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.

中文翻译：

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不正确的社会信息对人类群体集体智慧的影响

大量使用社交媒体导致的一个主要问题是不正确信息的潜在传播。然而，很少有研究调查错误信息对个人和集体决策的影响。我们进行了实验，参与者必须在接收社会信息之前和之后估计一系列数量。他们不知道的是，我们通过“虚拟影响者”控制了社会信息的不准确程度，他们提供了一些不正确的信息。我们发现很大一部分人只是部分地遵循社会信息，从而抵制不正确的信息。此外，在对抗人类低估偏差时，不正确的信息比正确的信息更有助于提高团队绩效。然后，我们设计了一个计算模型，其预测与经验数据非常吻合，并阐明了我们结果背后的机制。除了这些主要发现之外，我们还证明了估计的离散性在数量之间变化很大，因此在对性质非常不同的数量的估计进行归一化和汇总时必须考虑到这一点。总的来说，我们的结果表明，不正确的信息不一定会损害群体的集体智慧，甚至可以用来抑制已知认知偏见的负面影响。因此，在对性质非常不同的数量的估计进行归一化和汇总时必须加以考虑。总的来说，我们的结果表明，不正确的信息不一定会损害群体的集体智慧，甚至可以用来抑制已知认知偏见的负面影响。因此，在对性质非常不同的数量的估计进行归一化和汇总时必须加以考虑。总的来说，我们的结果表明，不正确的信息不一定会损害群体的集体智慧，甚至可以用来抑制已知认知偏见的负面影响。