An Agent-based Model of Interactional Theory of Delinquency

While agent-based modeling is growing within many areas (e.g., geography, ecology) one area that has not seen many applications is that of social work. For example how can we explore what may cause an increase or a decrease in delinquency and recidivism within a given population? To this end, JoAnn Lee and myself recently had a paper published in the  Journal of Artificial Societies and Social Simulation entitled "Youth and their Artificial Social Environmental Risk and Promotive Scores (Ya-TASERPS): An Agent-based Model of Interactional Theory of Delinquency." In the paper we explore how one can test the interactional theory of delinquency via and agent-based model and as such provides a means of increasing our understanding of delinquency.

If this sounds of interest, below we provide the abstract to the paper and some of the figures (including the graphical user interface of the model, the conceptual model of interactions and how the model actually works. At the bottom of the post you can find the full citation of the paper and a link to it. The model itself was created in NetLogo and a detailed Overview, Design concepts, and Details plus Decision (ODD + D) protocol document is available at: https://bit.ly/YaTASERPS. We provide this documentation in order to provide more details about the model and aid others in replicating the results presented in the paper along with extending the model if so desired. 

Abstract: Risk assessments are designed to measure cumulative risk and promotive factors for delinquency and recidivism, and are used by criminal and juvenile justice systems to inform sanctions and interventions. Yet, these risk assessments tend to focus on individual risk and often fail to capture each individual’s environmental risk. This paper presents an agent-based model (ABM) which explores the interaction of individual and environmental risk on the youth. The ABM is based on an interactional theory of delinquency and moves beyond more traditional statistical approaches used to study delinquency that tend to rely on point-in-time measures, and to focus on exploring the dynamics and processes that evolve from interactions between agents (i.e., youths) and their environments. Our ABM simulates a youth’s day, where they spend time in schools, their neighborhoods, and families. The youth has proclivities for engaging in prosocial or antisocial behaviors, and their environments have likelihoods of presenting prosocial or antisocial opportunities. Results from systematically adjusting family, school, and neighborhood risk and promotive levels suggest that environmental risk and promotive factors play a role in shaping youth outcomes. As such the model shows promise for increasing our understanding of delinquency. 

Keywords: Agent-based Modeling, Antisocial Behaviors, Delinquency, Risk Factors, Youth, Social Work.

Graphical user interface of the model at model initialization. The model environment (right) shows youths (grey) at home (blue) and their neighborhood (green) and their school (brown).

Conceptual model of interactions.

Flow diagram of the overall model logic during the simulation.

 Full reference:

Lee, J. and Crooks A.T. (2021), Youth and their Artificial Social Environmental Risk and Promotive Scores (Ya-TASERPS): An Agent-Based Model of Interactional Theory of Delinquency, Journal of Artificial Societies and Social Simulation. 24 (4) 2. Available at: https://www.jasss.org/24/4/2.html (pdf)