Computational Social Science for Sustainability

Catalog description

Computational techniques are essential tools for understanding how people can work toward and achieve sustainable societies. Social systems are inherently complex, with individual decisions strongly contingent on an individual's social position and history of social interactions. People are embedded in social networks, which constrain with whom they interact and, as a consequence, what information they consume. In this class, we will combine rigorous social science theory with computational techniques to enable us to understand and predict individual-level decision making and the behavior of collectives such as states, institutions, and socioecological systems more generally. Computational social science helps us strategically simplify collective social phenomena into manageable, focused computer models of behavior change, opinion change and political polarization, and cooperation on shared long-term goals. This approach develops and analyzes models of individual-level psychology interacting with group memberships that constitute social networks to result in collective social phenomena. This course will introduce students to computational methods for simulating and measuring such collective social phenomena, including cultural evolutionary dynamics and agent-based simulation models, evolutionary game theory, opinion dynamics modeling and measurement, and the analysis of social networks. Students will learn highly transferable software-development skills that support any variety of computational or analytical work, including using Git version control and GitHub for open-source development; and the R programming language, RStudio integrated development environment (IDE), and the Shiny library for making data analytics dashboards and web apps in R.