Understanding Sector Dependencies in the Stabilization and Reconstruction of Nation-States

Abstract

The United States Army is undergoing a re-definition of its Civil Affairs officer positions. A recent project to define the educational requirements for an Army Civil Affairs Officer (38G) identified an educational requirement to help officers understand the complex ways in which the operations that advance the achievement of one stabilization objective often hinder the achievement of other objectives. The system level thinking was seen to be frequently insufficiently ingrained amongst Civil Affairs Officers (and the leaders they advised), who were both often perceived to be inclined, in the face of the complexities of the situation on the ground, to become too narrowly focused on achieving their specific assigned responsibilities, limiting their ability to see how the mission effectiveness of what they were recommending would be influenced by the state and trajectory of other Sectors and how, in turn, their recommendations would influence the mission effectiveness of other Sector stewards. While system dynamics modeling has proven itself to be effective in capturing and effectively communicating feedback loops that define such non-linear (and non-intuitive) systems they do not, in themselves, provide sufficient modeling richness to comprehensively capture the critical spatial (geographical) determinants of a successful state reconstruction process. For these purposes, a multi-agent cellular automata model is recommended both as a vehicle for introducing students to the complex nature of the state reconstruction process and, eventually, for use in the field by deployed Civilian Affairs Officers at all levels. This paper describes the problem and the modeling approach to address it.