The new method is simple. For a given question, people are asked two things: What they think the right answer is, and what they think popular opinion will be. The variation between the two aggregate responses indicates the correct answer.
“In situations where there is enough information in the crowd to determine the correct answer to a question, that answer will be the one [that] most outperforms expectations,” says paper co-author Drazen Prelec, a professor at the MIT Sloan School of Management as well as the Department of Economics and the Department of Brain and Cognitive Sciences.
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Across all these areas, the researchers found that the “surprisingly popular” algorithm reduced errors by 21.3 percent compared to simple majority votes, and by 24.2 percent compared to basic confidence-weighted votes (where people express how confident they are in their answers). And it reduced errors by 22.2 percent compared to another kind of confidence-weighted votes, those taking the answers with the highest average confidence levels.
The paper, “A solution to the single-question crowd wisdom problem,” is being published today in Nature. The authors are Prelec; John McCoy, a doctoral student in the MIT Department of Brain and Cognitive Sciences; and H. Sebastian Seung, a professor of neuroscience and computer science at Princeton University and a former MIT faculty member. Prelec and McCoy are also researchers in the MIT Neuroeconomics Laboratory, where Prelec is the principal investigator.
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In this sense, the “surprisingly popular” principle is not simply derived from the wisdom of crowds. Instead, it uses the knowledge of a well-informed subgroup of people within the larger crowd as a diagnostically powerful tool that points to the right answer.
“A lot of crowd wisdom weights people equally,” McCoy explains. “But some people have more specialized knowledge.” And those people — if they have both correct information and a correct sense of public perception — make a big difference.
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