Using an agent-based model to explore troop surge strategy

Author:

Sokolowski John A1,Banks Catherine M1,Morrow Brent2

Affiliation:

1. Virginia Modeling, Analysis and Simulation Center, Old Dominion University, USA

2. U.S. Army, USA

Abstract

In October of 2001, the United States invaded Afghanistan and replaced the Taliban government. Since its overthrow, the Taliban has pieced together and waged an insurgency to retake Afghanistan, and that insurgency has gained momentum and grown in strength while the United States/North Atlantic Treaty Organization (NATO) effort shrank in size to about 55,000 troops in 2007. A wide range of factors contributed to the insurgency, ranging from socio-cultural to economic to political. This research applied an in-depth study of Afghanistan to an agent-based model to determine if a military troop surge emphasizing a focused security effort could be successful in battling the growing insurgency within Afghanistan. An agent-based model was created and validated against the strategy and situation on the ground in Afghanistan that existed in 2007. Three experiments were conducted representing surges of 50%, 200%, and 400%. The results indicated that a surge of 200% or greater of the existing size force would be necessary to reduce the size of the insurgency, but that a surge of only 50% (50,000 more troops) would not bring about any significant changes as compared to the existing strategy. These model results provide insight into the potential success of various sized troop surges in Afghanistan that implement a focused security effort.

Publisher

SAGE Publications

Subject

Engineering (miscellaneous),Modelling and Simulation

Reference14 articles.

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1. A Systems-Of-Systems Conceptual Model and Live Virtual Constructive Simulation Framework for Improved Nuclear Disaster Emergency Preparedness, Response, and Mitigation;Journal of Homeland Security and Emergency Management;2016-09-01

2. A Call to Arms: Standards for Agent-Based Modeling and Simulation;Journal of Artificial Societies and Social Simulation;2015

3. Applying reinforcement learning to an insurgency Agent-based Simulation;The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology;2013-08-21

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