Abstract
Counterinsurgencies are a type of violent struggle between state and non-state actors in which one group attempts to gain or maintain influence over a certain portion of the population. When an insurgency (i.e., non-state actor) challenges a host nation (i.e., state actor), often an external counterinsurgent force intervenes. While researchers have categorized insurgencies with social science techniques and United States Army doctrine has established possible counterinsurgency strategies, little research prescribes host nation and counterinsurgent force strength. To this end, we develop a mixed-integer program to provide an estimate of the number of forces required to maximize the probability of a favorable resolution to the counterinsurgent and host nation countries, while minimizing unfavorable resolutions and the number of counterinsurgent deaths. This program integrates: (i) a multivariate piecewise-linear regression model to estimate the number of counterinsurgent deaths each year and (ii) a logistic regression model to estimate the probability of four types of conflict resolution over a 15-year time horizon. Constraints in the model characterize: (i) upper and lower limits on the number of counterinsurgent and host nation forces and their annual rates of increase and decrease, (ii) the characteristics of the type of counterinsurgency, (iii) an estimation of the number of counterinsurgent deaths, and (iv) an estimation of the probability of one of four resolutions. We use Somalia as a case study to estimate how counterinsurgent strategies affect the probability of obtaining each conflict resolution. We conclude that a strategy focusing on building and empowering a stable host nation force provides the highest probability of achieving a positive resolution to the counterinsurgency. Senior leaders can use this information to guide strategic decisions within a counterinsurgency.
Similar content being viewed by others
References
Armed Conflict Location and Event Data Project (ACLED). (2014). All Africa files. http://www.acleddata.com. Accessed 31 Oct 2019.
Arney, D. C., & Arney, K. (2013). Modeling insurgency, counter-insurgency, and coalition strategies and operations. The Journal of Defense Modeling and Simulation, 10(1), 57–73.
Artelli, M. J. (2007). Modeling and analysis of resolve and morale for the ‘Long War’. Fort Belvoir: Defense Technical Information Center.
Artelli, M. J., Deckro, R. F., Zalewski, D. J., Leach, S. E., & Perry, M. B. (2009). A system dynamics model for selected elements of modern conflict. Military Operations Research, 14(2), 51–74.
Atkinson, M., Gutfraind, A., & Kress, M. (2012). When do armed revolts succeed: Lessons from Lanchester theory. Journal of the Operational Research Society, 63(10), 1363–1373.
Baker, P. (2009). How Obama came to plan for ‘Surge’ in Afghanistan. New York Times, 5(12), 2009.
Bentson, K. A. (2006). An epidemiological approach to terrorism. Defense Technical Information Center: Technical report.
Berman, E., Callen, M., Felter, J. H., & Shapiro, J. N. (2011a). Do working men rebel? Insurgency and unemployment in Afghanistan, Iraq, and the Philippines. Journal of Conflict Resolution, 55(4), 496–528.
Berman, E., Shapiro, J., & Felter, J. (2011b). Can hearts and minds be bought? The economics of counterinsurgency in Iraq. Journal of Political Economy, 119(4), 766–819.
Center for Army Analysis. (2010). Irregular warfare database.
Collier, P., & Hoeffler, A. (2004). Greed and grievance in civil war. Oxford Economic Papers, 56(4), 563–595.
Collier, P., Hoeffler, A., & Söderbom, M. (2004). On the duration of civil war. Journal of Peace Research, 41(3), 253–273.
Department of Defense. (2011). Joint publication 1–02: Department of defense dictionary of military and associated terms. Washington, DC: Government Printing Office.
Department of the Army. (2006). Field manual 3–24: Counterinsurgency. Washington, DC: Government Printing Office.
Drapeau, M. D., Armstrong, R. E., & Hurley, P. C. (2008). So many zebras, so little time: Ecological models and counterinsurgency operations. Darby: DIANE Publishing.
Fearon, J. D. (2004). Why do some civil wars last so much longer than others? Journal of Peace Research, 41(3), 275–301.
Fearon, J. D., & Laitin, D. D. (2003). Ethnicity, insurgency, and civil war. American Political Science Review, 97(1), 75–90.
Fourer, R., Gay, D. M., & Kernighan, B. W. (2003). AMPL: A modeling language for mathematical programming. South Melbourne: Thomson.
Gelpi, C., Feaver, P., & Reifler, J. (2009). Paying the human costs of war: American public opinion and casualties in military conflicts. Princeton: Princeton University Press.
Ghose, D., Speyer, J. L., & Shamma, J. S. (2002). A game theoretical multiple resource interaction approach to resource allocation in an air campaign. Annals of Operations Research, 109(1–4), 15–40.
Gurr, T. R. (1990). Ethnic warfare and the changing priorities of global security. Mediterranean Quarterly, 1(1), 82–98.
Hackett, J. (2008). The military balance 2008. Abingdon: Routledge.
Harness, C. (2006). The revolutionary John Adams. Washington: National Geographic Books.
