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Destabilization of Adversarial O rganizations with Strategic Interventions Il-Chul Moon C M U-ISR-08-124 June 2008 School of Computer Science Institute for Software Research International Carnegie Mellon University Pittsburgh, PA Thesis Committee K athleen M. Carley, Chair Alexander H. Levis (George Mason University) James D. Herbsleb John M. G raham (United States Military Academy) Submitted in partial fulfillment of the requirements for the Degree of Doctor of Philosophy Copyright © 2008 Il-Chul Moon This research was sponsored by CAPES project (FA8650-07-C-6769) of Air Force Office of Scientific Research (AFOSR), MURI project (FA9550-05-1-0388) of Air Force Office of Scien- tific Research (AFOSR), DNA project (N00014-06-1-0921, N00014-06-0104) of Office of Naval Research (ONR), Geospatial analysis project (W911NF0710317) of ERDC-TEC, Army Research Institute (ARI), and SPAWAR project (N0016403D6623) of Naval Warfare Center. Additional support came from Carnegie Mellon University’s Center for Computational Analysis of Social and Organizational Systems (CMU CASOS). Finally, George Mason University’s System Archi- tectures Laboratory (GMU SAL) provided valuable support. The views and conclusions in this thesis should not be interpreted as representing the official policies, either expressed or implied, of AFOSR, ONR, ARI or the U.S. government. CMU SCS ISR -1 - CASOS Report Abstract Destabilization of adversarial organizations is crucial to combating terrorism. The adversarial organizations are complex adaptive systems, which include different types of entities and links to perform complex tasks and evolve over-time to adapt to changing situations. Both the complexity and the adaptivity of the adversary make it difficult for friendly forces to destabilize the adversary and to damage the performance of the adversary’s organization. The commander desires to identi- fy the terrorists command structure, key leader; assess their capability; and identify weaknesses; current technologies do not support these desires. At best, the commander might know historic activities, and most of the web of connections among known terrorists. By taking a dynamic network analytic approach and focusing on how to identify, reason about, and break 1) the adversary’s decision making structure, 2) the likelihood that the adversary can engage in key tasks; and 3) the adversary’s over-time social and geospatial behavior, we can be- gin to make headway in reasoning about this complexity and adaptivity. I develop four different, interoperable approaches supporting this assessment and estimation on adversarial organizations. These four approaches analyze different aspects of an organization, i.e. the core decision making structure, the high level assessment of task completion likelihood and the micro level simulation of the behavior of adversaries. By unifying these approaches, we can grasp a complete picture of the target as well as the destabilization strategies against it. First, I estimate the decision making structure of the organization, apply dynamic analysis metrics to it, and identify critical terrorists to be removed. This decision making structure is a trimmed organizational structure expected to be the critical command structure of the adversaries. Second, I extract an influence network for the key tasks. This is an assessment about the organizational support for the adversaries’ mission. Third, I use a multi-agent simulation (JDynet) to analyze organizational change. I simulate the CMU SCS ISR -2 - CASOS Report adversaries’ social interactions and task execution behavior. Also, I create and test simulation scenarios of removing key adversaries over the course of simulations. This estimates the damage that we can inflict on the adversarial organization with interventions. Fourth, I augment a geospa- tial component to JDynet, so the relocations of personnel and resources are included in the desta- bilization analysis. I use the geo-spatial-JDynet to simulate the strategic intervention effects with a joint picture from adversaries’ social and geospatial behavior. To ground and demonstrate this research, I use, primarily, three adversarial organizations, the terrorist networks responsible for 1998 US embassy bombing in Tanzania and Kenya; 1998 US embassy bombing in Kenya; and a global terrorist network. These are adversarial organizational structures including a task dependency network for mission execution; geospatial terrorist, re- source and expertise distributions; and terrorist social networks. I regard these datasets as an ob- served target adversarial structure and provide analyses results that are basis for destabilization strategies. My research makes contribution at theoretical, technical and empirical