An Agent-Based Model for Assessing the Resilience of Drug Trafficking Organizations to Law Enforcement Interventions

Deborah Manzi () and Francesco Calderoni ()
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Francesco Calderoni: https://docenti.unicatt.it/ppd2/it/docenti/18107/francesco-calderoni/profilo

Journal of Artificial Societies and Social Simulation, 2024, vol. 27, issue 3, 3

Abstract: The resilience and resistance of criminal networks, particularly drug trafficking organizations, remain crucial issues in contemporary society. Existing studies have unrealistically modelled law enforcement interventions and fail to capture the complexity of the adaptations of criminal networks. This study introduces MADTOR, the first agent-based model that examines the responses of drug trafficking organizations to different types of law enforcement interventions. MADTOR addresses previous research gaps by enabling more realistic simulations of law enforcement interventions, modeling adaptations by organizations based on real-world operations, and allowing comparisons of different interventions. To demonstrate the possible applications of MADTOR, we assess the impact of arresting varying proportions of members on the resilience of drug trafficking organizations. Our results reveal the disruptive impact of arresting even a few members, and a non-linear relationship between the share of arrested members and disruptive impact, with diminishing returns as the proportion increases. Surviving organizations face increasing recovery difficulties as more members are arrested. These findings contribute to the development of strategies for effective interventions against drug trafficking.

Keywords: Organized Crime; Criminal Network; Resilience; Drug Trafficking; Disruption; Agent-Based Modeling; Asset Recovery (search for similar items in EconPapers)
Date: 2024-06-30
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Persistent link: https://EconPapers.repec.org/RePEc:jas:jasssj:2023-101-3

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