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Partitioning Method for Emergent Behavior Systems Modeled by Agent-Based Simulations

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Partitioning Method for Emergent Behavior Systems Modeled by Agent-Based Simulations Old Dominion University ODU Digital Commons Computational Modeling & Simulation Computational Modeling & Simulation Engineering Theses & Dissertations Engineering Winter 2012 Partitioning Method for Emergent Behavior Systems Modeled by Agent-Based Simulations O. Thomas Holland Old Dominion University Follow this and additional works at: https://digitalcommons.odu.edu/msve_etds Part of the Computer Sciences Commons Recommended Citation Holland, O. T.. "Partitioning Method for Emergent Behavior Systems Modeled by Agent-Based Simulations" (2012). Doctor of Philosophy (PhD), Dissertation, Computational Modeling & Simulation Engineering, Old Dominion University, DOI: 10.25777/wqf0-ke57 https://digitalcommons.odu.edu/msve_etds/36 This Dissertation is brought to you for free and open access by the Computational Modeling & Simulation Engineering at ODU Digital Commons. It has been accepted for inclusion in Computational Modeling & Simulation Engineering Theses & Dissertations by an authorized administrator of ODU Digital Commons. For more information, please contact [email protected]. PARTITIONING METHOD FOR EMERGENT BEHAVIOR SYSTEMS MODELED BY AGENT-BASED SIMULATIONS by O. Thomas Holland B. S. E. E. 1983, Tennessee Technological University M. S. E. E. 1996, Virginia Polytechnic Institute and State University A Dissertation Submitted to the Faculty of Old Dominion University in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY MODELING AND SIMULATION OLD DOMINION UNIVERSITY December 2012 Approved by: John Sokolowski (Director) ^rederic D. McKenitfe (Member) -?Cndreas Tolk (Member) Bruce Copeland (Member) ABSTRACT PARTITIONING METHOD FOR EMERGENT BEHAVIOR SYSTEMS MODELED BY AGENT-BASED SIMULATIONS O. Thomas Holland Old Dominion University, 2012 Director: Dr. John Sokolowski Used to describe some interesting and usually unanticipated pattern or behavior, the term emergence is often associated with time-evolutionary systems comprised of relatively large numbers of interacting yet simple entities. A significant amount of previous research has recognized the emergence phenomena in many real-world applications such as collaborative robotics, supply chain analysis, social science, economics and ecology. As improvements in computational technologies combined with new modeling paradigms allow the simulation of ever more dynamic and complex systems, the generation of data from simulations of these systems can provide data to explore the phenomena of emergence. To explore some of the modeling implications of systems where emergent phenomena tend to dominate, this research examines three simulations based on familiar natural systems where each is readily recognized as exhibiting emergent phenomena. To facilitate this exploration, a taxonomy of Emergent Behavior Systems (EBS) is developed and a modeling formalism consisting of an EBS lexicon and a formal specification for models of EBS is synthesized from the long history of theories and observations concerning emergence. This modeling formalism is applied to each of the systems and then each is simulated using an agent-based modeling framework. To develop quantifiable measures, associations are asserted: 1) between agent-based models of EBS and graph-theoretical methods, 2) with respect to the formation of relationships between entities comprising a system and 3) concerning the change in uncertainty of organization as the system evolves. These associations form the basis for three measurements related to the information flow, entity complexity, and spatial entropy of the simulated systems. These measurements are used to: 1) detect the existence of emergence and 2) differentiate amongst the three systems. The results suggest that the taxonomy and formal specification developed provide a workable, simulation-centric definition of emergent behavior systems consistent with both historical concepts concerning the emergence phenomena and modern ideas in complexity science. Furthermore, the results support a structured approach to modeling these systems using agent-based methods and offers quantitative measures useful for characterizing the emergence phenomena in the simulations. iv Copyright, 2012, by O.Thomas Holland, All Rights Reserved. V To Linda, Kathryn, and Daniel. And to my father, Orgal, for his 90th birthday. vi ACKNOWLEDGMENTS Much like the advent of computer science in the middle of the 20th Century, we have not yet seen the full maturity of the discipline of modeling and simulation. I am certain that the 21st Century will see tremendous strides in this field and I am thrilled to be part of it, even if only in a small way. For the opportunity to be part of something revolutionary I am sincerely grateful to my professors, who taught me that modeling and simulation is indeed an academic discipline. I want to thank sincerely each member of my committee for your time and guidance; I truly admire