University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations Spring 2011 Modeling Memes: A Memetic View of Affordance Learning Benjamin D. Nye University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Artificial Intelligence and Robotics Commons, Cognition and Perception Commons, Other Ecology and Evolutionary Biology Commons, Other Operations Research, Systems Engineering and Industrial Engineering Commons, Social Psychology Commons, and the Statistical Models Commons Recommended Citation Nye, Benjamin D., "Modeling Memes: A Memetic View of Affordance Learning" (2011). Publicly Accessible Penn Dissertations. 336. https://repository.upenn.edu/edissertations/336 With all thanks to my esteemed committee, Dr. Silverman, Dr. Smith, Dr. Carley, and Dr. Bordogna. Also, great thanks to the University of Pennsylvania for all the opportunities to perform research at such a revered institution. This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/336 For more information, please contact [email protected]. Modeling Memes: A Memetic View of Affordance Learning Abstract This research employed systems social science inquiry to build a synthesis model that would be useful for modeling meme evolution. First, a formal definition of memes was proposed that balanced both ontological adequacy and empirical observability. Based on this definition, a systems model for meme evolution was synthesized from Shannon Information Theory and elements of Bandura's Social Cognitive Learning Theory. Research in perception, social psychology, learning, and communication were incorporated to explain the cognitive and environmental processes guiding meme evolution. By extending the PMFServ cognitive architecture, socio-cognitive agents were created who could simulate social learning of Gibson affordances. The PMFServ agent based model was used to examine two scenarios: a simulation to test for potential memes inside the Stanford Prison Experiment and a simulation of pro-US and anti-US meme competition within the fictional Hamariyah Iraqi village. The Stanford Prison Experiment simulation was designed, calibrated, and tested using the original Stanford Prison Experiment archival data. This scenario was used to study potential memes within a real-life context. The Stanford Prison Experiment simulation was complemented by internal and external validity testing. The Hamariyah Iraqi village was used to analyze meme competition in a fictional village based upon US Marine Corps human terrain data. This simulation demonstrated how the implemented system can infer the personality traits and contextual factors that cause certain agents to adopt pro-US or anti-US memes, using Gaussian mixture clustering analysis and cross-cluster analysis. Finally, this research identified significant gaps in empirical science with respect to studying memes. These roadblocks and their potential solutions are explored in the conclusions of this work. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Electrical & Systems Engineering First Advisor Barry G. Silverman, Ph.D. Keywords Memes, Affordances, Agent Based Model, Cognitive Modeling, Social Systems, Artificial Intelligence Subject Categories Artificial Intelligence and Robotics | Cognition and erP ception | Other Ecology and Evolutionary Biology | Other Operations Research, Systems Engineering and Industrial Engineering | Social Psychology | Statistical Models Comments With all thanks to my esteemed committee, Dr. Silverman, Dr. Smith, Dr. Carley, and Dr. Bordogna. Also, great thanks to the University of Pennsylvania for all the opportunities to perform research at such a revered institution. This dissertation is available at ScholarlyCommons: https://repository.upenn.edu/edissertations/336 MODELING MEMES: A MEMETIC VIEW OF AFFORDANCE LEARNING Benjamin D. Nye A DISSERTATION in Electrical and Systems Engineering Presented to the Faculties of the University of Pennsylvania in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy 2011 Supervisor of Dissertation: Dr. Barry Silverman, Professor Graduate Group Chairperson: Dr. Roch Guerin, Alfred Fitler Moore Professor Dissertation Committee Chair: Dr. Tony Smith, University of Pennsylvania Reader: Dr. Joseph Bordogna, University of Pennsylvania Reader: Dr. Kathleen Carley, Carnegie Mellon University Modeling Memes: A Memetic View of Affordance Learning Copywrite c Benjamin Daniel Nye, 2011 This work is licensed under the Creative Commons Attribution-NonCommercial- NoDerivs 3.0 Unported License. To view a copy of this license, visit: http://creativecommons.org/licenses/by-nc-nd/3.0/ or send a letter to: Creative Commons, 171 Second Street, Suite 300, San Francisco, California, 