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 decisions under 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. RDMembeds analytic tools in a decision support process called “deliberationwith 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 highlightsRDM’s approach to adaptive strategies, agent-based modeling, and complex systems. Frontiers for RDM development include expanding the capabilities of multiobjective 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.
CITATION STYLE
Lempert, R. J. (2019). Robust Decision Making (RDM). In Decision Making under Deep Uncertainty: From Theory to Practice (pp. 23–52). Springer International Publishing. https://doi.org/10.1007/978-3-030-05252-2_2
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