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促进社会融入并降低累犯率，但受限于资源容量，决策者常需借助风险评估工具进行优先级分配。尽管这些工具具有预测能力，但它们通常将风险视为静态的个体特征，忽略了风险随时间演化以及治疗决策通过社会互动影响结果的过程。本文构建了一个将重新犯罪风险建模为人-系统交互的新框架，将个体行为与系统层面动态及内生性社区反馈联系起来。通过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限制和监禁情境下的治疗分配策略。结果表明，不存在占主导地位的单一优先级分配策略，政策有效性取决于时间窗口和系统参数：当长期轨迹更重要时，优先考虑低风险个体效果更佳；而在短期内或当监禁导致监控周期缩短时，优先考虑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责任的社会技术系统进行评估，而非孤立的预测工具。