Jan 1, 2003
Published in: Decision Making under Deep Uncertainty: From Theory to Practice, Chapter 2, pages 23-51. doi: 10.1007/978-3-030-05252-2_2
Posted on RAND.org on April 10, 2019
The quest for predictions—and a reliance on the analytical methods that require them—can prove counter-productive and sometimes dangerous in a fast-changing world. Robust Decision Making (RDM) is a set of concepts, processes, and enabling tools that use computation, not to make better predictions, but to yield better decision sunder conditions of deep uncertainty. RDM combines Decision Analysis, Assumption-Based Planning, scenarios, and Exploratory Modeling to stress test strategies over myriad plausible paths into the future, and then to 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 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. Frontiers for RDM development include expanding the capabilities of multi-objective RDM (MORDM), more extensive evaluation of the impact and effectiveness of RDM-based decision support systems, and using RDM's ability to reflect multiple world views and ethical frameworks to help improve the way organizations use and communicate analytics for wicked problems.