Nye, Benjamin D
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Publication Social Learning and Adoption of New Behavior in a Virtual Agent Society(2013-01-01) Nye, Benjamin D.; Silverman, Barry G.Social learning and adoption of new behavior govern the rise of a variety of behaviors: from actions as mundane as dance steps to those as dangerous as new ways to make IED detonators. However, agents in immersive virtual environments lack the ability to realistically simulate the spread of new behavior. To address this gap, a cognitive model was designed that represents well-known socio-cognitive factors of attention, social influence, and motivation that influence learning and adoption of a new behavior. To explore the effectiveness of this model, simulations modeled the spread of two competing memes in Hamariyah, an archetypal Iraqi village developed for cross-cultural training. Diffusion and clustering analyses were used to examine adoption patterns in these simulations. Agents produced well-defined clusters of early versus late adoption based on their social influences, personality, and contextual factors such as employment status. These findings indicate that the spread of behavior can be simulated plausibly in a virtual agent society and has the potential to increase the realism of immersive virtual environments.Publication Social Learning and Adoption of New Behavior in a Virtual Agent Society(2013-01-01) Nye, Benjamin D; Silverman, Barry GSocial learning and adoption of new behavior govern the rise of a variety of behaviors: from actions as mundane as dance steps to those as dangerous as new ways to make IED detonators. However, agents in immersive virtual environments lack the ability to realistically simulate the spread of new behavior. To address this gap, a cognitive model was designed that represents the well-known socio-cognitive factors of attention, social influence, and motivation that influence learning and the adoption of a new behavior. To explore the effectiveness of this model, simulations modeled the spread of two competing memes in Hamariyah, an archetypal Iraqi village developed for cross-cultural training. Diffusion and clustering analyses were used to examine adoption patterns in these simulations. Agents produced well-defined clusters of early versus late adoption based on their social influences, personality, and contextual factors, such as employment status. These findings indicate that the spread of behavior can be simulated plausibly in a virtual agent society and has the potential to increase the realism of immersive virtual environments.Publication Modeling Memes: A Memetic View of Affordance Learning(2011-05-16) Nye, Benjamin DThis 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.