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A Resource-Constrained Optimal Control Model for Crackdown on Illicit Drug Markets

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Listed:
  • Baveja, A.
  • Feichtinger, G.
  • Hartl, R.F.
  • Haunschmied, J.L.
  • Kort, P.M.

    (Tilburg University, School of Economics and Management)

Abstract

In this paper we present a budget-constrained optimal control model aimed at finding the optimal enforcement profile for a street-level, illicit drug crackdown operation. The objective is defined as minimizing the number of dealers dealing at the end of the crackdown operation, using this as a surrogate measure of residual criminal activity. Analytical results show that optimal enforcement policy will invariably use the budget resources completely. Numerical analysis using realistic estimates of parameters shows that crackdowns normally lead to significant results within a matter of a week, and if they do not, it is likely that they will be offering very limited success even if pursued for a much longer duration. We also show that a ramp-up enforcement policy will be most effective in collapsing a drug market if the drug dealers are risk-seeking, and the policy of using maximum enforcement as early as possible is usually optimal in the case when the dealers are risk averse or risk neutral. The work then goes on to argue that the underlying model has some general characteristics that are both reasonable and intuitive, allowing possible applications in focussed, local enforcement operations on other similar illegal activities.
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Suggested Citation

  • Baveja, A. & Feichtinger, G. & Hartl, R.F. & Haunschmied, J.L. & Kort, P.M., 1999. "A Resource-Constrained Optimal Control Model for Crackdown on Illicit Drug Markets," Other publications TiSEM 8648e4a1-5cc5-4167-8aad-9, Tilburg University, School of Economics and Management.
    • Handle: RePEc:tiu:tiutis:8648e4a1-5cc5-4167-8aad-959a890139e3
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    1. Kort, P.M. & Feichtinger, G. & Hartl, R.F. & Haunschmied, J.L., 1998. "Optimal enforcement policies (crackdowns) on an illicit drug market," Other publications TiSEM c6de10bc-16b6-4f0c-830d-0, Tilburg University, School of Economics and Management.
    2. Baveja, Alok & Caulkins, Jonathan P. & Liu, Wensheng & Batta, Rajan & Karwan, Mark H., 1997. "When haste makes sense: Cracking down on street markets for illicit drugs," Socio-Economic Planning Sciences, Elsevier, vol. 31(4), pages 293-306, December.
    3. Edward J. Nell, 1994. "The Dynamics of the Drug Market," Challenge, Taylor & Francis Journals, vol. 37(2), pages 13-21, March.
    4. Baveja, Alok & Batta, Rajan & Caulkins, Jonathan P. & Karwan, Mark H., 1993. "Modeling the response of illicit drug markets to local enforcement," Socio-Economic Planning Sciences, Elsevier, vol. 27(2), pages 73-89, June.
    5. Caulkins, Jonathan P. & Larson, Richard C. & Rich, Thomas F., 1993. "Geography's impact on the success of focused local drug enforcement operations," Socio-Economic Planning Sciences, Elsevier, vol. 27(2), pages 119-130, June.
    6. Naik, Ashich V. & Baveja, Alok & Batta, Rajan & Caulkins, Jonathan P., 1996. "Scheduling crackdowns on illicit drug markets," European Journal of Operational Research, Elsevier, vol. 88(2), pages 231-250, January.
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    Cited by:

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    3. Gopalan, Ram & Narayan, Bindu, 2010. "Improving customer experience in tourism: A framework for stakeholder collaboration," Socio-Economic Planning Sciences, Elsevier, vol. 44(2), pages 100-112, June.
    4. Anton Bondarev & Alfred Greiner, 2018. "Technology lock-in with horizontal and vertical innovations through limited R&D spending," 4OR, Springer, vol. 16(1), pages 51-65, March.
    5. Redmond, Michael & Baveja, Alok, 2002. "A data-driven software tool for enabling cooperative information sharing among police departments," European Journal of Operational Research, Elsevier, vol. 141(3), pages 660-678, September.
    6. Baveja, Alok & Jamil, Mamnoon & Kushary, Debashis, 2004. "A sequential model for cracking down on street markets for illicit drugs," Socio-Economic Planning Sciences, Elsevier, vol. 38(1), pages 7-41, March.
    7. Yunker, James A., 2012. "Estimated optimal drug law enforcement expenditures based on U.S. annual data," Journal of Policy Modeling, Elsevier, vol. 34(3), pages 356-371.
    8. Kaya, C. Yalcin, 2004. "Time-optimal switching control for the US cocaine epidemic," Socio-Economic Planning Sciences, Elsevier, vol. 38(1), pages 57-72, March.

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    More about this item

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • K42 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - Illegal Behavior and the Enforcement of Law

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