each of you and the accomplishments in your careers. I owe a great debt of gratitude to Dr. Bruce Copeland, a mentor and colleague, who has been a great encouragement to me when the demands of work, family and health have challenged the completion of this dissertation. Finally, I am deeply grateful to Dr. John Sokolowski for his guidance, efforts, patience and support over the years of this work. Thanks to you all. vii TABLE OF CONTENTS Page LIST OF TABLES ix LIST OF FIGURES xi Chapter 1. INTRODUCTION 1 1.1 PROBLEM STATEMENT 3 1.2 MOTIVATION 6 1.3 SCOPE AND LIMITATIONS 11 1.4 DISSERTATION ORGANIZATION 14 1.5 SIGNIFICANCE OF THIS WORK 17 1.6 DISSERTATION PRODUCTS 18 2. LITERATURE REVIEW AND SYNTHESIS 19 2.1 A BRIEF HISTORY OF EMERGENCE 22 2.2 MODERN INTERPRETATION OF EMERGENCE 25 2.3 CONCEPTS AND LEXICON 26 2.4 COMPLEXITY AND THE ROLE OF MODELING AND SIMULATION 30 2.5 EMERGENT BEHAVIOR AND THE AGENT METAPHOR 32 2.6 CHALLENGES TO MODELING EMERGENCE AND EMERGENT BEHAVIOR SYSTEMS 38 3. METHOD AND APPROACH 49 3.1 ENTITY COMPLEXITY IN EBS 52 3.2 GRAPH THEORY CONCEPTS AND RELEVANCE TO EBS 55 3.3 INFORMATION THEORY (ENTROPY) 65 3.4 MECHANISMS OF FEEDBACK (SOURCES OF CONSTRAINT) 69 3.5 MEASURING CONSTRAINT 78 3.6 A WORKING TAXONOMY FOR EMERGENT BEHAVIOR SYSTEMS 80 3.7 THE ROLE OF CONSTRAINT IN THE EBS TAXONOMY 89 viii 3.8 DEFINING EMERGENT BEHAVIOR SYSTEMS 92 4. ANALYSIS FRAMEWORK 102 4.1 FRAMING THE PROBLEM AND DEFINING METRICS 102 4.2 THE INFORMATION FLOW METRIC - CHARACTERISTIC PATH LENGTH 105 4.3 THE COMPLEXITY METRIC - CLUSTERING COEFFICIENT 111 4.4 THE ORGANIZATION METRIC - SHANNON ENTROPY 115 4.5 HYPOTHESIS TESTS 123 SIMULATION, ANALYSIS AND RESULTS 129 5.1 APPLYING THE EBS MODELING FORMALISM 130 5.2 NETLOGO MODELS 143 5.3 EBS METRICS IN MATLAB 152 5.4 PRELIMINARY ANALYSIS 159 5.5 DESIGN OF EXPERIMENTS 169 5.6 ANALYSIS AND RESULTS 180 CONCLUSIONS 201 6.1 SUGGESTED NEAR-TERM APPLICATIONS OF THIS WORK 203 6.2 OBSERVATIONS AND AREAS FOR CONTINUED RESEARCH 204 6.3 OTHER CONSIDERATIONS 212 REFERENCES 215 APPENDICES 223 A. GLOSSARY 223 B. MATLAB CODE & SCRIPTS 225 C. A HEURISTIC MEASURE OF ENTITY SOPHISTICATION IN EMERGENT BEHAVIOR SYSTEMS 247 VITA 267 ix LIST OF TABLES Table Page 1. Ferber's Agent Definition 36 2. EBS Modeling Perspectives 45 3. Predator - Prey Rules 47 4. States of the British Traffic Signals 70 5. Traffic Signal with Independent States 70 6. Bar-Yam's types of emergence 80 7. Fromm's Four Types of Emergence 81 8. The Five Types of EBS 82 9. The Characteristics of the Different Types of Entities in an EBS 98 10. EBS Specification Terms 101 11. Meso-scale System State Plots 120 12. A Minimally Constrained Case Data Analysis 123 13. Factors and Conditions for the Particle System Simulation 132 14. Factors and Conditions for the Flocking Simulation 136 15. Factors and Conditions Affecting the Stigmergy Simulation 142 16. Experiment Considerations for the Inherently Constrained Particle System 172 17. Factorial Experiment for Inherent Constraint in the Particle System 173 18. Experiment Considerations for the Flocking System 175 19. Factorial Experiment for the Constrained Flocking System 176 20. Experiment Considerations for the Constrained Stigmergy System 178 X 21. Factorial Experiment for the Constrained Stigmergy System 179 22. HI Results for the Flow Metric (CPL) in the Particle System 181 23. HI Results for the Complexity Metric in the Particle System 184 24. HI Results for the Entropy Metric in the Particle System 185 25. HI Results for the Flow Metric in the Flocking System 186 26. HI Results for the Complexity Metric in the Flocking System 188 27. HI Results for the Entropy Metric in the Flocking System 191 28. HI Results for the Flow Metric in the Stigmergy System 192 29. HI Results for the Entropy Metric in the Stigmergy System 192 30. ANOVA Results for CPL 196 31. ANOVA Results for GCC 198 32. ANOVA Results for SE 199 xi LIST OF FIGURES Figure Page 1. History Timeline of Emergence Related Interests 4 2. Local to Global to Local Feedback in an EBS 6 3. Dissertation Research Process 13 4. Approach Mapped to Dissertation Chapters 14 5. Ablowitz's Resultant and Emergent 24 6. Nwana Agent Typology (Nwana, 1996) 34 7. Communication in Port Automata 41 8. A Pile of Rocks Depicted as a Graph 56 9. An Undirected Graph and its Corresponding Adjacency Matrix 57 10. Removing the Centermost Vertex 58 11. Regular Graph 60 12. Complete Graph 61 13. Directed Graph 62 14. A Hamiltonian Path Through a Simple Regular Graph 63 15. Tree Graph 63 16. Bipartite Graph 64 17. Disconnected Graph 65 18. Schematic of Shannon's General Communication System 66 19. Micro- to Meso- Scale Relationship 74 20. Inherent Constraints Are Built-in Restrictions 75 21. Contextual Constraints Result From Objects Constituting the Environment 76 22. Entity Constraints Result From Direct Interaction Between Entities 77 23. Stigmergic Constraint Results When Entities Communicate Indirectly 78 24. Guerin's and Kunkle's Pheromone Following Ants is an Example of Meso-scale Inherent and Stigmergic Constraints 79 25. Type 1- Nominal Emergence 85 26. Type 2- Moderated Emergence 87 27.
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