License to Simulate: When Agent-Based Models Are More Fiction Than Function

Agent-based models (ABMs) are increasingly used to study supply chain (SC) systems due to their capacity to capture decentralised behaviour, heterogeneity, and emergent dynamics. However, the mere use of agent-based simulation platforms does not necessarily imply that the models fully exploit the agent-based paradigm. This study investigates the extent to which ABMs applied to SC contexts make meaningful use of agents, based on behavioural, interactional, and systemic criteria. A systematic and thorough review of 58 academic contributions was conducted, focusing on six analytical parameters: context and objectives, justification for adopting ABMs, simulated entities, nature of interactions, system dynamics, and final classification. A central distinction is introduced between the so-called Green and Red Flag models, where the former are coherent implementations of agent-based simulation, while the latter fall short of its essential features. Further nuance is provided by two subtypes of Green Flag models: those featuring cognitively evolved agents and those based on simple interacting entities that give rise to emergent dynamics. Since the results reveal that many SC models labelled as “agent-based” lack key characteristics (such as inter-agent influence or system-level evolution) to justify the use of ABM, the pa-per provides conceptual tools to aid in distinguish between representational and functionally justified agent-based approaches. In addition, it offers both a theoretical framework and a practical evaluation guide to support the development of future models and to foster critical analysis within the field.