The wisdom of crowds – just how wise are they?

The Wisdom of crowds is not a new concept – but research in to it’s use is. Derren Brown used this very concept in his show “How To Predict The Lottery” – it powers the interest in his websites and often is at the core of his live shows. MIT have this to say:

The rise of the Internet has sparked a fascination with what The New Yorker’s financial writer James Surowiecki called, in a book of the same name, “the wisdom of crowds”: the idea that aggregating or averaging the imperfect, distributed knowledge of a large group of people can often yield better information than canvassing expert opinion.

But as Surowiecki himself, and many commentators on his book, have pointed out, circumstances can conspire to undermine the wisdom of crowds. In particular, if a handful of people in a population exert an excessive influence on those around them, a “herding” instinct can kick in, and people will rally around an idea that could turn out to be wrong.

Fortunately, in a paper to be published in the Review of Economic Studies, researchers from MIT’s Departments of Economics and Electrical Engineering and Computer Science have demonstrated that, as networks of people grow larger, they’ll usually tend to converge on an accurate understanding of information distributed among them, even if individual members of the network can observe only their nearby neighbors. A few opinionated people with large audiences can slow that convergence, but in the long run, they’re unlikely to stop it.

In the past, economists trying to model the propagation of information through a population would allow any given member of the population to observe the decisions of all the other members, or of a random sampling of them. That made the models easier to deal with mathematically, but it also made them less representative of the real world. “What this paper does is add the important component that this process is typically happening in a social network where you can’t observe what everyone has done, nor can you randomly sample the population to find out what a random sample has done, but rather you see what your particular friends in the network have done,” says Jon Kleinberg, Tisch University Professor in the Cornell University Department of Computer Science, who was not involved in the research. “That introduces a much more complex structure to the problem, but arguably one that’s representative of what typically happens in real settings.”

More Over at MIT