Developing ABMs of organized crime networks supports law enforcement strategies but is often limited by scarce quantitative data. This challenge extends to other psychosocial contexts like mental health and social systems. While qualitative data from reports and interviews is more accessible, current ABM methodologies struggle to integrate both data types effectively. To address this, we propose FREIDA, a mixed-methods framework that combines qualitative and quantitative data to develop, train, and validate ABMs in data-sparse contexts. FREIDA's four-phase process includes data acquisition, conceptual modeling, computational implementation, and model assessment. Using Thematic Content Analysis (TCA), Expected System Behaviors (ESBs) are translated into Training Statements (TS) for calibration and Validation Statements (VS) for assessment. Iterative sensitivity analysis and uncertainty quantification refine the model's accuracy. We apply FREIDA to a case study of the Netherlands cocaine network, producing the Criminal Cocaine Replacement Model (CCRM) to simulate kingpin removal dynamics. FREIDA enables robust ABM development with limited data, aiding law enforcement decisions and resource allocation.

翻译：开发有组织犯罪网络的基于主体模型（ABM）有助于执法策略制定，但常受限于稀缺的定量数据。这一挑战同样存在于心理健康、社会系统等其他心理社会情境中。虽然来自报告和访谈的定性数据更易获取，但现有ABM方法难以有效整合两类数据。为此，我们提出FREIDA——一种混合方法框架，通过在数据稀疏情境中结合定性与定量数据来开发、训练和验证ABM。FREIDA的四阶段流程包括数据采集、概念建模、计算实现和模型评估。运用主题内容分析法（TCA），将预期系统行为（ESB）转化为用于校准的训练陈述（TS）和用于评估的验证陈述（VS）。通过迭代敏感性分析与不确定性量化来提升模型精度。我们将FREIDA应用于荷兰可卡因网络的案例研究，构建了犯罪可卡因替代模型（CCRM）以模拟头目清除动态。FREIDA能够在数据有限的情况下实现稳健的ABM开发，为执法决策与资源分配提供支持。