Organizational Networks as Collective Processing Systems: An Agent-Based Modeling Framework for Structural Analysis

Complex adaptive systems, from neural circuits integrating sensory stimuli to organizational networks, share a fundamental property: collective patterns emerge from local interactions among individual units. In neuroscience, distributed sensing relies on agents integrating information through local rules, where no central controller is required. We propose that this framework is analogous to Organizational Network Analysis (ONA). This work explores how Agent-Based Modeling (ABM) can provide a robust methodology to simulate organizational dynamics, moving beyond descriptive network snapshots. In this conceptual framework, each individual is represented as an agent with communication ties that define the informal network structure, rather than the formal hierarchy. We investigate how an agent's position within the network — specifically regarding central nodes and bridges connecting disjoint functional areas — influences the propagation of information and organizational signaling. By applying an ABM approach, we aim to design a model capable of mapping how structural network features relate to collective organizational outcomes. This study establishes a methodological bridge between computational neuroscience principles and ONA, providing a platform to simulate decentralised coordination phenomena. Empirical validation using real-world organizational data is proposed for subsequent development.