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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. Prerequisite: EBS 123

Adaptation is the process by which organisms or societies become better suited to their environments. In this class, we will explore three distinct but related notions of adaptation. Biological adaptations arise through natural selection, while cultural adaptations arise from a variety of processes, some of which closely resemble natural selection. A newer notion of adaptation has emerged in the context of climate change where adaptation takes on a highly instrumental, and often planned, quality as a response to the negative impacts of environmental change. We will discuss each of these ideas, using their commonalities and subtle differences to develop a broader understanding of the dynamic interplay between people and their environments. Topics covered will include, among others: evolution, natural selection, levels of selection, formal models of cultural evolution, replicator dynamics, resilience, rationality and its limits, complexity, adaptive management.

Under supervision of a Social Science Division faculty member on a subject of mutual interest.

The Climate Cognition Class is a graduate level course offered by Prof. Madalina Vlasceanu in the Environmental Social Sciences Department of the Stanford Doerr School of Sustainability. The course will focus on the insights drawn from research in cognitive, social, and environmental psychology relevant to understanding climate beliefs and behaviors. From low level processes such as perception, memory, emotions to the higher order processes involved in judgment and decision making, to macro level emergent phenomena leading to social movements and policy change, this course covers research on the psychological factors behind climate action at the individual, collective, and systemic level. Throughout the quarter, we will be hearing from numerous experts, overview the field, and discuss new empirical research, with the goal of identifying promising future research directions.

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. Prerequisite: EBS 123

Under supervision of a Social Science Division faculty member on a subject of mutual interest.

Focuses on the relations between education and sustainable development from a comparative cross-national perspective. The course covers questions and debates around education for sustainable development and the nature of "the global"; global influences on national institutions of sustainable development; and key themes in the cross-national study of education for sustainable development such as stratification and achievement, gender, human rights, and the global authority of science and experts.