Harrison, S. A., & Rayward-Smith, V. J. (1999). Minimal cost linkages in graphs. Annals of Operations Research, 86, 295–319.
Hartzell, C., & Hoddie, M. (2007). Crafting peace: Power-sharing institutions and the negotiated settlement of civil wars. University Park: Penn State Press.
Heston A., & Summers R. (2011). Penn world table version 7.0. https://urldefense.proofpoint.com/v2/urlu=https-3A__cid.econ.ucdavis.edu_pwt.html&d=DwIFAg&c=vh6FgFnduejNhPPD0fl_yRaSfZy8CWbWnIf4XJhSqx8&r=tr37pLMKuZcfSC3Gl2yDumEEj4eKb1_KBfWD90OLbA&m=AxTKHdLUx3p2uRTRvmrJVIRcwCvZawzio9eo5gOQIQo&s=cgcp7LdBHucGT0yFf6GcjI0fC1F_IKUx9gX_hCxD0B8&e=. Accessed 31 Oct 2019.
Ianovsky, E., & Kreimer, J. (2011). An optimal routing policy for unmanned aerial vehicles (analytical and cross-entropy simulation approach). Annals of Operations Research, 189(1), 215–253.
IBM (2014) IBM ILOG CPLEX optimization studio: CPLEX user’s manual (V12.6). International Business Machines Corporation, Armonk, NY.
Ivanova, P. I., & Tagarev, T. D. (2000). Indicator space configuration for early warning of violent political conflicts by genetic algorithms. Annals of Operations Research, 97(1–4), 287–311.
Kaufman, L., & Rousseeuw, P. (1987). Clustering by means of medoids. In Y. Dodge (Ed.), Statistical data analysis based on the \(L_{1}\)-norm and related methods (pp. 405–416). Basel: Birkhäuser Verlag.
King, M. L. (2014). Optimizing counterinsurgency operations. Ph.D. Dissertation, Colorado School of Mines.
King, M. L., Hering, A. S., & Newman, A. M. (2014). Evaluating counterinsurgency classification schemes. Military Operations Research, 19(3), 5–25.
King, M. L., Hering, A. S., & Aguilar, O. M. (2016). Building predictive models of counterinsurgent deaths using robust clustering and regression. The Journal of Defense Modeling and Simulation, 13(4), 449–465.
Kolesar, P., Leister, K., Stimpson, D., & Woodaman, R. (2013). A simple model of optimal clearance of improvised explosive devices. Annals of Operations Research, 208(1), 451–468.
Kozanidis, G., Gavranis, A., & Liberopoulos, G. (2014). Heuristics for flight and maintenance planning of mission aircraft. Annals of Operations Research, 221(1), 211–238.
Lacina, B., & Gleditsch, N. (2005). Monitoring trends in global combat: A new dataset of battle deaths. European Journal of Population/Revue Européenne de Démographie, 21(2), 145–166.
Larson, E. V. (1996). Casualties and consensus: The historical role of casualties in domestic support for US military operations. Santa Monica: Rand Corporation.
Lemeshow, S., & Hosmer, D. (1982). A review of goodness of fit statistics for use in the development of logistic regression models. American Journal of Epidemiology, 115(1), 92–106.
Loerch, A., & Rainey, L. (2007). Methods for conducting military operational analysis. Virginia: Military Operations Research Society.
Logistics Management Institute. (2009). 2009 Annual report.
Lyall, J., & Wilson, I. (2009). Rage against the machines: Explaining outcomes in counterinsurgency wars. International Organization, 63(1), 67–106.
Mason, T. D., & Fett, P. (1996). How civil wars end: A rational choice approach. Journal of conflict resolution, 40(4), 546–568.
Mason, T. D., Weingarten, J. P., & Fett, P. J. (1999). Win, lose, or draw: Predicting the outcome of civil wars. Political Research Quarterly, 52(2), 239–268.
McGrath, J. (2006). Boots on the ground: Troop density in contingency operations. Global War on Terrorism Occasional Paper 16, 2(3), 47–57.
Mueller, J. E. (1980). The search for the ‘Breaking Point’ in Vietnam. International Studies Quarterly, 24(4), 497–519.
Mueller, J. E. (1993). American public opinion and the Gulf War. In The political psychology of the Gulf War: Leaders, publics, and the process of conflict (pp. 199–226).
Mueller, J. E. (2005). The Iraq syndrome. Foreign Affairs, 84, 44–54.
Newey, W. K., & West, K. D. (1986). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Cambridge: National Bureau of Economic Research.
Paul, J. A., & Bagchi, A. (2018). Civil liberties and terrorism in Middle East, North Africa, Afghanistan, and Pakistan. Annals of Operations Research, 275, 623–651.
Perla, P. P. (1990). The art of wargaming: A guide for professionals and hobbyists. Annapolis: Naval Institute Press.
Quinlivan, J. T. (1995). Force requirements in stability operations. Parameters, 25(4), 59–69.
