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By Root 1051 0

kind is likely ubiquitous. But unlike the simple threshold of Granovetter’s thought experiment, the resulting decision rule is neither binary nor deterministic. Rather, when people tend to like something that other people like, differences in popularity are subject to what is called cumulative advantage, meaning that once, say, a song or a book becomes more popular than another, it will tend to become more popular still. Over the years, researchers have studied a number of different types of cumulative advantage models, but they all have the flavor that even tiny random fluctuations tend to get bigger over time, generating potentially enormous differences in the long run, a phenomenon that is similar to the famous “butterfly effect” from chaos theory, which says that a butterfly fluttering its wings in China can lead to a hurricane months later and oceans away.14

As with Granovetter’s model, cumulative advantage models have disruptive implications for the kinds of explanations that we give of success and failure in cultural markets. Commonsense explanations, remember, focus on the thing itself—the song, the book, or the company—and account for its success solely in terms of its intrinsic attributes. If we were to imagine history being somehow “rerun” many times, therefore, explanations in which intrinsic attributes were the only things that mattered would predict that the same outcome would pertain every time. By contrast, cumulative advantage would predict that even identical universes, starting out with the same set of people and objects and tastes, would nevertheless generate different cultural or marketplace winners. The Mona Lisa would be popular in this world, but in some other version of history it would be just one of many masterpieces, while another painting that most of us have never heard of would be in its place. Likewise, the success of Harry Potter, Facebook, and The Hangover would turn out to be a product of chance and timing as much as of intrinsic quality.

In real life, however, we have only one world—the one that we are living in—thus it’s impossible to make the sort of “between world” comparisons that the models say we should. It may not surprise you, therefore, that when someone uses the output of a simulation model to argue that Harry Potter may not be as special as everyone thinks it is, Harry Potter fans tend not to be persuaded. Common sense tells us that Harry Potter must be special—even if the half dozen or so children’s book publishers who passed on the original manuscript didn’t know it at the time—because more than 350 million people bought it. And because any model necessarily makes all manner of simplifying assumptions, whenever we have to choose between questioning common sense and questioning a model, our tendency is to do the latter.

For exactly this reason, several years ago my collaborators Matthew Salganik and Peter Dodds and I decided to try a different approach. Instead of using computer models, we would run a controlled, laboratory-style experiment in which real people made more or less the same kinds of choices that they make in the real world—in this case, between a selection of songs. By randomly assigning different people to different experimental conditions, we would effectively create the “many worlds” situation imagined in the computer models. In some conditions, people would be exposed to information about what other people were doing, but it would be up to them to decide whether or not to be influenced by the information and how. In other conditions, meanwhile, participants would be faced with exactly the same set of choices, but without any information about other participants’ decisions; thus they would be forced to behave independently. By comparing the outcomes in the “social influence” conditions with those in the “independent” condition, we would be able to observe the effects of social influence on collective outcomes directly. In particular, by running many such worlds in parallel, we would be able to measure how much of a song’s success depended on its intrinsic