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Dynamic Agent Based Modeling Using Bayesian Framework For DYNAMIC AGENT BASED MODELING USING BAYESIAN FRAMEWORK FOR ADDRESSING INTELLIGENCE ADAPTIVE NUCLEAR NONPROLIFERATION ANALYSIS A Dissertation by ROYAL ALBERT ELMORE Submitted to the Office of Graduate and Professional Studies of Texas A&M University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Chair of Committee, William S. Charlton Committee Members, David Boyle Craig Marianno Justin Yates Head of Department, Yassin A. Hassan December 2014 Major Subject: Nuclear Engineering Copyright 2014 Royal Albert Elmore ABSTRACT Realistically, no two nuclear proliferating or defensive entities are exactly identical; Agent Based Modeling (ABM) is a computational methodology addressing the uniqueness of those facilitating or preventing nuclear proliferation. The modular Bayesian ABM Nonproliferation Enterprise (BANE) tool has been developed at Texas A&M University for nuclear nonproliferation analysis. Entities engaged in nuclear proliferation cover a range of activities and fall within proliferating, defensive, and neutral agent classes. In BANE proliferating agents pursue nuclear weapons, or at least a latent nuclear weapons capability. Defensive nonproliferation agents seek to uncover, hinder, reverse, or dismantle any proliferation networks they discover. The vast majority of agents are neutral agents, of which only a small subset can significantly enable proliferation. BANE facilitates intelligent agent actions by employing entropy and mutual information for proliferation pathway determinations. Factors including technical success, resource expenditures, and detection probabilities are assessed by agents seeking optimal proliferation postures. Coupling ABM with Bayesian analysis is powerful from an omniscience limitation perspective. Bayesian analysis supports linking crucial knowledge and technology requirements into relationship networks for each proliferation category. With a Bayesian network, gaining information on proliferator actions in one category informs ii defensive agents where to expend limited counter-proliferation impeding capabilities. Correlating incomplete evidence for pattern recognition in BANE using Bayesian inference draws upon technical supply side proliferation linkages grounded in physics. Potential or current proliferator security, economic trajectory, or other factors modify demand drivers for undertaking proliferation. Using Bayesian inference the coupled demand and supply proliferation drivers are connected to create feedback interactions. Verification and some validation for BANE is performed using scenarios and historical case studies. Restrictive export controls, swings in global soft power affinity, and past proliferation program assessments for entities ranging from the Soviet Union to Iraq demonstrates BANE‟s flexibility and applicability. As a newly developed tool, BANE has room for future contributions from computer science, engineering, and social scientists. Through BANE the framework exists for detailed nonproliferation expansion into broader weapons of mass effect analysis; since, nuclear proliferation is but one option for addressing international security concerns. iii DEDICATION For my grandparents and parents who sacrificed so much in two generations to bring me to this point. iv ACKNOWLEDGEMENTS I would like to thank my committee chair, Dr. William Charlton, and my committee members, Dr. Boyle, Dr. Marianno, and Dr. Yates, for their guidance and support throughout the course of this research. Specifically to Dr. Charlton I will always remember the NUEN 656 discussions on stochastic process and marble based discussions on cultural sensitivities. Thanks also go to my friends and colleagues and the department faculty and staff for making my time at Texas A&M University a great experience. I also want to extend my gratitude to the National Science Foundation and Nuclear Nonproliferation International Safeguards Fellowship for financial support. Finally, thanks to my mother and father for their encouragement and to my wife for her patience and love. v NOMENCLATURE ABM Agent Based Modeling API Application Programmer Interface BANE Bayesian Agent Based Modeling [ABM] Nonproliferation Enterprise CAS Complex Adaptive Systems CMA Civil Military Affairs CWMD Counter Weapons of Mass Destruction DoD Department of Defense DOE Department of Energy GUI Graphical User Interface IC Intelligence Community MAS Multi-Agent System NNSA National Nuclear Security Administration SQL Structured Query Language WMD Weapons of Mass Destruction WME Weapons of Mass Effect UF6 Uranium Hexafluoride vi TABLE OF CONTENTS Page ABSTRACT .......................................................................................................................ii DEDICATION .................................................................................................................. iv ACKNOWLEDGEMENTS ............................................................................................... v NOMENCLATURE .......................................................................................................... vi TABLE OF CONTENTS .................................................................................................vii LIST OF FIGURES ............................................................................................................ x LIST OF TABLES .......................................................................................................... xiv CHAPTER I INTRODUCTION ........................................................................................ 1 I.A. History of Nuclear Proliferation Assessment Methodologies................................. 2 I.A.1. Trends in Proliferation Resistance Methodologies: Barrier Approaches ......... 3 I.A.2. Trends in Proliferation Resistance Methodologies: Pathway Approaches ...... 5 I.A.3. Proliferation Resistance Methodology Comparisons ....................................... 7 I.A.4. Moving From Static to Dynamic Proliferation Analysis Tools ....................... 7 I.B. Justification of Bayes Theory and ABM from Nuclear Policy Perspective ............ 9 CHAPTER II BANE UNDERLYING THEORY ............................................................ 14 II.A. Bayes‟ Theory Background ................................................................................. 14 II.B. Past Netica Graphical User Interface Basis for Nonproliferation Assessments .. 18 II.B.1. Netica Bayesian Network Overview ............................................................. 18 II.C. Agent Based Modeling Background and Theory ................................................. 29 II.D. Agent Based Modeling Elaboration for Nuclear Nonproliferation ..................... 34 II.E. Advantages of ABM using Bayesian Analysis .................................................... 40 CHAPTER III BANE STRUCTURE AND ARRANGEMENT ..................................... 44 III.A. Outline of BANE Agent Methodology .............................................................. 44 III.A.1. Proliferating Agents ..................................................................................... 45 III.A.2. Defensive Agents ......................................................................................... 46 III.A.3. Neutral Agents ............................................................................................. 48 vii Page III.B. BANE Modular Configuration Overview .......................................................... 50 III.B.1. BANE Network Arrangement ..................................................................... 57 III.C. Netica GUI Node Level Verification Testing..................................................... 70 III.C.1. Netica GUI Visualization Tool .................................................................... 72 III.C.2. Netica Application Programmer Interface Parser ........................................ 74 III.C.3. SQL Server Access and Storage .................................................................. 89 III.C.4. Optimization Solver ..................................................................................... 91 III.C.5. Uncertainties .............................................................................................. 100 III.C.6. Affinities and Influence ............................................................................. 105 III.C.7. Omniscience and Monitoring .................................................................... 113 III.D. Justification of BANE Modular Development and Upgrade Potential ............ 116 III.E. BANE Tiered Operations and Input Data ......................................................... 117 CHAPTER IV AGENT EMPOWERING BANE MODULES IN ABM CONTEXT ... 119 IV.A. BANE Agent Technical Success Decision Making ......................................... 119 IV.A.1. Shannon Entropy and Mutual Information Based Technical Success ...... 120 IV.A.2. Netica Cases for Agent Information .......................................................... 133 IV.B. Optimization for Constrained Agents............................................................... 139 IV.C. Internal Agent Uncertainties ............................................................................ 144 IV.D. Temporal Affinity and Influence Evolution Modules ...................................... 147 IV.E. Omniscience and Monitoring Agent Implications............................................ 152 IV.F. Agent Empowerment Synopsis ........................................................................
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