Moussaïd Mehdi, Noriega Campero Alejandro, Almaatouq Abdullah
Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany.
Massachusetts Institute of Technology, Cambridge, MA, United States of America.
PLoS One. 2018 Jan 16;13(1):e0190541. doi: 10.1371/journal.pone.0190541. eCollection 2018.
In many domains of life, business and management, numerous problems are addressed by small groups of individuals engaged in face-to-face discussions. While research in social psychology has a long history of studying the determinants of small group performances, the internal dynamics that govern a group discussion are not yet well understood. Here, we rely on computational methods based on network analyses and opinion dynamics to describe how individuals influence each other during a group discussion. We consider the situation in which a small group of three individuals engages in a discussion to solve an estimation task. We propose a model describing how group members gradually influence each other and revise their judgments over the course of the discussion. The main component of the model is an influence network-a weighted, directed graph that determines the extent to which individuals influence each other during the discussion. In simulations, we first study the optimal structure of the influence network that yields the best group performances. Then, we implement a social learning process by which individuals adapt to the past performance of their peers, thereby affecting the structure of the influence network in the long run. We explore the mechanisms underlying the emergence of efficient or maladaptive networks and show that the influence network can converge towards the optimal one, but only when individuals exhibit a social discounting bias by downgrading the relative performances of their peers. Finally, we find a late-speaker effect, whereby individuals who speak later in the discussion are perceived more positively in the long run and are thus more influential. The numerous predictions of the model can serve as a basis for future experiments, and this work opens research on small group discussion to computational social sciences.
在生活、商业和管理的许多领域，大量问题是由进行面对面讨论的小群体来解决的。虽然社会心理学领域对小群体绩效的决定因素进行了长期研究，但支配群体讨论的内部动态机制仍未得到充分理解。在此，我们依靠基于网络分析和意见动态的计算方法来描述个体在群体讨论过程中是如何相互影响的。我们考虑一个由三个人组成的小群体进行讨论以解决一项估计任务的情况。我们提出了一个模型，描述群体成员在讨论过程中如何逐渐相互影响并修正他们的判断。该模型的主要组成部分是一个影响网络——一个加权有向图，它决定了个体在讨论过程中相互影响的程度。在模拟中，我们首先研究能产生最佳群体绩效的影响网络的最优结构。然后，我们实施一个社会学习过程，个体据此适应其同伴过去的表现，从而从长远来看影响影响网络的结构。我们探究高效或适应不良网络出现的潜在机制，并表明影响网络能够趋向于最优网络，但前提是个体通过降低其同伴的相对表现来表现出社会折扣偏差。最后，我们发现了一种后发言者效应，即那些在讨论后期发言的个体从长远来看会被更积极地看待，因此更具影响力。该模型的众多预测可为未来实验提供基础，并且这项工作为计算社会科学开启了对小群体讨论的研究。
