Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02142;
Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139.
Proc Natl Acad Sci U S A. 2020 May 26;117(21):11379-11386. doi: 10.1073/pnas.1917687117. Epub 2020 May 11.
Social networks continuously change as new ties are created and existing ones fade. It is widely acknowledged that our social embedding has a substantial impact on what information we receive and how we form beliefs and make decisions. However, most empirical studies on the role of social networks in collective intelligence have overlooked the dynamic nature of social networks and its role in fostering adaptive collective intelligence. Therefore, little is known about how groups of individuals dynamically modify their local connections and, accordingly, the topology of the network of interactions to respond to changing environmental conditions. In this paper, we address this question through a series of behavioral experiments and supporting simulations. Our results reveal that, in the presence of plasticity and feedback, social networks can adapt to biased and changing information environments and produce collective estimates that are more accurate than their best-performing member. To explain these results, we explore two mechanisms: 1) a global-adaptation mechanism where the structural connectivity of the network itself changes such that it amplifies the estimates of high-performing members within the group (i.e., the network "edges" encode the computation); and 2) a local-adaptation mechanism where accurate individuals are more resistant to social influence (i.e., adjustments to the attributes of the "node" in the network); therefore, their initial belief is disproportionately weighted in the collective estimate. Our findings substantiate the role of social-network plasticity and feedback as key adaptive mechanisms for refining individual and collective judgments.
社交网络随着新关系的建立和旧关系的淡化而不断变化。人们普遍认为，我们的社交嵌入对我们接收到的信息以及我们如何形成信念和做出决策有重大影响。然而，大多数关于社交网络在集体智慧中的作用的实证研究都忽略了社交网络的动态性质及其在培养适应性集体智慧方面的作用。因此，对于个人群体如何动态地修改其局部连接，以及相应地，如何响应变化的环境条件来修改网络的交互拓扑结构，我们知之甚少。在本文中，我们通过一系列行为实验和支持性模拟来解决这个问题。我们的结果表明，在存在可塑性和反馈的情况下，社交网络可以适应有偏差和变化的信息环境，并产生比其表现最佳成员更准确的集体估计。为了解释这些结果，我们探索了两种机制：1）全局适应机制，其中网络本身的结构连接发生变化，从而放大了群体内表现最佳成员的估计值（即，网络“边缘”编码计算）；2）局部适应机制，其中准确的个体更能抵抗社交影响（即，对网络“节点”属性进行调整）；因此，他们的初始信念在集体估计中被不成比例地加权。我们的发现证实了社交网络可塑性和反馈作为完善个人和集体判断的关键适应机制的作用。
