The concept of deterrence, using fear of punishment to encourage compliant behavior, is widely discussed. However, deterrence often places an emphasis on the economic side of compliance while neglecting other possibly crucial factors, as is argued by the literature. Psychological factors, notably the personal norm and the social norm, often appear to play important roles in the decision to comply or not. The personal norm describes an individual’s attitude and moral stance toward specific behaviors, such as compliance. On the other hand, the social norm revolves around perceptions of others’
behavior and opinions within one’s social network. Hence, the influence of these factors is researched within different populations, and different environments, some enabling and some impeding compliance. A new framework to encompass all these elements is explored with Agent-Based Modeling and applied to a case study in collaboration with the ILT (Inspectie Leefomgeving en Transport). Findings included the lack of a social influence on compliance, whenever the personal norm was high and the necessity to be able to comply in order for deterrence to show an effect. For future research, suggestions are done to improve the framework and to explore additional aspects.

Law enforcement occurs in a complex environment that contains a variety of actors that interact with one another. These interactions create emerging collective behavior over time. For example, inspectors will try to influence non-compliant actors to become compliant, while inspectees may comply or thwart inspections. Inspection agencies such as the Inspectie Leefomgeving en Transport (ILT) evaluate inspectees’ adherence to regulation and aim to boost compliance within an industry. However, they have limited resources and a wide range of potential societal challenges to address. They face an action dilemma, having to decide on what actions to take without full knowledge of whether their actions lead to higher compliance or improved social outcomes. Previous studies of the inspection environment rely on behavioral theories to investigate the underlying motivations of inspectees’ behavior. However, these theories presume inspectees’ motivations and characterize them homogeneously, often assuming they have perfect rationality. This leads to an inaccurate depiction of inspectees, reducing them to one-dimensional actors when in reality, their behavior is motivated by multiple factors and can be idiosyncratic. Data science techniques provide the opportunity to understand behavioral phenomena with data, leveraging datasets to identify statistical patterns in behavior to help inspectorates make decisions within a degree of certainty. A particular modeling technique that focuses on representing empirical data without assuming behavioral motivations is phenomenological modeling. Coupled with agent-based modeling (ABM), phenomenological modeling allows the researcher to simulate possible outcomes of observed behavior before the underlying motivations are understood. This provides insight into the macro-level behavior produced by micro-level interactions within the complex inspection environment...