The Dynamic and Endogenous Behavior of Re-Offense Risk: An Agent-Based Simulation Study of Treatment Allocation in Incarceration Diversion Programs

TL;DR

This study models re-offense risk as a dynamic, system-influenced process using agent-based simulation to evaluate how different treatment prioritization policies impact recidivism outcomes over time.

Contribution

It introduces a novel framework linking individual reoffense risk with social system dynamics, emphasizing the importance of temporal factors in treatment prioritization policies.

Findings

No single policy dominates across all scenarios.

Long-term effectiveness favors prioritizing low-risk individuals.

Short-term or incarceration-shortening policies benefit high-risk prioritization.

Abstract

Incarceration-diversion treatment programs aim to improve societal reintegration and reduce recidivism, but limited capacity forces policymakers to make prioritization decisions that often rely on risk assessment tools. While predictive, these tools typically treat risk as a static, individual attribute, which overlooks how risk evolves over time and how treatment decisions shape outcomes through social interactions. In this paper, we develop a new framework that models reoffending risk as a human-system interaction, linking individual behavior with system-level dynamics and endogenous community feedback. Using an agent-based simulation calibrated to U.S. probation data, we evaluate treatment allocation policies under different capacity constraints and incarceration settings. Our results show that no single prioritization policy dominates. Instead, policy effectiveness depends on temporal windows and system parameters: prioritizing low-risk individuals performs better when long-term trajectories matter, while prioritizing high-risk individuals becomes more effective in the short term or when incarceration leads to shorter monitoring periods. These findings highlight the need to evaluate risk-based decision systems as sociotechnical systems with long-term accountability, rather than as isolated predictive tools.