psychologists call anchoring, affects all sorts of estimates that we make, from estimating the number of countries in the African Union to how much money we consider to be a fair tip or donation. Whenever you receive a solicitation from a charity with a “suggested” donation amount, in fact, or a bill with precomputed tip percentages, you should suspect that your anchoring bias is being exploited—because by suggesting amounts on the high side, the requestor is anchoring your initial estimate of what is fair. Even if you subsequently adjust your estimate downward—because, say, a 25 percent tip seems like too much—you will probably end up giving more than you would have without the initial suggestion.12
Individual preferences can also be influenced dramatically simply by changing the way a situation is presented. Emphasizing one’s potential to lose money on a bet, for example, makes people more risk averse while emphasizing one’s potential to win has the opposite effect, even when the bet itself is identical. Even more puzzling, an individual’s preferences between two items can be effectively reversed by introducing a third alternative. Say, for example, that option A is a high-quality, expensive camera while B is both much lower quality and also much cheaper. In isolation, this could be a difficult comparison to make. But if, as shown in the figure below, I introduce a third option, C1, that is clearly more expensive than A and around the same quality, the choice between A and C1 becomes unambiguous. In these situations people tend to pick A, which seems perfectly reasonable until you consider what happens if I introduce instead of C1 a third option, C2, that is about as expensive as B yet significantly lower quality. Now the choice between B and C2 is clear, and so people tend to pick B. Depending on which third option is introduced, in other words, the preference of the decision maker can effectively be reversed between A and B, even though nothing about either has changed. What’s even stranger is that the third option—the one that causes the switch in preferences—is never itself chosen.13
Illustration of preference reversal
Continuing this litany of irrationality, psychologists have found that human judgments are often affected by the ease with which different kinds of information can be accessed or recalled. People generally overestimate the likelihood of dying in a terrorist attack on a plane relative to dying on a plane from any cause, even though the former is strictly le
ss likely than the latter, simply because terrorist attacks are such vivid events. Paradoxically, people rate themselves as less assertive when they are asked to recall instances where they have acted assertively—not because the information contradicts their beliefs, but rather because of the effort required to recall it. They also systematically remember their own past behavior and beliefs to be more similar to their current behavior and beliefs than they really were. And they are more likely to believe a written statement if the font is easy to read, or if they have read it before—even if the last time they read it, it was explicitly labeled as false.14
Finally, people digest new information in ways that tend to reinforce what they already think. In part, we do this by noticing information that confirms our existing beliefs more readily than information that does not. And in part, we do it by subjecting disconfirming information to greater scrutiny and skepticism than confirming information. Together, these two closely related tendencies—known as confirmation bias and motivated reasoning respectively—greatly impede our ability to resolve disputes, from petty disagreements over domestic duties to long-running political conflicts like those in Northern Ireland or Israel-Palestine, in which the different parties look at the same set of “facts” and come away with completely different impressions of reality. Even in science, confirmation bias and motivated reasoning play pernicious roles. Scientists, that is, are supposed to follow the evidence, even if it contradicts their own preexisting beliefs; and yet, more often then they should, they question the evidence instead. The result, as the physicist Max Planck famously acknowledged, is often that “A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die.”15
WHAT IS RELEVANT?
Taken together, the evidence from psychological experiments makes clear that there are a great many potentially relevant factors that affect our behavior in very real and tangible ways but that operate largely outside of our conscious awareness. Unfortunately, psychologists have identified so many of these effects—priming, framing, anchoring, availability, motivated reasoning, loss aversion, and so on—that it’s hard to see how they all fit together. By design, experiments emphasize one potentially relevant factor at a time in order to isolate its effects. In real life, however, many such factors may be present to varying extents in any given situation; thus it’s critical to understand how they interact with one another. It may be true, in other words, that holding a green pen makes you think of Gatorade, or that listening to German music predisposes you to German wine, or that thinking of your social security number affects how much you will bid for something. But what will you buy, and how much will you pay for it, when you are exposed to many, possibly conflicting, subconscious influences at once?
It simply isn’t clear. Nor is the profusion of unconscious psychological biases the only problem. To return to the ice cream example from before, although it may be true that I like ice cream as a general rule, how much I like it at a particular point in time might vary considerably, depending on the time of day, the weather, how hungry I am, and how good the ice cream is that I expect to get. My decision, moreover, doesn’t depend just on how much I like ice cream, or even just the relation between how much I like it versus how much it costs. It also depends on whether or not I know the location of the nearest ice cream shop, whether or not I have been there before, how much of a rush I’m in, who I’m with and what they want, whether or not I have to go to the bank to get money, where the nearest bank is, whether or not I just saw someone else eating an ice cream, or just heard a song that reminded me of a pleasurable time when I happened to be eating an ice cream, and so on. Even in the simplest situations, the list of factors that might turn out to be relevant can get very long very quickly. And with so many factors to worry about, even very similar situations may differ in subtle ways that turn out to be important. When trying to understand—or better yet predict—individual decisions, how are we to know which of these many factors are the ones to pay attention to, and which can be safely ignored?
