Agent-based modeling

Catalog description

Developing sustainable societies requires that we can predict the possible outcomes of policies and interventions aimed at spreading sustainable practices. Such prediction requires an understanding of how behaviors are learned socially within modern, complex social networks. Agent-based modeling is an important computational technique for predicting how sustainable behaviors might be transmitted via social learning, diffusing through social networks, under stochastic and uncertain conditions. Agents are simulated people. Agents have specified psychological characteristics, such as how likely a person is to adopt a more sustainable behavior based on a person's demographics, socio-economic status, and beliefs, among other factors. Agent psychology is based on published psychological research. In this course, students will learn how to construct agent-based models of the transmission of sustainable behaviors under different ecological, social, and uncertainty conditions. Students will strengthen their knowledge of psychology, evolutionary anthropology, sociology, and network science that we use to motivate model assumptions. Students will learn transferable technical skills, including high-performance computing techniques to run millions of simulations at the same time to generate a range of outcomes across a range of contexts. Students will also learn transferable data science skills used to analyze large, complex agent-based model outputs. Students will program agent-based models using the R programming language, the RStudio integrated development environment (IDE), and create interactive agent-based models in a web dashboard using the Shiny library for R. Students will use Git for version control, and GitHub for open-source software development.