Organizational Networks as Collective Processing Systems: An Agent-Based Approach to Information Propagation — An Exploratory Study

Introduction: Complex adaptive systems — from neural circuits to organizational networks — share emergent collective patterns. In neuroscience, sensory populations integrate stimuli via distributed rules (collective sensing). From a computational perspective, this work explores how mathematical agent-based modeling (ABM) provides a testable methodological analogy for organizational networks. Organizations operate analogously: employees interact locally, and information propagation emerges from these interactions. Methods: Each member is modeled as an agent with specific interaction rules, moving beyond formal charts. Ties are modeled via informal daily interactions, capturing practical information sharing. As a case study, we examine a 'bridge' agent connecting isolated functional units, evaluating its impact on signal propagation efficiency and speed. Expected Results: The model formalizes the link between network topology — specifically structural positioning (central, peripheral, and bridge nodes) — and signal dynamics. We expect to demonstrate that system resilience and information transfer speed depend critically on these roles, validating that bridge node disruption causes a superior systemic impact on propagation rates than peripheral node removal. Conclusions: This computational approach models critical structural positions for information flow, providing a dynamic perspective on decentralized communication that traditional static network analyses miss.