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Why Defeating Insurgencies Is Hard: The Effect of Intelligence in Counterinsurgency Operations---A Best-Case Scenario

Author

Listed:
  • Moshe Kress

    (Department of Operations Research, Naval Postgraduate School, Monterey, California 93943)

  • Roberto Szechtman

    (Department of Operations Research, Naval Postgraduate School, Monterey, California 93943)

Abstract

In insurgency situations, the government-organized force is confronted by a small guerrilla group that is dispersed in the general population with no or a very small signature. Effective counterinsurgency operations require good intelligence. Absent intelligence, not only might the insurgents escape unharmed and continue their violent actions, but collateral damage caused to the general population from poor targeting may generate adverse response against the government and create popular support for the insurgents, which may result in higher recruitment to the insurgency. We model the dynamic relations among intelligence, collateral casualties in the population, attrition, recruitment to the insurgency, and reinforcement to the government force. Even under best-case assumptions, we show that the government cannot totally eradicate the insurgency by force. The best it can do is contain it at a certain fixed level.

Suggested Citation

  • Moshe Kress & Roberto Szechtman, 2009. "Why Defeating Insurgencies Is Hard: The Effect of Intelligence in Counterinsurgency Operations---A Best-Case Scenario," Operations Research, INFORMS, vol. 57(3), pages 578-585, June.
    • Handle: RePEc:inm:oropre:v:57:y:2009:i:3:p:578-585
    DOI: 10.1287/opre.1090.0700
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Giorgio Gallo, 2013. "Conflict Theory, Complexity and Systems Approach," Systems Research and Behavioral Science, Wiley Blackwell, vol. 30(2), pages 156-175, March.
    2. Shawn C. McKay & Alok Chaturvedi & Douglas E. Adams, 2011. "A process for anticipating and shaping adversarial behavior," Naval Research Logistics (NRL), John Wiley & Sons, vol. 58(3), pages 255-280, April.
    3. Bertrand Crettez & Naila Hayek, 2014. "Terrorists’ Eradication Versus Perpetual Terror War," Journal of Optimization Theory and Applications, Springer, vol. 160(2), pages 679-702, February.
    4. Anna O. Pechenkina & D. Scott Bennett, 2017. "Violent and Non-Violent Strategies of Counterinsurgency," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(4), pages 1-11.
    5. Kolebaje, Olusola & Popoola, Oyebola & Khan, Muhammad Altaf & Oyewande, Oluwole, 2020. "An epidemiological approach to insurgent population modeling with the Atangana–Baleanu fractional derivative," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    6. Moshe Kress, 2020. "Lanchester Models for Irregular Warfare," Mathematics, MDPI, vol. 8(5), pages 1-14, May.
    7. Jaspersen, Johannes G. & Montibeller, Gilberto, 2020. "On the learning patterns and adaptive behavior of terrorist organizations," European Journal of Operational Research, Elsevier, vol. 282(1), pages 221-234.
    8. Kress, Moshe & Caulkins, Jonathan P. & Feichtinger, Gustav & Grass, Dieter & Seidl, Andrea, 2018. "Lanchester model for three-way combat," European Journal of Operational Research, Elsevier, vol. 264(1), pages 46-54.
    9. Edward G. Anderson, 2011. "A dynamic model of counterinsurgency policy including the effects of intelligence, public security, popular support, and insurgent experience," System Dynamics Review, System Dynamics Society, vol. 27(2), pages 111-141, April.
    10. Kjell Hausken & Jun Zhuang, 2011. "Governments' and Terrorists' Defense and Attack in a T -Period Game," Decision Analysis, INFORMS, vol. 8(1), pages 46-70, March.
    11. Shan, Xiaojun & Zhuang, Jun, 2013. "Hybrid defensive resource allocations in the face of partially strategic attackers in a sequential defender–attacker game," European Journal of Operational Research, Elsevier, vol. 228(1), pages 262-272.
    12. Seidl, Andrea & Kaplan, Edward H. & Caulkins, Jonathan P. & Wrzaczek, Stefan & Feichtinger, Gustav, 2016. "Optimal control of a terror queue," European Journal of Operational Research, Elsevier, vol. 248(1), pages 246-256.
    13. Manh D Hy & My A Vu & Nam H Nguyen & Anh N Ta & Dinh V Bui, 2020. "Optimization in an asymmetric Lanchester (n, 1) model," The Journal of Defense Modeling and Simulation, , vol. 17(1), pages 117-122, January.
    14. Bayón, L. & Fortuny Ayuso, P. & García-Nieto, P.J. & Grau, J.M. & Ruiz, M.M., 2019. "Optimal control of counter-terrorism tactics," Applied Mathematics and Computation, Elsevier, vol. 347(C), pages 477-491.
    15. Bagchi, Aniruddha & Paul, Jomon A., 2021. "National security vs. human rights: A game theoretic analysis of the tension between these objectives," European Journal of Operational Research, Elsevier, vol. 290(2), pages 790-805.
    16. Atkinson, Michael P. & Kress, Moshe & Szechtman, Roberto, 2012. "Carrots, sticks and fog during insurgencies," Mathematical Social Sciences, Elsevier, vol. 64(3), pages 203-213.
    17. Edward H. Kaplan & Moshe Kress & Roberto Szechtman, 2010. "Confronting Entrenched Insurgents," Operations Research, INFORMS, vol. 58(2), pages 329-341, April.

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