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Policing Australia’s ‘heroin drought’: using an agent-based model to simulate alternative outcomes

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Abstract

Our paper examines how street-level drug markets adapt to a macro-level disruption to the supply of heroin, under three experimental conditions of street-level drug law enforcement: random patrol, hot-spot policing and problem-orientated policing. We utilize an agent-based model to explore the relative impact of abstractions of these three law enforcement strategies after simulating an ‘external shock’ to the supply of heroin to the street-level drug market. We use 3 years of data, which include the period of the ‘heroin drought’ in Melbourne (Australia) that commenced in late 2000 and early 2001, to measure changes in a selected range of crime and harm indicators under the three policing conditions. Our results show that macro-level disruptions to drug supply have a limited impact on street-level market dynamics when there is a ready replacement drug. By contrast, street-level police interventions are shown to vary in their capacity to alter drug market dynamics. Importantly, our laboratory abstraction of problem-orientated policing is shown to be the optimal strategy to disrupt street-level injecting-drug markets, reduce crimes and minimize harm, regardless of the type of drug being supplied to the market.

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Acknowledgements

This paper was supported by a grant from the Colonial Foundation Trust to the National Drug and Alcohol Research Centre (with sub-contracts to Griffith University, Australian National University and Turning Point Drug and Alcohol Centre). The authors are part of the Drug Policy Modelling Program (DPMP) team and wish to thank Gabriele Bammer, Jonathan Caulkins, Paul Dietze, Tim Moore and Peter Reuter for their contributions to this paper.

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Correspondence to Anne Dray.

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Dray, A., Mazerolle, L., Perez, P. et al. Policing Australia’s ‘heroin drought’: using an agent-based model to simulate alternative outcomes. J Exp Criminol 4, 267–287 (2008). https://doi.org/10.1007/s11292-008-9057-1

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