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Policymakers rely on quantitative systems models to inform decision-making about environmental policy design, infrastructure development, and resource allocation. However, many rapid, transformational changes in the climate and socioeconomic systems are difficult to predict and quantify in models. Therefore, reliance on traditional model-based decision analysis can leave policymakers vulnerable to unforeseen risks. In this class, students will learn quantitative methods for addressing deep uncertainties using systems modeling, enabling them to identify potential vulnerabilities and design decision policies that are robust and resilient to a wide range of uncertain futures. Drawing on tools in simulation, optimization, and machine learning, specific methods include: exploratory modeling, scenario discovery, robust decision making, and adaptation pathways. We will demonstrate these approaches in a range of sustainability domains such as water resources, agriculture, and energy systems. Students will complete Python-based modeling assignments, read contemporary journal articles, and develop a research proposal. Prerequisites: Prior coursework in applied optimization (e.g. CEE 266G or MS&E 211); and prior coursework in decision or policy analysis (e.g. CEE 275D or MS&E 250A or MS&E 252); and proficiency in Python programming at the level of CME 193