A Connectionist Modeling Approach to Rapid Analysis of Emergent Social Cognition Properties in Large-Populations
Abstract
Traditional modeling methodologies, such as those based on rule-based agent modeling, are exhibiting limitations in application to rich behavioral scenarios, especially when applied to large population aggregates. Here, we propose a new modeling methodology based on a well-known ‘connectionist approach,’ and articulate its pertinence in new applications of interest. This methodology is designed to address challenges such as speed of model development, model customization, model reuse across disparate geographic/cultural regions, and rapid and incremental updates to models over time.
[Pub 2]
Kalyan Perumalla
Kalyan Perumalla is a computer scientist focused on research in supercomputing, quantum computing, and artificial intelligence, as research staff member, faculty, and program manager with the U.S. government, national labs, and universities. As a Federal Program Manager in Advanced Scientific Computing Research at the U.S. Dept. of Energy, Office of Science, He managed a $100-million R&D portfolio covering AI, HPC, Quantum, SciDAC, and Basic Computer Science. In his 25-year R&D leadership experience, he previously led advanced R&D as Distinguished Research Staff Member at the Oak Ridge National Laboratory (ORNL) developing scalable software and applications on the world’s largest supercomputers for 17 years, including as a line manager and a founding group leader. He has held senior faculty and adjunct appointments at UTK, GT, and UNL, and was an IAS Fellow at Durham University.