An Agent-Based Model of Leader Emergence and Leadership Perception within a Collective

Abstract

Effective teamwork in an initially leaderless group requires a high level of collective leadership emerging from dynamic interactions among group members. Leader emergence is a crucial topic in collective leadership, yet it is challenging to investigate as the problem context is typically highly complex and dynamic. Here, we explore leadership emergence and leadership perception by means of computational simulations whose assumptions and parameters were informed by empirical research and human-subject experiments. Our agent-based model describes the process of group planning. Each agent is assigned with three key attributes: talkativeness, intelligence, and credibility. An agent can propose a suggestion to modify the group plan as a speaker or respond and evaluate others’ suggestions and leadership as a listener. Simulation results suggested that agents with high values of talkativeness, intelligence, and credibility tended to be perceived as leaders by their peers. Results also showed that talkativeness may be the most significant and instantaneous predictor for leader emergence of the three investigated attributes: talkativeness, intelligence, and credibility. In terms of group performance, smaller groups may outperform larger groups regarding their problem-solving ability in the beginning, but their performance tends to be of no significant difference in a long run. These results match the empirical literature and offer a mechanistic, operationalized description of the collective leadership processes.

COVID-19 Transmission Risks Assessment using Agent-Based Weighted Clustering Approach

“…The agent-based model applies for cluster planning and each agent is allocated for three key attributes like intelligence, talkativeness, and credibility. The problem-solving ability is better for small groups compared to large groups [9]. The fully computable model is utilized for two-state model which is used to identify the healthy or infectious people permitting for in environment simulation and risk assessment [10].…”

Idea selection in design teams: a computational framework and insights in the presence of influencers

“…The perceived expert status is often independent of individuals’ knowledge as participants in Sinaceur et al (2010) had similar information in the experiment. Some researchers have shown in their work that assigned leaders or hierarchy in teams could be beneficial to idea selection because it reduces the tendency in individuals to choose their own versus others’ ideas (Keum & See 2017; Cao et al 2020). The other social factor that is considered for this work and could affect idea selection is the majority effect.…”

“…Despite its limitations, the ABM approach has been used in a variety of different fields, including energy management (Zhao, Suryanarayanan & Simões 2013), stock trading (Luo, Liu & Davis 2002) and spatial planning (Ligtenberg et al 2004) as is it an efficient way to represent complex phenomena. However, in the field of studying design teams, agent-based models have been used to model collaborative behaviour as they are efficient in modelling dynamic systems, interactions and features like learning and adaptation (Fernandes et al 2017; Lapp, Jablokow & McComb 2019; Cao et al 2020).…”

Towards Social Capital in a Network Organization: A Conceptual Model and an Empirical Approach

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