The ability to know what is relevant to a given situation is of course the hallmark of commonsense knowledge that I discussed in the previous chapter. And in practice, it rarely occurs to us that the ease with which we make decisions disguises any sort of complexity. As the philosopher Daniel Dennett points out, when he gets up in the middle of the night to make himself a midnight snack, all he needs to know is that there is bread, ham, mayonnaise, and beer in the fridge, and the rest of the plan pretty much works itself out. Of course he also knows that “mayonnaise doesn’t dissolve knives on contact, that a slice of bread is smaller than Mount Everest, that opening the refrigerator doesn’t cause a nuclear holocaust in the kitchen” and probably trillions of other irrelevant facts and logical relations. But somehow he is able to ignore all these things, without even being aware of what it is that he’s ignoring, and focus on the few things that matter.16
But as Dennett argues, there is a big difference between knowing what is relevant in practice and being able to explain how it is that we know it. To begin with, it seems clear that what is relevant about a situation is just those features that it shares with other comparable situations—for example, we know that how much something costs is relevant to a purchase decision because cost is something that generally matters whenever people buy something. But how do we know which situations are comparable to the one we’re in? Well, that also seems clear: Comparable situations are those that share the same features. All “purchase” decisions are comparable in the sense that they involve a decision maker contemplating a number of options, such as cost, quality, availability, and so on. But now we encounter the problem. Determining which features are relevant about a situation requires us to associate it with some set of comparable situations. Yet determining which situations are comparable depends on knowing which features are relevant.
This inherent circularity poses what philosophers and cognitive scientists call the frame problem, and they have been beating their heads against it for decades. The frame problem was first noticed in the field of artificial intelligence, when researchers started trying to program computers and robots to solve relatively simple everyday tasks like, say, cleaning a messy room. At first they assumed that it couldn’t be that hard to write down everything that was relevant to a situation like this. After all, people manage to clean their rooms every day without even really thinking about it. How hard could it be to teach a robot? Very hard indeed, as it turned out. As I discussed in the last chapter, even the relatively straightforward activity of navigating the subway system requires a surprising amount of knowledge about the world—not just about subway doors and platforms but also about maintaining personal distance, avoiding eye contact, and getting out of the way of pushy New Yorkers. Very quickly AI researchers realized that virtually every everyday task is difficult for essentially the same reason—that the list of potentially relevant facts and rules is staggeringly long. Nor does it help that most of this list can be safely ignored most of the time—because it’s generally impossible to know in advance which things can be ignored and which cannot. So in practice, the researchers found that they had to wildly overprogram their creations in order to perform even the most trivial tasks.17
The intractability of the frame problem effectively sank the original vision of AI, which was to replicate human intelligence more or less as we experience it ourselves. And yet there was a silver lining to this defeat. Because AI researchers had to program every fact, rule, and learning process into their creations from scratch, and because their creations failed to behave as expected in obvious and often catastrophic ways—like driving off a cliff or trying to walk through a wall—the frame problem was impossible to ignore. Rather than trying to crack the problem, therefore, AI researchers took a different approach entirely—one that emphasized statistical models of data rather than thought processes. This approach, which nowadays is called machine learning, was f
ar less intuitive than the original cognitive approach, but it has proved to be much more productive, leading to all kinds of impressive breakthroughs, from the almost magical ability of search engines to complete queries as you type them to building autonomous robot cars, and even a computer that can play Jeopardy!18
WE DON’T THINK THE WAY WE THINK WE THINK
The frame problem, however, isn’t just a problem for artificial intelligence—it’s a problem for human intelligence as well. As the psychologist Daniel Gilbert describes in Stumbling on Happiness, when we imagine ourselves, or someone else, confronting a particular situation, our brains do not generate a long list of questions about all the possible details that might be relevant. Rather, just as an industrious assistant might use stock footage to flesh out a drab PowerPoint presentation, our “mental simulation” of the event or the individual in question simply plumbs our extensive database of memories, images, experiences, cultural norms, and imagined outcomes, and seamlessly inserts whatever details are necessary in order to complete the picture. Survey respondents leaving restaurants, for example, readily described the outfits of the waiters inside, even in cases where the waitstaff had been entirely female. Students asked about the color of a classroom blackboard recalled it as being green—the normal color—even though the board in question was blue. In general, people systematically overestimate both the pain they will experience as a consequence of anticipated losses and the joy they will garner from anticipated gains. And when matched online with prospective dates, subjects report greater levels of liking for their matches when they are given less information about them. In all of these cases, a careful person ought to respond that he can’t answer the question accurately without being given more information. But because the “filling in” process happens instantaneously and effortlessly, we are typically unaware that it is even taking place; thus it doesn’t occur to us that anything is missing.19
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