Everything Is Obvious (32 page)

Rather than questing after grand theories or universal laws of human behavior, therefore, Merton instead advocated that sociologists should focus on developing “theories of the middle range,” meaning theories that are broad enough to account for more than isolated phenomena but specific enough
to say something concrete and useful. For example, the “theory of relative deprivation” states that people feel distressed by circumstances only inasmuch as their hardship exceeds that of the people around them. Thus if your house burns down in a freak fire, you’re devastated, but if your whole city is wiped out in an earthquake and hundreds of your neighbors die, you feel lucky to be alive. It’s not a completely general theory, claiming only to predict how people respond to adversity, but it also aims to apply to perceptions of adversity quite broadly. Likewise, the “theory of the role set” stresses that each individual plays not only multiple roles—a teacher at school, a father at home, a catcher on the weekend softball team—but also that each of these roles is itself a collection of relationships: between a teacher and his students, between him and his colleagues, and between him and his principal. Again, the theory is somewhat specific—saying nothing about markets or governments, or other important features of the social world—but also somewhat general, applying to people of all kinds.
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Merton’s call for middle-range theories is generally regarded as sensible, but it didn’t quell the ardor for theories of a grander nature. Barely a year after Merton published his critique, in fact, the economist John Harsanyi, who shared the 1994 Nobel memorial Prize in Economics for his work on game theory, proposed that rational choice theory—the theory of human decision making that I discussed in
Chapter 2
—was ready to provide precisely the kind of general theory that Merton has just concluded was wildly premature. And so another cycle began, with rational choice theorists drawing parallels between their efforts and Newtonian mechanics, while critics increasingly leveled the same complaints against it that the rational choice theorists themselves had made about the previous round of theories like Parsons’s.
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Nor has
the growing realization that rational choice theory cannot provide a universal theory of human behavior any more than its predecessors could yet delivered social science from the green-eyed monster of physics envy.
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Quite to the contrary, if the complaint of my physicist colleague is anything to go by, even if sociologists have finally gotten tired of grand theories of everything, there is a whole generation of physicists waiting to step into the breach.
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When you think about the sheer complexity of human behavior, this approach to doing social science seems kind of implausible. As I discussed in
Chapter 2
, individual behavior is complicated by dozens of psychological biases, many of which operate outside of our conscious awareness and interact in as-yet-unknown ways. And as I discussed in
Chapter 3
, when individuals interact with one another, their collective behavior may simply not be derivable from their individual attributes and incentives, no matter how much you know about them. Given that the real complexity of the social world—involving not just people and groups, but also a bewildering array of markets, governments, firms, and other institutions that we have created for ourselves—is so much greater than I have even begun to describe here, why on earth would any one person even
think
that they could write down a set of rules that could explain it all?

My answer is that social theorists are people too, and so they make the same mistake that planners, politicians, marketers, and business strategists make, which is to dramatically underestimate the difficulty of what they are trying to do. And just like planners, politicians, and so on, no matter how many times such grand theories fail, there is always someone new who thinks that it can’t be that difficult—because, after all, “it’s not rocket science.” If much of what sociology has to offer seems like common sense, in other words, it
is not just because everything about human behavior seems obvious once you know the answer. Part of the problem is also that social scientists, like everyone else, participate in social life and so feel as if they can understand why people do what they do simply by thinking about it. It is not surprising, therefore, that many social scientific explanations suffer from the same weaknesses—ex post facto assertions of rationality, representative individuals, special people, and correlation substituting for causation—that pervade our commonsense explanations as well.

One response to this problem, as Lazarsfeld’s colleague Samuel Stouffer noted more than sixty years ago, is for sociologists to depend
less
on their common sense, not more, and instead try to cultivate uncommon sense.
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But getting away from commonsense reasoning in sociology is easier said than done. In large part the difficulty is that for most of the history of social science, it simply hasn’t been possible to
measure
the elements of social phenomena the way we measure the elements of physical and biological phenomena. Social phenomena, as I have already noted, consist of large populations of people interacting with and influencing one another as well as with the organizations and governments they create—none of which is easy to observe directly, let alone put in a lab.
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Recently, however, the world has begun to change in ways that may lift some of these historical limitations on social science. Communication technologies, like e-mail, cell phones, and instant messaging now implicitly trace out social networks among billions of individuals, along with the flow of information among them. Online communities such as Facebook,
Twitter, Wikipedia, and World of Warcraft facilitate interactions among people in ways that both promote new kinds of social activity and also record it. Crowdsourcing sites like Amazon’s Mechanical Turk are increasingly being used as “virtual labs” in which researchers can run psychological and behavioral experiments.
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And Web search, online media, and electronic commerce are generating ever-increasing insight into the intentions and actions of people everywhere. The capability to observe the actions and interactions of potentially billions of people presents some serious issues about the rights and privacy of individuals, and so we must proceed with caution.
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Nevertheless, these technologies also exhibit enormous scientific potential, allowing us for the first time in history to observe, in high fidelity, the real-time behavior of large groups, and even societies as a whole.

