This New York Times Magazine Article ”Is Justin Timberlake a Product of Cumulative Advantage?” discusses how the entertainment industry relies on the hitting it real big with a blockbuster to offset many failed investments. But, what causes one artist to became enormously popular is hard to explain and explains why studio executives so bad at predicting which of their many potential projects will become a home run. Recent research suggests that predicting hits is impossible. Predicting success is not a matter of just anticipating the preferences of the millions of individual people. One of the wrong assumptions is that people make decisions about what they like independently of one another. 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. As a result, tiny, random fluctuations can blow up, leading to long-run differences among indistinguishable competitors — a phenomenon similar to the“butterfly effect” from chaos theory.

Matthew Salganik and Peter Dodds, and Duncan Watts conducted a Web-based experiment where more than 14,000 participants registered at our Web site, Music Lab (www.musiclab.columbia.edu) listened to, rate and, if they chose, downloaded 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, “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. All the artists in all the worlds started out identically with zero downloads. Because the different worlds were kept separate, they subsequently evolved independently of one another. 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 and the best songs should become hits in all social-influence worlds. What they found was 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, the particular songs that became hits were different in different worlds. Certain songs reached a tipping point. Introducing social influence into human decision making made the hits bigger and also made them more unpredictable. “Good” songs had higher market share, on average, than “bad” ones, but ones own reactions were easily overwhelmed by his or her reactions to others. For example, a song in the Top 5 in terms of quality had only a 50 percent chance of finishing in the Top 5 of success. Social influence played as large a role in determining the market share of successful songs as differences in quality. This is just like a chapter out of tipping point. Long-run success of a song depends on the decisions of a few early-arriving individuals. Their choices are amplified and eventually locked in by the cumulative-advantage process. The “randomness” according to the article is that the early adopters who are chosen randomly make many different decisions resulting in market being unpredictably. This example displays a great example of network effects and popularity as a network phenomenon where the attractiveness of a song increases with the number of people using it. The models discussed in class would be helpful to better evaluate this experiment.