In order to model criminal networks for law enforcement purposes, a limited supply of data needs to be translated into validated agent-based models. What is missing in current criminological modelling is a systematic and transparent framework for modelers and domain experts that establishes a modelling procedure for computational criminal modelling that includes translating qualitative data into quantitative rules. For this, we propose FREIDA (Framework for Expert-Informed Data-driven Agent-based models). Throughout the paper, the criminal cocaine replacement model (CCRM) will be used as an example case to demonstrate the FREIDA methodology. For the CCRM, a criminal cocaine network in the Netherlands is being modelled where the kingpin node is being removed, the goal being for the remaining agents to reorganize after the disruption and return the network into a stable state. Qualitative data sources such as case files, literature and interviews are translated into empirical laws, and combined with the quantitative sources such as databases form the three dimensions (environment, agents, behaviour) of a networked ABM. Four case files are being modelled and scored both for training as well as for validation scores to transition to the computational model and application phase respectively. In the last phase, iterative sensitivity analysis, uncertainty quantification and scenario testing eventually lead to a robust model that can help law enforcement plan their intervention strategies. Results indicate the need for flexible parameters as well as additional case file simulations to be performed.

翻译：为执法目的建模犯罪网络时，需将有限的数据转化为经过验证的智能体模型。当前犯罪学建模缺乏系统透明的框架，使建模者与领域专家能够建立包含将定性数据转化为定量规则的计算犯罪建模流程。为此，我们提出FREIDA（专家知情数据驱动智能体模型框架）。本文以可卡因犯罪网络重组模型（CCRM）为例，展示FREIDA方法论。在CCRM中，对荷兰某可卡因犯罪网络进行建模，模拟其核心节点被移除后剩余智能体重组网络至稳定状态的过程。通过将案件档案、文献和访谈等定性数据源转化为经验法则，与数据库等定量数据源结合，构成网络化ABM的三个维度（环境、智能体、行为）。对四份案件档案进行建模与评分，分别用于训练验证与计算模型转换及实际应用阶段。在最后阶段，通过迭代敏感性分析、不确定性量化及情景测试，最终形成有助于执法机构规划干预策略的稳健模型。结果表明需引入灵活参数并进行更多案例仿真。