FREIDA: A Framework for developing quantitative agent based models based on qualitative expert knowledge

Abstract:Agent Based Models (ABMs) often deal with systems where there is a lack of quantitative data or where quantitative data alone may be insufficient to fully capture the complexities of real-world systems. Expert knowledge and qualitative insights, such as those obtained through interviews, ethnographic research, historical accounts, or participatory workshops, are critical in constructing realistic behavioral rules, interactions, and decision-making processes within these models. However, there is a scarcity of systematic approaches that are able to incorporate both qualitative and quantitative data across the entire modeling cycle. To address this, we propose FREIDA, a systematic mixed-methods framework to develop, train, and validate ABMs, particularly in data-sparse contexts. The main technical innovation introduced within this framework is the extraction of what we call Expected System Behaviors (ESBs) from qualitative data, which are testable statements evaluated through model simulations. Divided into Calibration Statements (CS) for model calibration and Validation Statements (VS) for model validation, ESBs underpin a rigorous evaluation mechanism on the same footing as quantitative data. By structuring qualitative insights as explicit model constraints, FREIDA creates a transparent foundation for applying established modelling practices such as Sensitivity Analysis (SA) and Uncertainty Quantification (UQ), allowing modellers to assess parameter influence, model robustness, and remaining uncertainties in a systematic manner. Through this, qualitative insights can inform not only model specification but also parameterization, validation, and continuous improvement of model reliability and fitness for purpose, addressing a long-standing challenge in agent-based modeling. We illustrate the application of FREIDA through a case study of criminal cocaine networks in the Netherlands.

Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)

arXiv:2308.00505 [cs.AI]

(or arXiv:2308.00505v4 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2308.00505 Focus to learn more arXiv-issued DOI via DataCite

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