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Modeling Social Learning: an Agent-Based Approach

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Modeling Social Learning: an Agent-Based Approach Old Dominion University ODU Digital Commons Computational Modeling & Simulation Computational Modeling & Simulation Engineering Theses & Dissertations Engineering Fall 2019 Modeling Social Learning: An Agent-Based Approach Erika G. Ardiles Cruz Old Dominion University, [email protected] Follow this and additional works at: https://digitalcommons.odu.edu/msve_etds Part of the Computer Engineering Commons, and the Social Psychology Commons Recommended Citation Ardiles Cruz, Erika G.. "Modeling Social Learning: An Agent-Based Approach" (2019). Doctor of Philosophy (PhD), Dissertation, Computational Modeling & Simulation Engineering, Old Dominion University, DOI: 10.25776/ppbs-8751 https://digitalcommons.odu.edu/msve_etds/53 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]. MODELING SOCIAL LEARNING: AN AGENT-BASED APPROACH by Erika G. Ardiles Cruz B.S. December 1998, Universidad San Antonio Abad 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 2019 Approved by: John Sokolowski (Director) Roland Mielke (Member) Bryan Paine (Member) ABSTRACT MODELING SOCIAL LEARNING: AN AGENT-BASED APPROACH Erika G. Ardiles Cruz Old Dominion University, 2019 Director: Dr. John Sokolowski Learning is the process of acquiring or modifying knowledge, behavior, or skills. The ability to learn is inherent to humans, animals, and plants, and even machines are provided with algorithms that could mimic in a restricted way the processes of learning. Humans learn from the time they are born until they die because of a continuous process of interaction between them and their environment. Behavioral Psychology Theories and Social Learning Theories study behavior learned from the environment and social interactions through stimulus-response. Some computer approaches to modeling human behavior attempted to represent the learning and decision-making processes using agent-based models. This dissertation develops a computer model for social learning that allows agents to exhibit behavior learned through social interactions and their environment. The use of an agent- based model allows representing a complex human system in a computer environment. Behavioral Psychology Theories and Social Learning Theories provide the explanatory theoretical framework. The learning processes are implemented using the Rescorla-Wagner Model. The learning structure is implemented using an adaptation of Agent Zero. The decision- making process is implemented using a threshold equation. A use case in youth gang homicides is developed, calibrated, validated, and used for policing and decision making through simulation of multiple case scenarios. The simulation results show the model accuracy in representing learning and decision-making processes similar to those exhibited in the complex human system represented. iv Copyright, 2019, by Erika G. Ardiles Cruz, All Rights Reserved. v This thesis is dedicated to Marco, Diego, and Piero, for they unconditional love and support all these years. vi ACKNOWLEDGMENTS Many people have contributed to the successful completion of this dissertation. I am grateful to my advisor Dr. John Sokolowski, who provided me with valuable advice and guidance during all the years that took the competition of this research. I am thankful to each member of my committee for their valuable time, guidance, and feedback. I want to express my gratitude to Dr. Timothy Kroecker for his unconditional support, guidance, and funding since I joined the Centers of Excellence in Cybersecurity Program from the Air Force Research Laboratory Rome NY. I am very thankful to the working staff at the Batten College of Engineering and Technology, who received me as a college and friend, and to whom I thank for the most welcoming and pleasant work environment. A special thanks to Dr. Anthony Dean and Dr. Khan Intekharuddin for all their support, guidance, and for providing me funding. Finally, I want to thank my family for the unconditional love and support, for which I will always be grateful. Many institutions had provided me with funding during the years that took the completion of this research. I want to thank the Virginia Modelling Analysis and Simulation Center for granting me a three years scholarship. I want to thank the Air Force Research Laboratory Rome NY, the Griffiss Institute, and the Information Institute at Rome NY, for granting me with internships, assistantships, and fellowships. I want to thank the Batten College of Engineering Technology for providing me assistantship for two years. vii NOMENCLATURE SLA Social Learning Agent Dti () Definitions attribute of i− th SLA in time t Ati () Association attribute of i− th SLA in time t Oti () Observation attribute of i− th SLA in time t Rti () Reinforcement attribute of i− th SLA in time t Lti () Social Learning attribute of i− th SLA in time t Mti () Motivated? attribute of i− th SLA in time t Pti () Position attribute of i− th SLA in time t xti () x component attribute of i− th SLA in time t yti () y component attribute of i− th SLA in time t Si Cellular Automaton State LV Local Vision SN Social Network GA Gang Affiliation SZ Social Network Size p Grid’s Number of patches τ SLA’s Threshold H Number of Homicides viii TABLE OF CONTENTS Page LIST OF TABLES ......................................................................................................................... ix LIST OF FIGURES .........................................................................................................................x Chapter 1. INTRODUCTION .......................................................................................................................1 1.1 THESIS STATEMENT ...............................................................................................1 1.2 PROBLEM STATEMENT .........................................................................................1 1.3 MOTIVATION ............................................................................................................3 1.4 APPROACH ..............................................................................................................10 1.5 CONTRIBUTIONS ...................................................................................................14 1.6 DISSERTATION ORGANIZATION .......................................................................14 2. LITERATURE REVIEW ..........................................................................................................16 2.1 BEHAVIORAL PSYCHOLOGY .............................................................................16 2.2 SOCIAL LEARNING THEORY ..............................................................................26 2.3 AGENT-BASED MODELING AND SIMULATION .............................................36 2.4 SUMMARY OF THE STATE-OF-THE-ART .........................................................46 3. METHODOLOGY ....................................................................................................................50 3.1 LEARNING COMPONENT .....................................................................................51 3.2 LOCAL COMPONENT ............................................................................................59 3.3 SOCIAL COMPONENT ...........................................................................................63 3.4 BEHAVIOR COMPONENT .....................................................................................63 4. VALIDATION AND RESULTS ...............................................................................................67 4.1 USE CASE ................................................................................................................67 4.2 CALIBRATION ........................................................................................................86 4.3 VALIDATION ..........................................................................................................99 4.4 POLICING AND DECISION MARKING .............................................................113 5. CONCLUSIONS......................................................................................................................119 5.1 CONCLUSIONS .....................................................................................................120 5.2 RECOMMENDATIONS ........................................................................................124 5.3 FUTURE WORK ....................................................................................................125 REFERENCES ............................................................................................................................132 VITA ............................................................................................................................................140 ix LIST OF TABLES Table Page 1. Environment Settings. ............................................................................................................... 75 2. Confidence Intervals for the Number of Homicides. .............................................................
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