Optimizing counterinsurgency operations

Abstract

The United States military requires models that estimate the number of forces needed or that measure success during a counterinsurgency. These types of models depend on data from historic counterinsurgencies, but scholars differ on how to compare historic counterinsurgencies to an ongoing or future conflict. One such method to analyze counterinsurgencies applies a classification scheme to them, and then compares past counterinsurgencies of the same type to the counterinsurgency in question. We test existing classification schemes and a data-driven classification scheme to determine the best groupings of historic data to use in modeling counterinsurgencies. We use these groupings in a math program that: (i) employs a linear regression model with transformed variables to estimate the number of counterinsurgent deaths for each year, and (ii) uses this estimate with a logistic regression model to maximize the likelihood of a favorable resolution and minimize casualties for a fifteen year horizon. Constraints in the model include: i) upper and lower limits on counterinsurgent and host nation forces and their rates of increase and decrease, (ii) an assessment of the category of a counterinsurgency, based on the values of multiple decision variables, (iii) an estimation of the number of counterinsurgent deaths, and (iv) the estimation of the likelihood of one of four resolutions of the counterinsurgency. We apply the math program by using available historic data to examine a case study for a current conflict. The results of this model provide valuable insights for military analysts and leaders on counterinsurgency modeling techniques and trends in historic counterinsurgencies.