94105, USA. Acknowledgments My greatest thanks and regards to my advisor Dr. Barry Silverman, the members of the ACASA Lab past and present, and my family who has been fully behind me since the day I was born (and perhaps before). I would also like to thank my committee: Dr. Tony Smith, Dr. Joseph Bordogna, and Dr. Kathleen Carley. I am honored to have such approachable and distinguished members consulting me on this endeavor. Each of your insights has made me examine different perspectives that have improved not only this research, but will also guide me on my next steps. Additionally, I would like to thank all the external scholars who have assisted me on this journey. For the data from the Stanford Prison Experiment, I received an unprecedented level of assistance. Dr. Zimbardo is one of the most approachable scholars I have ever contacted, extremely responsive and helpful. The members of the Archives of the History of American Psychology also graciously let me work on-site for many days as I collected data. I would like to give a special thanks to Rhonda Rinehart for being my point of contact and also to Dr. Baker for helping to arrange my access. Without such assistance, I could not have worked with such landmark data. I would also like to thank my wife, Yuko, who has been patient with me through all the long days and nights toward the completion of this effort. I will always appreciate your love and support through this period. Your mere presence means the world to me. Finally, I would like to thank God for giving me the resources and stability in life to pursue these endeavors, which I hope will someday make some small benefit to the world. I promise that this work is only the beginning of a long and dedicated effort toward improving our understanding and interaction with the world. iii iv Modeling Memes: A Memetic View of Affordance Learning Benjamin D. Nye Barry G. Silverman Abstract This research employed systems social science inquiry to build a synthesis model that would be useful for modeling meme evolution. First, a formal definition of memes was proposed that balances both ontological adequacy and empirical observability. Based on this definition, a systems model for meme evolution was synthesized from Shannon Information Theory and elements of Bandura's Social Cognitive Learning Theory. Research in perception, social psychology, learning, and communication were incorporated to explain the cognitive and environmental processes guiding meme evolution. By extending the PMFServ cognitive architecture, socio-cognitive agents were created who could simulate social learning of Gibson affordances. The PMFServ agent based model was used to examine two scenarios: a simulation to test for potential memes inside the Stanford Prison Experiment and a simulation of pro-US and anti-US meme competition within the fictional Hamariyah Iraqi village. The Stanford Prison Experiment simulation was designed, calibrated, and tested using the original Stanford Prison Experiment archival data. This scenario was used to study potential memes within a real- life context. The Stanford Prison Experiment simulation was complemented by internal and external validity testing. The Hamariyah Iraqi village was used to analyze meme competition in a fictional village based upon US Marine Corps human terrain data. This simulation demonstrated how the implemented system can infer the personality traits and contextual factors that cause certain agents to adopt pro-US or anti-US memes, using Gaussian mixture clustering analysis and cross-cluster analysis. Finally, this research identified significant gaps in empirical science with respect to studying memes. These roadblocks and their potential solutions are explored in the conclusions of this work. Table of Contents Table of Contentsv List of Tables ix List of Figures xii 1 Introduction1 1.1 Studying Memetics Using the Systems Social Science Paradigm.. 2 1.2 Objectives................................ 5 1.2.1 Definition: Formally Defining Memes............. 6 1.2.2 Systems Model: Synthesis of Theories to Explains Meme Evolution............................ 6 1.2.3 Measurement: Identifying and Measuring Memes Empirically7 1.2.4 Implementation: Realizing the Memes Computationally.. 7 1.2.5 Usefulness: Applying the Model to Study Real-World Scenarios9 1.3 Summary ................................ 11 2 Defining Memes 12 2.1 The Debate on Meme Definition ................... 13 2.2 Memes as Information......................... 14 2.3 Memes as Operators.......................... 15 2.4 Recursive Reproduction of Memes .................. 17 3 The Meme Process: A Systems Model for Memes 19 3.1 Interaction with the