Excerpt from:

Everything is Obvious, Once You Know the Answer

By Duncan J. Watts Copyright 2011

Crown Publishing Group

With the help of our resident computer programmer, a young Hungarian named Peter Hausel, and some friends at Bolt media, an early social networking site for teenagers, we set up a Web-based experiment designed to emulate a "market" for music. Bolt agreed to advertise our experiment, called Music Lab, on their site, and over the course of several weeks about fourteen thousand of its members clicked through on the banner ads and agreed to participate. Once they got to our site they were asked to listen to, rate, and if they chose to, download songs by unknown bands. Some of the participants saw only the names of the songs while others also saw how many times the songs had been downloaded by previous participants. People in the latter "social influence" category were further split into eight parallel "worlds" such that they could only see the prior downloads of people in their own world. Thus if a new arrival were to be allocated (randomly) to World #1, she might see the song "She Said" by the band Parker Theory in first place. But if she were allocated instead to World #4, Parker Theory might be in tenth place and "Lockdown" by 52 Metro might be first instead.

We didn't manipulate any of the rankings-all the worlds started out identically, with zero downloads. But because the different worlds were carefully kept separate, they could subsequently evolve independently of one another. This setup therefore enabled us to test the effects of social influence directly. If people know what they like regardless of what other people think, there ought not to be any difference between the social influence and independent conditions. In all cases, the same songs should win by roughly the same amount. But if people do not make decisions independently, and if cumulative advantage applies, the different worlds within the social influence condition should look very different from one another, and they should all look different from the independent condition.

What we found was that when people had information about what other people downloaded,

they were indeed influenced by it in the way that cumulative advantage theory would predict. In all the "social influence" worlds, that is, popular songs were more popular (and unpopular songs were less popular) than in the independent condition. At the same time, however, which particular songs turned out to be the most popular-the "hits"-were different in different worlds. Introducing social influence into human decision making, in other words, increased not just inequality but unpredictability as well. Nor could this unpredictability be eliminated by accumulating more information about the songs any more than studying the surfaces of a pair of dice could help you predict the outcome of a roll. Rather, unpredictability was inherent to the dynamics of the market itself.

Social influence, it should be noted, didn't eliminate quality altogether: It was still the case that, on average, "good" songs (as measured by their popularity in the independent condition) did better than "bad" ones. It was also true that the very best songs never did terribly, while the very worst songs never actually won. That said, even the best songs could fail to win sometimes, while the worst songs could do pretty well. And for everything in the middle-the majority of songs that were neither the best nor the worst-virtually any outcome was possible. The song "Lockdown" by 52 Metro, for example, ranked twenty-sixth out of forty-eight in quality; yet it was the no. 1 song in one social- influence world, and fortieth in another. The "average" performance of a particular song, in other words, is only meaningful if the variability that it exhibits from world to world is small. But it was precisely this random variability that turned out to be large. For example, by changing the format of the website from a randomly arranged grid of songs to a ranked list we found we could increase the effective strength of the social signal, thereby increasing both the inequality and unpredictability. In this "strong influence" experiment, the random fluctuations played a bigger role in determining a song's ranking than eyen the largest differences in quality. Overall, a song in the Top 5 in terms of quality had only a 50 percent chance of finishing in the Top 5 of success.

Many observers interpreted our findings as a commentary on the arbitrariness of teenage music tastes or the vacuousness of contemporary pop music. But in principle the experiment could have been about any choice that people make in a social setting: whom we vote for, what we think about homosexual marriage, which phone we buy or social networking service we join, what clothes we wear to work, or how we deal with our credit card debt. In many cases designing these experiments is easier said than done, and that's why we chose' to study music. People like to listen to music and they're used to downloading it from the Web, so by setting up what looked like a site for music downloads we could conduct an experiment that was not only cheap to run (we didn't have to pay our subjects) but was also reasonably close to a "natural" environment. But in the end all that really mattered was that our subjects were making choices among competing options, and that their choices were being influenced by what they thought other people had chosen. Teenagers also were an expedient choice, because that's mostly who was hanging around on social networking sites in 2004. But once again, there was nothing special about teenagers-as we showed in a subsequent version of the experiment for which we recruited mostly adult professionals. As you might expect, this population had different preferences than the teenagers, and so the average performance of the songs changed slightly. Nevertheless, they were just as influenced by one another's behavior as the teenagers were, and so generated the same kind of inequality and unpredictability.

What the Music Lab experiment really showed, therefore, was remarkably similar to the basic insight from Granovetter's riot model-that when individuals are influenced by what other people are doing, similar groups of people can end up behaving in very different ways. This may not sound like a big deal, but it fundamentally undermines the kind of commonsense explanations that we offer for why some things succeed and others fail, why social norms dictate that we do some things and not others, or even why we believe what we believe. Commonsense explanations sidestep the whole problem of how individual choices aggregate to collective behavior simply by replacing the collective with a representative individual. And

because we think we know why individual people do what they do, as soon as we know what happened, we can always claim that it was what this fictitious individual-"the people," "the market," whatever- wanted.

Please answer the following questions using the article above

1. The article described one experiment that was completed to show how social influence affects people's behavior. Based on the description of the experiment, how would you state the null and alternative hypotheses in layman's terms?
2. What are the treatment(s) in the experiment? That is, what is being manipulated?
3. Does the experiment have a control group, and if so, which group serves as the control?
4. How would you describe their basic research design (e.g., one-shot case study)?
5. Do you think the study is high or low in internal validity? Why?
6. Do you think the study is high or low in external validity? Why?
7. How would you summarize the experiment's findings?