THUMBS-UP TO THE WISDOM OF CROWDS
cent of the time; when prices suggest a probability of 80 per cent, the events happen 80 per cent of the time, and so forth.
An appreciation of crowd wisdom suggests social networks hold special potential, because they can aggregate diverse views with astonishing speed. But recent research raises cautionary notes. It turns out 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 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 per cent 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 per cent.
With respect to negative votes, the picture was not symmetrical. The initial “down” vote did increase the likelihood 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. 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 their findings have implications for product recommendations, stock-market predictions and electoral polling.