Modeling Memes: A Memetic View of Affordance Learning
Agent Based Model
Artificial Intelligence and Robotics
Cognition and Perception
Other Ecology and Evolutionary Biology
Other Operations Research, Systems Engineering and Industrial Engineering
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.