An Exploratory Computational Study on the Effect of Emergent Leadership on Social and Task Cohesion

Leadership is a complex and dynamic phenomenon that has received a lot of attention from psychologists over the last 50 years, primarily due to its relationships with team effectiveness and performances. Depending on the group (e.g., size, relationships among members) and the context (e.g., solving a task under pressure), various styles of leadership could emerge. These styles can either be formally decided or manifest informally. Among the informal types of leadership, emergent leadership is one of the most studied. It is an emergent state that develops over time in a group and that interplays with other emergent states such as cohesion. Only a few computational studies focusing on predicting emergent leadership take advantage of the relationships with other phenomena to improve their models’ performances. These approaches, however, only apply to their models aimed at predicting emergent leadership. There is, to the best of our knowledge, no approach that integrates emergent leadership into computational models of cohesion. In this study, we take a first step towards bridging this gap by introducing 2 families of approaches inspired by Social Sciences’ insights to integrate emergent leadership into computational models of cohesion. The first family consists of amplifying the differences between leaders’ and followers’ features while the second one focuses on adding leadership representation directly into the computational model’s architecture. In particular, for each family, we describe 2 approaches that are applied to a Deep Neural Network model aimed at predicting the dynamics of cohesion across various tasks over time. This study explores whether and how applying our approaches improves the prediction of the dynamics of the Social and Task dimensions of cohesion. Therefore, the performance of a computational model of cohesion that does not integrate the interplay between cohesion and emergent leadership is compared with the same computational models that apply our approaches. Results show that approaches from both families significantly improved the prediction of the Task cohesion dynamics, confirming the benefits of integrating emergent leadership following Social Psychology’s insights to enforce computational models of cohesion at both feature and architecture levels.