Framework for developing quantitative agent based models based on qualitative expert knowledge: an organised crime use-case

• URL: http://arxiv.org/abs/2308.00505v1
• Date: Fri, 21 Jul 2023 11:26:54 GMT
• Title: Framework for developing quantitative agent based models based on qualitative expert knowledge: an organised crime use-case
• Authors: Frederike Oetker, Vittorio Nespeca, Thijs Vis, Paul Duijn, Peter Sloot, Rick Quax
• Abstract summary: 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. We propose FREIDA ( Framework for Expert-Informed Data-driven Agent-based models) for this purpose.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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