A major problem resulting from the massive use of social media is the potential spread of incorrect information. Yet, very few studies have investigated the impact of incorrect information on individual and collective decisions. We performed experiments in which participants had to estimate a series of quantities, before and after receiving social information. Unbeknownst to them, we controlled the degree of inaccuracy of the social information through ‘virtual influencers’, who provided some incorrect information. We find that a large proportion of individuals only partially follow the social information, thus resisting incorrect information. Moreover, incorrect information can help improve group performance more than correct information, when going against a human underestimation bias. We then design a computational model whose predictions are in good agreement with the empirical data, and sheds light on the mechanisms underlying our results. Besides these main findings, we demonstrate that the dispersion of estimates varies a lot between quantities, and must thus be considered when normalizing and aggregating estimates of quantities that are very different in nature. Overall, our results suggest that incorrect information does not necessarily impair the collective wisdom of groups, and can even be used to dampen the negative effects of known cognitive biases.
human collective behaviour, incorrect information, social influence, computational modelling, wisdom of crowds;
Bertrand Jayles, Ramon Escobedo, Stéphane Cezera, Adrien Blanchet, Tatsuya Kameda, Clément Sire, and Guy Théraulaz, “The impact of incorrect social information on collective wisdom in human groups”, IAST working paper, n. 20-106, May 2020.
Journal of the Royal Society Interface, vol. 117, n. 20200496, September 2020