# Licence to Simulate: When Agent-Based Models Are More Fiction than Function +
## 2 Background  
Global and complex SCs, with both their strengths and weaknesses, are the network that drive economic systems [14; 15]. The traditional economic focus of supply chain management (SCM) has broadened to also encompass environmental and social sustainability considerations within inter-organisational business systems [16]. Environmental influences and social factors modify SC systems, which in turn \"react\" or \"adapt\" to these socio-environmental changes. Therefore, the overall sustainability of economic systems ends up being dependent upon the complex interplay of SCs on several dimensions from the microscopic (intra-organisational) to the mesoscopic (inter-organisational) and macroscopic (industry--or sector wide aggregate) levels [17]. These complex interactions have long led researchers to look at supply networks as complex and adaptive systems [18]. For these reasons, simulation methods offer several advantages over other formal modelling approaches, such as multi-criteria decision making and mathematical programming, as effective and practical analytical tools for supply chain design and optimisation [19; 20]. Several simulation approaches are available, each with distinct characteristics and preferred applications. Monte Carlo simulation is widely used for probabilistic analysis and risk assessment by generating random samples to model uncertainty [21]. Discrete event simulation (DES) focuses on systems where state changes occur at discrete points in time, making it suitable for modelling queuing systems and logistics operations [22]. System dynamics (SD) captures feedback loops and time delays in complex systems, often at an aggregate level [23]. In SCM research, these methods have been used to study different problems, such as identification and control of inventory levels, cost management, SC network design and facility location, and, notably, coordination mechanisms in the context of SC dynamics [24]. Readers can refer to [25] for a general review on discrete event simulation and system dynamics implementations in the SC domain, and to [26; 27] for a comprehensive analysis of the development of multiagent systems in SC applications. However, when modelling an SC as a collection of autonomous members that interact with and influence each other, learn from their experiences, and adapt their behaviours to be better suited to their environment, and when the objective is to study patterns, structures, and behaviours emerging through the agent interactions, ABS (often also labelled as multiagent system approach or multiagent simulation) is a primary modelling solution to cope with this kind of complexity [28]. In ABS, in fact, the core modelling element is the agent and its associated behaviours, which influence not only its own actions but also those of other agents and the environment [29; 30]. More precisely, indeed, a typical agent-based model consists of three elements [31]: (i) a set of agents with their attributes and behaviours; (ii) a set of agent relationships and methods of interaction (how and with whom agents interact); and (iii) the agents' environment. Even if there is no universally accepted definition of agents in the literature, the property of _autonomy_ appears to be their most important defining feature. Therefore, agents should exhibit specific behaviours and be equipped with dedicated decision-making mechanisms, enabling them to make autonomous decisions in response to the situations they encounter, without relying on external control [31]. In that regard, agents could be active, with their actions aiming to achieve their internal goals, or passive, reacting to other agents and the environment. Agents' actions may be a direct intervention, a communication with other agents, or further reasoning. Moreover, agents possess mechanisms that adapt their behaviours, at a single and collective level, in response to accumulated experiences or learning by leveraging some form of memory [32; 33].