levels. First, I provide a theory of how to create a joint picture from different organizational and computational theories. I create theories to merge various existing theories, i.e. merging multi-agent simulations and dy- namic network analysis, dynamic network analysis and Bayesian network, dynamic network analysis and decision making structure, etc. This theoretical advancement provides a joint thought and analysis process about reasoning organizational behavior to managers, commanders and intelligence analysts. Second, I develop and test an interoperable analysis framework sup- ported by the suggested joint theory. This analysis framework is realized by expanding an analy- sis system (Organization Risk Analyzer), so that real world analysts can apply the suggested theory to their target organizations. Third, I empirically analyze three adversarial organizations to demonstrate the usage of this framework and the newly enabled analysis results. For example, the CMU SCS ISR -3 - CASOS Report newly enabled analysis approaches estimate Bin Laden considered as a non-critical terrorist in the U.S. Embassy Bombing in Tanzania and Kenya based on the existing dataset and approach is an actual critical contributor over the mission execution. This unifying theory and framework for adversarial destabilization, a partially automated intelli- gence analysis capability, 1) provide intelligence analysis results that can meet the operation tem- po in the real world, 2) bridge dynamic network analysis and various inference theories, and 3) provide a better tool that human analysts may use to reduce their time and cost of destabilization analysis. CMU SCS ISR -4 - CASOS Report Acknowledgement I thank God for allowing me to complete this thesis. Supports for this work came in part from the below research funding of various agencies. CAPES project of Air Force Office of Scientific Research (AFOSR, FA8650-07-C-6769) MURI project of Air Force Office of Scientific Research (AFOSR, FA9550-05-1-0388) DNA project of Office of Naval Research (ONR, N00014-06-1-0921, N00014-06-0104) Geospatial analysis project of ERDC-TEC, Army Research Institute (ARI, W911NF0710317) SPARWAR project of Naval Warfare Center (N0016403D6623) Additional support came from Carnegie Mellon University’s Center for Computational Analysis of Social and Organizational Systems (CMU CASOS). Finally, George Mason University’s Sys- tem Architectures Laboratory (GMU SAL) provided valuable support. The views and conclusions in this thesis should not be interpreted as representing the official policies, either expressed or implied, of AFOSR, ONR, ARI or the U.S. government. This thesis is completed because I was able to get help from three predecessors. Dr. Kathleen M. Carley is my academic advisor, and she enabled this work by her previous work on the multi- agent social modeling, the dynamic network analysis approach, the meta-network concept, etc. She is also a mentor who I will thank during my life time. I thank her for her advice on various matters, academic, research, job, etc. Time-to-time, i.e. a long drive from DC to Pittsburgh, early morning at a hotel lobby or meetings in her office, I had chances to talk to her about research, personal life, etc. She is a very enthusiastic researcher and advisor with a warm heart. Dr. Alex- ander H. Levis is another predecessor who developed many models that I used in this thesis. I met him when the MURI project started about three years ago. He makes comments and questions that go directly to the center of a problem or a task. I am glad to have his comments and questions about my works because those improve my research a lot. Dr. Lee Wagenhals is the one who de- veloped the Influence Network Analysis approach, which is used heavily in this thesis. He is also a nice researcher who reviewed my work and advised me with potential usages. I enjoyed work- ing with him. I want to thank Dr. John Graham and Dr. Jim Herbsleb. They are my thesis committee members and provided me valuable comments. Dr. John Graham gave me good comments about the do- main that I discuss in this thesis. Dr. Jim Herbsleb gave me unbiased comments that enabled me to think about the potential applications of my work to industries. I thank CASOS members for every support that they gave to me. They have been great work col- leagues, team mates, reviewers, and friends. I thank for my family and friends. I know that they know that I love them. Also, they know that I know that they love me. CMU SCS ISR -5 - CASOS Report Table of Contents 1. Introduction ........................................................................................................................... 13 1.1. Integrated framework and potential applications ........................................................