An Agent-Based Model of Collective Decision-Making: How Information Sharing Strategies Scale with Information Overload

Abstract

Organizations rely on teams for complex decision-making. By bringing diverse information together and utilizing information sharing strategies, teams can make intelligent decisions. However, as organizations face increasing information overload, it has become unclear whether such strategies remain adequate or whether bounds on human rationality will prevail. We develop an agent-based model that simulates information sharing in teams, where critical information is distributed across its members. We tested how robust various information sharing strategies are to information overload and bounds on rationality in terms of the speed and accuracy of collective decision-making. Our results suggest distinct strategies depending on whether speed or accuracy is imperative and, more broadly, shed light on how intelligence is best attained in collective decision-making.