This article in last week’s New York Times Sunday Magazine is very interesting, I think. It seems to debunk a few popular myths about hit records and the conditions that create them. I’m posting it here in full as it probably will go Select soon.
As anyone who follows the business of culture is aware, the profits of cultural industries depend disproportionately on the occasional outsize success — a blockbuster movie, a best-selling book or a superstar artist — to offset the many investments that fail dismally. What may be less clear to casual observers is why professional editors, studio executives and talent managers, many of whom have a lifetime of experience in their businesses, are so bad at predicting which of their many potential projects will make it big. How could it be that industry executives rejected, passed over or even disparaged smash hits like “Star Wars,” “Harry Potter” and the Beatles, even as many of their most confident bets turned out to be flops? It may be true, in other words, that “nobody knows anything,” as the screenwriter William Goldman once said about Hollywood. But why? Of course, the experts may simply not be as smart as they would like us to believe. Recent research, however, suggests that reliable hit prediction is impossible no matter how much you know — a result that has implications not only for our understanding of best-seller lists but for business and politics as well.
Conventional marketing wisdom holds that predicting success in cultural markets is mostly a matter of anticipating the preferences of the millions of individual people who participate in them. From this common-sense observation, it follows that if the experts could only figure out what it was about, say, the music, songwriting and packaging of Norah Jones that appealed to so many fans, they ought to be able to replicate it at will. And indeed that’s pretty much what they try to do. That they fail so frequently implies either that they aren’t studying their own successes carefully enough or that they are not paying sufficiently close attention to the changing preferences of their audience.
The common-sense view, however, makes a big assumption: that when people make decisions about what they like, they do so independently of one another. But people almost never make decisions independently — in part because the world abounds with so many choices that we have little hope of ever finding what we want on our own; in part because we are never really sure what we want anyway; and in part because what we often want is not so much to experience the “best” of everything as it is to experience the same things as other people and thereby also experience the benefits of sharing.
There’s nothing wrong with these tendencies. Ultimately, we’re all social beings, and without one another to rely on, life would be not only intolerable but meaningless. Yet our mutual dependence has unexpected consequences, one of which is that if people do not make decisions independently — if even in part they like things because other people like them — then predicting hits is not only difficult but actually impossible, no matter how much you know about individual tastes.
The reason is that when people tend to like what other people like, differences in popularity are subject to what is called “cumulative advantage,” or the “rich get richer” effect. This means that if one object happens to be slightly more popular than another at just the right point, it will tend to become more popular still. As a result, even tiny, random fluctuations can blow up, generating potentially enormous long-run differences among even indistinguishable competitors — a phenomenon that is similar in some ways to the famous “butterfly effect” from chaos theory. Thus, if history were to be somehow rerun many times, seemingly identical universes with the same set of competitors and the same overall market tastes would quickly generate different winners: Madonna would have been popular in this world, but in some other version of history, she would be a nobody, and someone we have never heard of would be in her place.
Because it’s not possible in the real world to test theories about events that never happened, most of what we know about cumulative advantage has been worked out using mathematical models and computer simulations — an approach that is often criticized for glossing over the richness of real human behavior. Fortunately, the explosive growth of the Internet has made it possible to study human activity in a controlled manner for thousands or even millions of people at the same time. Recently, my collaborators, Matthew Salganik and Peter Dodds, and I conducted just such a Web-based experiment. In our study, published last year in Science, more than 14,000 participants registered at our Web site, Music Lab (www.musiclab.columbia.edu), and were asked to listen to, rate and, if they chose, download songs by bands they had never heard of. Some of the participants saw only the names of the songs and bands, while others also saw how many times the songs had been downloaded by previous participants. This second group — in what we called the “social influence” condition — was further split into eight parallel “worlds” such that participants could see the prior downloads of people only in their own world. We didn’t manipulate any of these rankings — all the artists in all the worlds started out identically, with zero downloads — but because the different worlds were kept separate, they subsequently evolved independently of one another.
This setup let us test the possibility of prediction in two very direct ways. First, if people know what they like regardless of what they think other people like, the most successful songs should draw about the same amount of the total market share in both the independent and social-influence conditions — that is, hits shouldn’t be any bigger just because the people downloading them know what other people downloaded. And second, the very same songs — the “best” ones — should become hits in all social-influence worlds.
What we found, however, was exactly the opposite. In all the social-influence worlds, the most popular songs were much more popular (and the least popular songs were less popular) than in the independent condition. At the same time, however, the particular songs that became hits were different in different worlds, just as cumulative-advantage theory would predict. Introducing social influence into human decision making, in other words, didn’t just make the hits bigger; it also made them more unpredictable.