Development of Emergent Leadership Measurement: Implications for Human-Machine Teams

Abstract

Emergent leadership refers to the dynamic by which, when there is no appointed leader in a group, one or more members assume leadership behaviors. Understanding emergent leadership in task-oriented human-machine teams is critical to optimize the role and input of machine agents. We find, however, a dearth of measures of emergent leadership to guide the development of machine agents. Here we describe the initial development of peer-report and natural language processing (NLP) -derived measurement techniques for indexing emergent leadership in a team context, rooted in the leaderplex model (Denison et al. 1995; Quinn 1984); we take a behavioral approach to indexing emergent leadership which emphasizes the diverse functions of leaders in the team context. We describe initial evidence of validity, areas of further exploration, and implications for human-machine teams. Overall, we find good concordance between peer-report measures of leadership behaviors and peer-report identification of emergent leaders, as well as with initial NLP behavioral marker extractions. Our mixed-method approach presents a first step in developing language-derived computational methods to enhance machine agent artificial social intelligence and theory of mind, ultimately improving their effectiveness in human-machine teams.