Computational Social Science for Sustainability

Welcome!

Humans face an existential challenge to transition to sustainable practices that do not exhaust available ecological, economic, and social capital. This transition needs a rigorous, robust, and practical science of social behavior because it is difficult to predict the success of using limited educational or training resources to promote sustainability. It must be able to deductively estimate the costs and benefits of different intervention strategies by analyzing formal or computational models of interventions since different social contexts demand different designs.

Students in this course will develop their own formal and computational models of social behavior using the socmod library in R. This library integrates with igraph enabling researchers to create agent-based models with real-world social networks. By the end of the course, students will have gained practical technical skills in R programming and data science, a deeper understanding of how to apply computational tools to social science, and the ability to design research that addresses pressing sustainability challenges.

Resources