Abstract
The analysis of policy impacts in a dynamic and uncertain reality is vital to supporting informed economic policy design and implementation. Dynamic, stochastic economic models used in policy evaluation necessarily simplify the world as we know it. This motivates us to explore, refine, and extend tools aimed at producing marginal valuations that shed light on why some policies are optimal and how others, though suboptimal, can be improved. We present representations of these marginal valuations that embrace uncertainty and support robust implementation-even in environments characterized by "deep uncertainties." These representations offer a more complete understanding of how interactions among multiple state variables, concerns about model misspecification, and uncertainties surrounding potentially long-term implications contribute to the cogent assessment of policies. We argue that these methods are particularly salient for evaluating the global cost of climate change and the global value of research and development with long-term prospects for success.