Laboratoire de Physique Théorique, CNRS, Université de Toulouse (Paul Sabatier), 31062 Toulouse, France.
Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative, CNRS, Université de Toulouse, 31062 Toulouse, France.
Proc Natl Acad Sci U S A. 2017 Nov 21;114(47):12620-12625. doi: 10.1073/pnas.1703695114. Epub 2017 Nov 8.
In our digital and connected societies, the development of social networks, online shopping, and reputation systems raises the questions of how individuals use social information and how it affects their decisions. We report experiments performed in France and Japan, in which subjects could update their estimates after having received information from other subjects. We measure and model the impact of this social information at individual and collective scales. We observe and justify that, when individuals have little prior knowledge about a quantity, the distribution of the logarithm of their estimates is close to a Cauchy distribution. We find that social influence helps the group improve its properly defined collective accuracy. We quantify the improvement of the group estimation when additional controlled and reliable information is provided, unbeknownst to the subjects. We show that subjects' sensitivity to social influence permits us to define five robust behavioral traits and increases with the difference between personal and group estimates. We then use our data to build and calibrate a model of collective estimation to analyze the impact on the group performance of the quantity and quality of information received by individuals. The model quantitatively reproduces the distributions of estimates and the improvement of collective performance and accuracy observed in our experiments. Finally, our model predicts that providing a moderate amount of incorrect information to individuals can counterbalance the human cognitive bias to systematically underestimate quantities and thereby improve collective performance.
在我们的数字和互联社会中，社交网络、在线购物和声誉系统的发展提出了这样的问题：个人如何使用社交信息，以及它如何影响他们的决策。我们报告了在法国和日本进行的实验，在这些实验中，被试可以在收到其他被试的信息后更新他们的估计。我们在个体和集体层面上测量和建模这种社会信息的影响。我们观察并证明，当个体对某个数量的先验知识很少时，他们估计值的对数分布接近柯西分布。我们发现，社会影响有助于提高群体的适当定义的集体准确性。我们量化了当向被试提供未知的额外受控和可靠信息时，群体估计的改进。我们表明，被试对社会影响的敏感性使我们能够定义五个稳健的行为特征，并且随着个人和群体估计之间的差异而增加。然后，我们使用我们的数据构建和校准了一个集体估计模型，以分析个体收到的信息量和质量对群体绩效的影响。该模型定量地再现了我们实验中观察到的估计分布和集体绩效的提高。最后，我们的模型预测，向个体提供适量的错误信息可以抵消人类系统地低估数量的认知偏差，从而提高集体绩效。
