Measuring the Crowd Within

Measuring the crowd within: probabilistic representations within individuals finds any benefit of averaging two responses from one person would yield support for this hypothesis, which is consistent with such models that responses of many people are distributed probabilistically.

Abstract

A crowd often possesses better information than do the individuals it comprises. For example, if people are asked to guess the weight of a prize-winning ox (Galton, 1907), the error of the average response is substantially smaller than the average error of individual estimates. This fact, which Galton interpreted as support for democratic governance, is responsible for the success of polling the audience in the television program ‘‘Who Wants to be a Millionaire’ ’ (Surowiecki, 2004) and for the superiority of combined over individual financial forecasts (Clemen, 1989). Researchers agree that this wisdom-of-crowds effect depends on a statistical fact: The crowd’s average will be more accurate as long as some of the error of one individual is statistically independent of the error of other individuals—as seems almost guaranteed to be the case. Whether a similar improvement can be obtained by averaging