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.

为了对犯罪网络进行建模以供执法机构使用，需要将有限的数据转化为经过验证的基于代理的模型。我们提出了FREIDA（专家驱动的数据驱动代理模型框架），其中包括将定性数据转化为定量规则的模型过程。通过以荷兰的犯罪可卡因网络为例，展示了FREIDA方法论。该研究结果表明需要灵活的参数以及进行额外的案例模拟。

基于定性专家知识开发定量智能体模型的框架：以有组织犯罪为例