For example, the Music Lab experiments that I discussed in
Chapter 3
, which showed the importance of social influence in determining success, involved nearly thirty thousand participants. One could have dreamed up this experiment fifty years ago—back when social psychologists were first pioneering experimental studies of influence and group decision making—but it would have been impossible to conduct it until recently for the simple reason that you can’t fit that many people in a physical lab. Likewise, the “influencers” study on Twitter that I discussed in
Chapter 4
was designed to answer a question—about whether or not special individuals cause information to spread—that has been around for decades. Answering it, however, required us to track the diffusion of more than 70 million URLs over the entire Twitter network for a two-month period. Prior to social networking services like Twitter and Facebook, which, remember, are just a few years old, that level of scale and resolution would have been impossible.
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Other experiments that I have described, like the Small World experiment from
Chapter 4
, were certainly possible in the pre-Internet era, but not on the scale at which they can now be conducted. Milgram’s original experiment, for example, used physical letters and relied on just three hundred individuals attempting to reach a single person in Boston. The e-mail–based experiment that my colleagues and I conducted back in 2002 involved more than sixty thousand people directing messages to one of eighteen targets, who in turn were located in thirteen countries. In the course of being delivered, the message chains passed through more than 160 different countries; thus for all its limitations, the experiment was at least a crude test of the small-world hypothesis on a truly global scale. Likewise, David Reiley and Randall Lewis’s field experiment on ad effectiveness, described in
Chapter 8
, was similar in design to experiments that had been conducted in the past, but with 1.6 million participants, it was many times larger. The sheer scale of the exercise is impressive simply on the grounds that it can be done at all, but it’s also important scientifically—because it’s possible that the effects, while real, can be small, in which case one needs very large numbers to tease them out of the noise.
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Another kind of study that would have been impossible to conduct until recently concerns one of the most widely observed patterns in social life, known in sociology as the homophily principle—the idea that “birds of a feather flock together.” For several decades now, wherever sociologists have looked they have found that friends, spouses, coworkers, and social acquaintances are more similar than strangers with respect to a whole range of attributes—like race, age,
gender, income, education—and also attitudes. But where does all this similarity come from? At first, the answer seems obvious: People are likely to form ties with others who are similar because, rightly or wrongly, that’s whom they’d prefer to spend their time with. But what this commonsense explanation overlooks is that people can only choose their friends from among the people they actually meet, which is determined to a large extent by the people they work with, or who belong to the same organizations, or to whom they are introduced by mutual acquaintances. And as sociologists have also shown, many of these social environments tend to be highly homogeneous in terms of race, gender, age, and education. As a result, it is entirely possible that the similarity we see around us has less to do with our own psychological preferences than the restricted opportunities that the world presents to us.
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Resolving problems like this one is important because it has implications for how we go about dealing with controversial issues like racial segregation and affirmative action. Settling the matter with data, however, is extremely difficult because disentangling the various cause-and-effect relationships requires one to keep track of individuals, networks, and groups over extended intervals of time.
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And historically, that sort of data just hasn’t been available. Communication technologies like e-mail, however, have the potential to change all that. Because reciprocated e-mails for the most part represent real relationships, it is possible to use e-mail exchanges as a way to observe underlying social networks. And because e-mail servers can easily log the interactions among thousands or even millions of individuals over long periods of time, it is possible to reconstruct the evolution of even very large networks in great detail. Combine this sort of information with other data that is routinely collected by
firms, universities, and other organizations about their members, and a rough approximation of the more complete picture starts to emerge.

Recently my former graduate student Gueorgi Kossinets and I used exactly this kind of approach to study the origins of homophily within the students, faculty, and staff of a university community. As with previous studies, we found that acquaintances—meaning people who exchanged e-mail on a regular basis—were considerably more similar on a range of attributes such as age, gender, academic major, and so on than strangers. We also found that similar people who were not acquainted were more likely than dissimilar people to connect to each other over time—just as common sense would contend. Finally, however, we found that individuals who were already “close” to each other, either because they shared mutual friends or belonged to the same groups, were more similar than distant pairs, and that most of the bias toward connecting similar individuals disappeared once we accounted for the effects of proximity. Our conclusion was that although the individuals in our community did exhibit some preference for others who were similar, it was a relatively weak preference that had been amplified over time, by successive “rounds” of choices, to generate the appearance of a much stronger preference in the observed network.
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Another problem to do with homophily that the Internet may help to answer is one that political scientists and sociologists have long worried about—namely, that Americans, whether by choice or by circumstance, are increasingly associating with like-minded neighbors and acquaintances. If true, the trend is thought to be problematic, as homogeneous social circles can also lead to a more balkanized society in which differences of opinion lead to political conflict rather than exchanges of ideas among equals. But is there
actually any such trend? Political scientists generally agree that Congress is indeed more polarized now than at almost any point in history, and that the media is not much better. However, studies of polarization among ordinary citizens have tended to reach conflicting conclusions: Some find that it has increased dramatically while others point to levels of agreement that have changed little in decades.
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One possible explanation for these contradictory results is that people think that they agree with their friends much more than they actually do; thus much of the polarization may be perceived rather than real. But testing this hypothesis, although simple in theory, is difficult in practice. The reason is that in order to measure whether friends agree as much as they think they do, one would need to ask, for every issue of interest, and for every pair of friends A and B, what A thinks about the issue, what B thinks about the issue, and what A thinks B thinks about it. Do this for lots of issues and many pairs of individuals, and you have a tremendously laborious survey exercise, especially if you also have to get each respondent to name friends and then go track them down.
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