Quinlivan, J. T. (2003). Burden of victory: The painful arithmetic of stability operations. Rand Review, 27(2), 28–29.
R Core Team. (2014). R: A language and environment for statistical computing. http://www.R-project.org/. Accessed 31 Oct 2019.
Rappoport, H. K., Levy, L. S., Toussaint, K., & Golden, B. L. (1994). A transportation problem formulation for the MAC airlift planning problem. Annals of Operations Research, 50(1), 505–523.
Roeder, P. G. (2014). Ethnolinguistic fractionalization (ELF) indices, 1961 and 1985. http://pages.ucsd.edu/~proeder/elf.htm. Accessed 31 Oct 2019.
Safaei, N., Banjevic, D., & Jardine, A. K. (2011). Workforce-constrained maintenance scheduling for military aircraft fleet: A case study. Annals of Operations Research, 186(1), 295–316.
Saie, C. M. (2012). Understanding the instruments of national power through a system of differential equations in a counterinsurgency. Defense Technical Information Center: Technical report.
Schaffer, M. B. (1965). Lanchester models for phase II insurgency. Santa Monica: RAND Corporation.
Schaffer, M. B. (2007). A model of 21st century counterinsurgency warfare. The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology, 4(3), 252–261.
Schutte, S. (2011). Geography, outcome, and casualties: A unified model of insurgency. In Workshop on inequality, grievances and civil war, Zurich, Switzerland.
Singer, D. (1988). Reconstructing the correlates of war dataset on material capabilities of states, 1816–1985. International Interactions, 14(2), 115–132.
Smith, D. (2010). CAA current operations support to OIF/OEF. Defense Technical Information Center: Technical report.
Stockholm International Peace Research Institute. (2014). Multilateral peace operations database. http://www.sipri.org/databases/pko. Accessed 31 Oct 2019.
Teter, M. D., Royset, J. O., & Newman, A. M. (2016). Modeling uncertainty of expert elicitation for use in risk-based optimization. Annals of Operations Research, 280, 189–210.
Toft, M. D. (2009). Securing the peace: the durable settlement of civil wars. Princeton: Princeton University Press.
United Nations. (2014a). United National Operation in Somalia I. https://urldefense.proofpoint.com/v2/url?u=https-3A__peacekeeping.un.org_mission_past_unosom1backgr2.html&d=DwIFAg&c=vh6FgFnduejNhPPD0fl_yRaSfZy8CWbWnIf4XJhSqx8&r=tr37p-LMKuZcfSC3Gl2yDumEEj4eKb1_KBfWD90OLbA&m=AxTKHdLUx3p2uRTRvmrJVIRcwCvZawzio9eo5gOQIQo&s=K0K4Ykiwpt6fAt9d8rydWwRumIQIv-QueVff8GQFZUQ&e=.
United Nations. (2014b). United National Operation in Somalia II. https://urldefense.proofpoint.com/v2/url?u=https-3A__peacekeeping.un.org_mission_past_unosom2backgr2.html&d=DwIFAg&c=vh6FgFnduejNhPPD0fl_yRaSfZy8CWbWnIf4XJhSqx8&r=tr37p-LMKuZcfSC3Gl2yDumEEj4eKb1_KBfWD90OLbA&m=AxTKHdLUx3p2uRTRvmrJVIRcwCvZawzio9eo5gOQIQo&s=M5C0s2NqpUqixL0y5_VYWMYD2X0a-zGUiC6mVxtdsnE&e=. Accessed 31 Oct 2019.
Wilkes, S., & King, M. L. (2007). Counterinsurgency wargame development. http://www.mors.org/UserFiles/file/meetings/07wa/wilkes.pdf. Unpublished presentation.
World Bank. (2014). World Bank list of least developed nations. http://data.worldbank.org/region/LDC. Accessed 31 Oct 2019.
Xu, J., Zhuang, J., & Liu, Z. (2016). Modeling and mitigating the effects of supply chain disruption in a defender-attacker game. Annals of Operations Research, 236(1), 255–270.
Yakici, E., Dell, R. F., Hartman, T., & McLemore, C. (2018). Daily aircraft routing for amphibious ready groups. Annals of Operations Research, 264(1–2), 477–498.
Acknowledgements
We would like to thank Dr Steven Stoddard from the Center for Army Analysis and Dr Darryl Ahner from the Air Force Institute of Technology for their support on this project.
Funding
This work was funded by the Air Force Institute of Technology’s Center for Operational Analysis (Grant No. FA8601-12-P-0288).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Disclaimer
The views expressed in this paper are our own and do not represent the views of the United States Government, the Department of Defense, or the United States Army.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
King, M.L., Galbreath, D.R., Newman, A.M. et al. Combining regression and mixed-integer programming to model counterinsurgency. Ann Oper Res 292, 287–320 (2020). https://doi.org/10.1007/s10479-019-03420-x
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10479-019-03420-x