Integrated assessment (IA) considers interactions of physical, biological, and human systems in order to assess long-term consequences of environmental and energy policies such as limits on greenhouse gas emissions, and other strategies to negate climate change. Users of IA face the daunting task of interpreting large amounts of data and uncertainties. Multi-criteria decision-making (MCDM) methods can help users process IA data, understand policy tradeoffs, and learn how their value judgments affect decisions. We held a workshop during which climate change experts tested several MCDM methods for using IA outputs to rank hypothetical policies for abating greenhouse gas emissions. Participants also evaluated several methods for visualizing tradeoffs under both certainty and uncertainty cases. This paper explores potential roles for MCDM in IA identified during the workshop, along with implications for IA design and implementation. We summarize the workshops’ results regarding intertemporal discounting (a type of MCDM weighting judgment), visualization of impacts, how MCDM methods can help users to incorporate their background knowledge, and how MCDM can improve understanding of tradeoffs and the importance of value judgments. A key result is that the interest rates IA experts recommend for discounting future impacts depend strongly on what type of impact is being discounted, as well as upon the exact phrasing of questions used to elicit rates from the experts.