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In 1907, a statistician named Francis Galton recorded the entries from a weight-judging competition as people guessed the weight of an ox. Galton analyzed hundreds of estimates and found that while individual guesses varied wildly, the median of the entries was surprisingly accurate and within one percent of the ox's real weight. When Galton published his results, he ushered the theory of collective intelligence, or the "wisdom of crowds," into the public conscience.
Collective wisdom has its limits, though. In a new study published in the Journal of the Royal Society Interface, researchers Albert Kao (Harvard University), Andrew Berdahl (Santa Fe Institute), and their colleagues examined just how accurate our collective intelligence is and how individual bias and information sharing skew aggregate estimates. Using their findings, they developed a mathematical correction that takes into account bias and social information to generate an improved crowd estimate. In the study, their corrected measures were more accurate than the mean, median, and other traditional statistics.
Evidence - Wisdom - Crowds - Kao - Lot
"There is growing evidence that the wisdom of crowds can be really powerful," Kao says. "A lot of studies show that you can calculate the average of estimates and that average can be surprisingly good."
"However," adds Berdahl, "there is a great deal of evidence that people have strong biases in estimation and decision tasks."
Researchers - Volunteers - Study
The researchers recruited over 800 volunteers to participate in the study and asked each...
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