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Human groups can perform extraordinary accurate estimations compared toindividuals by simply using the mean, median or geometric mean of theindividual estimations [Galton 1907, Surowiecki 2005, Page 2008]. However, thisis true only for some tasks and in general these collective estimations showstrong biases. The method fails also when allowing for social interactions,which makes the collective estimation worse as individuals tend to converge tothe biased result [Lorenz et al. 2011]. Here we show that there is a brightside of this apparently negative impact of social interactions into collectiveintelligence. We found that some individuals resist the social influence and,when using the median of this subgroup, we can eliminate the bias of the wisdomof the full crowd. To find this subgroup of individuals more confident in theirprivate estimations than in the social influence, we model individuals asestimators that combine private and social information with different relativeweights [Perez-Escudero & de Polavieja 2011, Arganda et al. 2012]. We thencomputed the geometric mean for increasingly smaller groups by eliminatingthose using in their estimations higher values of the social influence weight.The trend obtained in this procedure gives unbiased results, in contrast to thesimpler method of computing the median of the complete group. Our results showthat, while a simple operation like the mean, median or geometric mean of agroup may not allow groups to make good estimations, a more complex operationtaking into account individuality in the social dynamics can lead to a bettercollective intelligence.

Wisdom of the Confident: Using Social Interactions to Eliminate the Bias in Wisdom of the Crowds