As we engage more with social networking sites, there is always the danger of a “group think” mentality–when people follow a group consensus rather than critically evaluate information; make decisions without guidance from the social network; or follow “gurus” who provide them with bad information. So how do we avoid these errors and maximize the “wisdom of the crowd”?
Part of the answer may come from recent work of Media Lab researcher Dr. Yaniv Altshuler, an expert in collective intelligence methods, who is working with Toshiba Professor Alex ‘Sandy’ Pentland in the Media Lab’s Human Dynamics research group. Altshuler has developed a tool for social financial trading that helps guide users to make better decisions by improving the information flow within the networks. This is accomplished by diverting the traders’ attention away from certain links, and drawing their attention to others, changing the dynamics of the network.
Working with the eToro investment network, Altshuler distributed $20 trading coupons to 500 active financial traders out of the more than two million eToro members. Matches between traders and recommendations were based on an innovative algorithm designed to optimize information flow within the network. Even this small number of coupons was enough to move the entire network away from dangerously high levels of “groupthink,” and as a consequence, the entire trading community–not just the 500 coupon users–saw a significant increase to their rate of return. The increase in return was more than 10 percent compared to those who traded without guidance from the social network, and 4 percent higher than those who only followed the highest-performing gurus.
“This study demonstrates how an efficient collaborative trading community can be formed by carefully balancing the complex mixture of ‘trend setters’ and ‘bellwethers’ who govern the behavior of the crowd,” says Altshuler.
“While users aren’t even aware of how we are helping to direct their attention,” adds Pentland, “we are helping to optimize the information they draw from the network, providing higher likelihood of increased gains, and helping to avoid dangerous market bubbles.”
The next step? “To implement this on a larger scale,” says Altshuler.
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