# Licence to Simulate: When Agent-Based Models Are More Fiction than Function +
## 1 Introduction  
In today's highly competitive and globalised economy, the effective design and efficient management of supply chains (SCs) have emerged as critical determinants of organisational success [1]. As markets grow increasingly interconnected and customer expectationscontinue to rise, the ability to effectively coordinate and optimise supply chain operations has emerged as a critical strategic advantage [2; 3]. Also, over recent decades, SCs have undergone significant transformations, becoming more expansive and complex [4]. These changes have been driven by various factors, including the growing emphasis on environmental sustainability, which gained momentum in the last decade [5], and the recent wave of disruptions, ranging from the global pandemic to geopolitical tensions, which have underscored the fragility and interdependence of modern SCs [6; 7]. To address these challenges, a wide array of tools and methodologies have been developed to support the design, planning, and optimisation of SCs. These include mathematical programming, heuristic and metaheuristic algorithms, digital twins, and machine learning techniques [8]. Among the available tools, simulation plays a central role and has proved to be particularly powerful, enabling researchers and practitioners to model dynamic behaviours, test scenarios, and evaluate performance under uncertainty without disrupting real-world operations [9]. Specifically, over the past decade, agent-based simulation (ABS) has been steadily gaining traction. This simulation paradigm is based on a collection of autonomous agents (such as suppliers, manufacturers, or customers) that operate independently, pursuing local objectives, but that can interact dynamically with other agents and the environment based on predefined rules [9]. This decentralised modelling approach is particularly well-suited for systems characterised by heterogeneity, distributed control, and non-linear interactions and it enables the emergence of complex system behaviours from simple interaction rules. For these reasons, the choice to simulate SCs, whose evolution and trend over time can be attributed to several different factors, is a particularly compelling and effective one [10]. However, in some cases, the models developed using the ABS paradigm do not explicitly require such an approach, as the dynamics of the simulated system do not necessitate the definition of active agents and could be more appropriately handled using simpler event-based systems or system dynamics models. In other words, while many models employ agents, it is not always clear whether the agent-based approach is necessary or merely a convenient design choice. Although this concern, namely, the potential misuse of agent-based models (ABMs), has been frequently raised in the literature (see for example [11; 12; 13]), there are no studies that have precisely quantified how frequently this issue occurs, especially in the SC domain. Even if the literature offers several comprehensive reviews and frameworks for ABS in SC contexts, none of them has systematically investigated whether and to what extent the use of ABS is essential to accurately represent the dynamics of a given SC model. It is therefore evident that a significant gap remains open in the literature in this regard. To address this gap, the present paper pursues the following objectives: (i) to propose a set of guidelines for classifying the ABMs found in the literature; (ii) to categorise these models into two clusters, which we refer to as _Green Flag_ and _Red Flag_, based on the necessity of using ABS; and (iii) to assess the extent to which existing ABMs fall into these categories, thereby evaluating whether ABS was essential or optional in each case. The remainder of the paper is organised as follows. Section 2 provides the necessary background on simulation approaches within SC modelling. Section 3 outlines the key elements of our methodology, and the criteria used for classification.