Modeling and Uncertainty Analysis for Sustainability and Policy
Complex models and data analysis often underlie sustainability-related actions by public and private actors. This course teaches students how to think about and conduct their own modeling and data analysis, with a focus on accounting for uncertainty. The course starts with a discussion of the promise and pitfalls of models. It then covers good modeling and data analysis practices. Most of the semester will delve into several tools useful for real-world applications, such as decision trees, scenario and sensitivity analysis, Monte Carlo analysis, optimization methods, agent-based modeling, and Markov Chains. This course will not provide a solid theoretical grounding in any of these techniques, but rather introduce them to students so they understand and can use them in the future. The course will intersperse lectures with classroom discussions on real-world examples and papers. No prerequisites are required.