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.

翻译：监禁分流治疗项目旨在改善社会再融入并降低再犯率，但有限的项目容量迫使政策制定者必须做出优先排序决策，而这些决策通常依赖于风险评估工具。尽管具有预测性，但这些工具通常将风险视为静态的个体属性，忽略了风险如何随时间演变，以及治疗决策如何通过社会互动影响结果。本文提出一个新的框架，将再犯罪风险建模为人-系统交互，将个体行为与系统层面的动态性及内生的社区反馈联系起来。通过使用基于美国缓刑数据校准的智能体模拟，我们评估了不同容量约束和监禁环境下的治疗分配策略。结果表明，不存在单一的主导性优先策略。相反，策略的有效性取决于时间窗口和系统参数：当长期轨迹至关重要时，优先治疗低风险个体表现更佳；而在短期或当监禁导致监控期缩短的情况下，优先治疗高风险个体则更为有效。这些发现强调，需要将基于风险的决策系统视为具有长期问责性的社会技术系统进行评估，而非孤立的预测工具。