Agent-based modelling as a research tool for criminological research

Computational modelling techniques, originating from fields like Computer Science and Artificial Intelligence, may be beneficial for criminological research. Because of their formal nature, computational models can be processed by machines that operate on them, for example for the purpose of simulation. As a consequence, these techniques may help gain insights that lacked based of purely informal theories. A well-known example of such a technique, which has become widely applied within criminology, is called agent-based modelling. Agent-based modelling (ABM) is a computational method that enables a researcher to create, analyse and experiment with models composed of agents, i.e., autonomous pieces of software that interact within a computational environment (Gilbert, 2008). In the current article this technique will be explored in depth. First, I will give a description of the technique and present the architecture of an ABM. Subsequently, I will apply the technique to a simple toy example in the context of a simulation model of the bystander effect, to demonstrate the possibilities of the approach. I will discuss some pros and cons of the approach and present related work to help appreciate the benefits of applying ABM to different criminological research questions. Hopefully, this will provide readers with the necessary knowledge to consider the use of ABM in their own research.