GitHub repository: shun-cao/ABM-of-collective-leadership
Primary language: Python
Description: This project is to use simulation approaches to study collective leadership and organizational effectiveness. Here we create an agent-based model to test our notions about collective leadership and collective planning and their impact on the performance and effectiveness of leaders and collectives. While we can not fully validate the models in one set of studies, this approach allows us to test significant portions of the models, eliminates single-study and common-method bias potential, and provides cross-method validation of results. 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 the long run. These results match the empirical literature and offer a mechanistic, operationalized description of the collective leadership processes.