# Licence to Simulate: When Agent-Based Models Are More Fiction than Function +
## 2 Background
In the case of learning, theories of learning become important and machine learning algorithms may be leveraged for recognising patterns in data [34]. Modelling agent relationships and interactions involves defining how agents are connected (i.e., the model's topology) and specifying the dynamics that govern their interactions [29]. In agent-based systems, by nature decentralised, each agent typically has access only to local information, which is acquired through interactions with neighbouring agents. An agent's neighbourhood can be represented as a network of nodes (agents) and links (relationships), and agents within a model may interact according to one or multiple topological structures. In ABMs, the environment plays a crucial role as it may constrain agent actions in contending for space, acquiring resources, and so on [30]. The environment may simply provide the agent with information about its spatial location with respect to the other agents or with a richer set of geographic information. However, more sophisticated environmental models can be used to model the agents' environment to provide point location-specific data [31]. It is worth highlighting here that many of these applications involve hybrid simulation, that is a modelling approach that combines two or more techniques [35]. Since its origins, agent-based modelling has seen a variety of applications over a wide range of disciplines from physical sciences to social science and from biology to business and finance. ABMs have had many applications in the SC domain, and several authors have tried to systematise these applications in appropriate literature reviews. As ref. [36] points out in a synthetic, but very optimistic, way, the next generation of ABMs in SCM will be all of the following: (i) distributed; (ii) dynamic; (iii) intelligent; (iv) integrated; (v) responsive; (vi) reactive; (vii) cooperative; (viii) interactive; (ix) anytime; (x) complete; (xi) reconfigurable; (xii) general; (xiii) adaptable; and (xiv) backwards compatible. Ref. [11] introduced a service-orientated framework for collaborative SCs, and it included practical applications and case studies that demonstrated the effectiveness of the proposed framework. Similarly, ref. [32] proposed a framework integrating ABS with simulation-based optimisation, thus allowing for a more dynamic and realistic representation of SC entities. This framework also emphasised how individual agents pursue their local objectives while also interacting with one another to achieve broader, holistic goals. Furthermore, ref. [37] provided a state-of-the-art survey on ABS for risk management in SCs, and this classification helps in understanding the various approaches and methodologies used in the field. The review in [33] offered a multi-level classification of ABS applications in logistics and SC research. It introduced a multi-level classification framework to systematically categorise the reviewed publications, and it helped to understand the various applications and issues related to ABS. Also, the research highlighted the different behaviours and capabilities of agents in ABS, such as their ability to learn and adapt, and it also assessed the maturity of various ABS applications, categorising them from conceptual proposals to field experiments. Lastly, other reviews investigated the contribution of ABS to SCs in various industry sectors, such as the relevant case of agri-food [14;38]. As for this literature review, to the best of the authors' knowledge, no previous study has explicitly addressed the issue of evaluating the proper use of the ABS approach, especially in the SC domain.