Abstract
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. 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.
Highlights
Toward the end of the Cold War, in the early 1980s, the RAND Corporation invested much effort toward using computer combat simulation models to inform national security decisions regarding weapons procurement and strategy
Frontiers for Robust Decision Making (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
RDM uses the domain criterion over the space of probabilities rather than the space of futures; that is, the analysis considers a set of plausible probability distributions over the futures (Walley 1991), and the domain criterion selects the strategy with the highest expected utility or expected regret over the largest number of distributions in the set
Summary
Abstract • 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. • 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 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. V. A. W. J. Marchau et al (eds.), Decision Making under Deep Uncertainty, https://doi.org/10.1007/978-3-030-05252-2_2
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