Marvin King

A summary of the proceedings and outcomes of the MORS Assessment Workshop "Analytic Support to Contingency/Named Operations and Advancing the Professionalism of Assessments," held in February 2018.

A satire on how to do and not to do a military assessment.

The purpose of a strategic assessment is to determine if an organization is achieving its strategic objectives. This is often a difficult process to implement, given normal staff aversion to introspective processes and a lack of doctrine specific to assessments. The purpose of this article is to discuss best practices and common pitfalls in military assessments while outlining steps needed to continue to improve assessments across the Department of Defense (DOD). First, we outline the doctrine… Show more

The purpose of a strategic assessment is to determine if an organization is achieving its strategic objectives. This is often a difficult process to implement, given normal staff aversion to introspective processes and a lack of doctrine specific to assessments. The purpose of this article is to discuss best practices and common pitfalls in military assessments while outlining steps needed to continue to improve assessments across the Department of Defense (DOD). First, we outline the doctrine and literature guiding the DOD. Second, we provide a review of common assessment methods used across the military. Next, we present the four best practices proven successful in the joint staff, strategic commands, and recent conflicts. Last, we provide recommendations on how to improve the state of assessments in the DOD. Show less

Counterinsurgencies are conflicts where an insurgent organization conducts violence to replace or influence a recognized government. Furthering our understanding of the conditions that influence violence in different types of counterinsurgencies is important to government leaders who must deploy scarce resources efficiently. Subject matter experts (SMEs) have developed classification schemes that divide counterinsurgencies into similar groups, but no data-driven methods have ever been… Show more

Counterinsurgencies are conflicts where an insurgent organization conducts violence to replace or influence a recognized government. Furthering our understanding of the conditions that influence violence in different types of counterinsurgencies is important to government leaders who must deploy scarce resources efficiently. Subject matter experts (SMEs) have developed classification schemes that divide counterinsurgencies into similar groups, but no data-driven methods have ever been developed. Using the robust partitioning around medoids (PAM) algorithm, we cluster counterinsurgencies based on distances among independent variables measured on each counterinsurgency. We apply several criteria for choosing the optimal number of clusters, and then we take these groups of counterinsurgencies and build regression models for counterinsurgent deaths, an annual measure of conflict status. We evaluate these schemes using cross-validation to select the grouping whose regression models best predict counterinsurgent deaths. This approach produces a set of data-driven clusters whose predictive ability is similar to the best existing SME classification scheme, but reduces error in the assignment of a new counterinsurgency to a cluster.

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The United States Military requires models that estimate the number of forces needed or measure success during a counterinsurgency. These models depend on data from historic counterin-surgencies, 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 category to the counterinsurgency in question. Three… Show more

The United States Military requires models that estimate the number of forces needed or measure success during a counterinsurgency. These models depend on data from historic counterin-surgencies, 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 category to the counterinsurgency in question. Three established classification schemes for grouping conflicts into categories based on subject matter expertise are: • the insurgent strategy, which groups insurgencies by the military methods insurgents use to fight; • the insurgent type, which groups insurgencies based on the ideology of the insurgent; and • the actor-centric classification scheme, which groups insurgencies using the counterinsurgent's mission. Using a novel dataset of counterinsurgent and insurgent deaths recorded since 1950, we build multivariate regression models for an annual measure of success for counterinsurgencies within each category of each classification scheme. We compare the results and interpret them in the context of known theories of counterinsurgency. We find evidence that supports the US Army and Joint Staff's doctrinal classification scheme and demonstrate how military modeling and social science can be integrated through the use of expert classification and quantitative analysis. Show less

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… Show more

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. Show less