One goal of climate science is often seen as reducing uncertainty. But the quest for predictions—and a reliance on the analytical methods that require them—can prove counterproductive and sometimes dangerous when addressing wicked problems such as climate change. Robust decision-making (RDM), one important method for decision-making under deep uncertainty (DMDU), is a set of concepts, processes, and enabling tools for multiscenario, multiobjective analysis aimed not to make better predictions, but to yield better decisions under conditions of deep uncertainty. RDM combines decision analysis, assumption-based planning, scenarios, and exploratory modeling methodologies to stress test strategies over myriad plausible paths into the future, and then identify policy-relevant scenarios and robust adaptive strategies. RDM embeds analytic tools in a decision support process called “deliberation with analysis” that promotes learning and consensus-building among stakeholders. The goal is to help those facing climate-related decisions to manage uncertainty through their choice of action rather than relying on science to reduce all relevant uncertainties. The chapter demonstrates an RDM approach to identifying a robust mix of policy instruments—carbon taxes and technology subsidies—for reducing greenhouse gas emissions. The example also highlights RDM’s approach to adaptive strategies, agent-based modeling, and complex systems.