We are in the midst of an explosion of interest in collective intelligence, sometimes described as “the wisdom of crowds.” The basic idea is that if you aggregate the knowledge of a lot of people, you are likely to come up with the correct answer. If two heads are better than one, then a hundred heads are even better, and once we are dealing with thousands, a good answer starts to look inevitable. This idea has major implications for business decisions, stock markets, political movements and democracy itself.
In 1907, Francis Galton provided a memorable example. He asked about 800 people to guess the weight of an ox. None of them gave the correct answer (1,198 pounds), but the mean response was eerily accurate (1,197 pounds). Many experiments have replicated Galton’s finding, showing that if a large group is asked to make some numerical estimate, the mean answer (and sometimes the average) is remarkably good.
This kind of crowd wisdom seems like a parlor trick, but it has important practical lessons. Suppose we want to make a prediction about a presidential election, gross domestic product growth, the unemployment rate or the sales of a new commercial product. If we have a large number of predictions, we might want to aggregate them and use something like the average. Indeed, this is a significant part of the approach taken by Nate Silver in his highly publicized, and stunningly successful, work on recent elections.
Alternatively, we might want to create a “prediction market,” in which people make wagers on what they think will happen and the market’s odds emerge from those wagers. The Iowa Electronic Markets have long done something like this for presidential elections. Google Inc., Microsoft Corp., Hewlett-Packard Co. and other businesses have been experimenting with prediction markets as well.
In general, the resulting forecasts have proved extremely accurate. With respect to economic forecasts, prediction markets outperform both professional forecasters and polls. The most dramatic finding is that market prices often operate as probabilities. Some research has found that when prices suggest that events are likely to occur with 90 percent probability, they occur 90 percent of the time; when prices suggest a probability of 80 percent, the events happen 80 percent of the time; and so forth.
An appreciation of crowd wisdom suggests that social networks hold special potential, because they can aggregate diverse views with astonishing speed. These networks make it easy to find out whether people like certain goods and services, and if a lot of people do, perhaps we have reason for confidence about those goods and services. But recent research raises cautionary notes. It turns out that crowds may have much less wisdom when their members are listening to one another. In such cases, we can end up with forms of herding, or social cascades, that reflect serious biases.
Researchers have long known that crowds can be misled if their members influence one another. But the new research goes far beyond this simple point. Lev Muchnik, a professor at Hebrew University of Jerusalem, and his colleagues used a website that aggregates stories and allows people to post comments, which can in turn be voted “up” or “down.” An aggregate score comes from subtracting the number of down-votes from the number of up-votes.
The researchers created three conditions: “up-treated,” in which a comment, when it appeared, was automatically and artificially given an immediate “up” vote; “down-treated,” in which a comment, when it appeared, was automatically and artificially given an immediate “down” vote; and “control,” in which comments did not receive an artificial initial signal. Millions of site visitors were randomly assigned to one of the three conditions.
You might think that after so many visitors (and hundreds of thousands of ratings), the single initial vote could not possibly matter. If so, you would be wrong. After seeing an initial up-vote, the first viewer became 32 percent more likely to give an up-vote himself. What’s more, this effect persisted over time. After a period of five months, a single positive initial vote artificially increased the mean rating of comments by 25 percent. It also significantly increased “turnout” (the number of ratings).
With respect to negative votes, the picture was not symmetrical. The initial down-vote did increase the likelihood that the first viewer would also give a down-vote. But the effect was rapidly corrected, and after a period of five months, the artificial down-vote had no effect on median ratings (though it did increase turnout). Muchnik and his colleagues conclude that “whereas positive social influence accumulates, creating a tendency toward ratings bubbles, negative social influence is neutralized by crowd correction.” They believe that their findings have implications for product recommendations, stock-market predictions and electoral polling.
We should be careful before drawing large lessons from a single study, particularly when no money was on the line. But there is no question that some products, people, movements and ideas have enjoyed social success only because of the functional equivalent of early up-votes. On the Internet and elsewhere, there are lessons here about the essential unpredictability of crowds -- and about their occasional lack of wisdom.
(Cass R. Sunstein, the Robert Walmsley University professor at Harvard Law School, is a Bloomberg View columnist. He is the former administrator of the White House Office of Information and Regulatory Affairs, the co-author of “Nudge” and author of “Simpler: The Future of Government.”)
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