SP&E 316 - Computational & Agent-Based Modeling

Whether the recent COVID pandemic, resulting economic shocks, increasing social inequality or the changing geo-technology landscapes of the 21st century, complex behavior surrounds us. Data, evidence and theory informed scientific understanding and decision-making offers promise for some tractable solutions to such challenges. However, complex human, social, cultural and behavioral phenomena test researchers and practitioners alike to solve ‘wicked problems’ we face. This begs the question: how can we operationalize decades of hard won, parsimonious social science theory and evidence building for actionable insights into the messy, dynamic real world of deep policy contexts given high dimensional uncertainty often at the limits of predictability and our understanding? Computational social science is one such new frontier for addressing such human, social, cultural and behavioral challenges. Instead of limiting our inquires by our theories and traditional techniques, computational social science explicitly embraces the increasing complexity, interconnectivity and uncertainty in the anthropocene era to search for new answers and possible solutions. Traditional stochastic or econometric models of human behavioral phenomena are being supplanted by dynamic simulation methods drawn from multidisciplinary perspectives including politics, economics, sociology, business as well as the physical sciences. For researchers and practitioners alike, no longer is it sufficient to rely on one particular methodology or discipline. Moreover, the proliferation of computing power has made computational approaches widely available making modeling and simulation a third approach in the scientific enterprise, complimenting traditional theory building, experiments and empirical evidence. Today, teams of scientists are banding together to produce new computational models of ‘wicked problems’ and complex phenomena. This course is the foundational introduction to computational social science, complex adaptive systems and agent based modeling that attempts to address some of society’s latest challenges. Agent based models offer a new methodological bridge across various theoretical disciplines to extending Noble laureate Thomas Schelling’s notions of the micro motivations for macro behavior. Understanding how micro-individuals act, react and interact in meso social contexts lead to the macro political, economic and social structures we live in that feedback to constrain or incentivize individual actions. More importantly, they allow us to model, simulate and text complex phenomena for policymakers, researchers and practitioners alike where many equation based modeling approaches have difficulty. Agent based models allow for various transdisciplinary theories and cross scale, micro-meso-macro human activity approaches to be tested in a mutually inclusive environment, focusing on understanding real world behavior through connectivity, interdependence, emergence, dynamics, self-organization and co-evolution among many other complexity phenomena. The goals of this course are to survey computational social science fundamentals, complex adaptive systems and agent based modeling’s methodological foundations applied across several social science disciplines so students can build their own computational and ABMs. We first introduce the core concepts and methods of computational, complexity science and agent based modeling, then begin our journey through basic to advanced modeling and simulation best practices using NetLogo, supported by applied articles across political, economic and social behavioral domains.
Units: 4
Course Type: Seminar