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Lempert, R. J. (2019). Robust decision making (rdm). In Decision making under deep uncertainty (pp. 23–51). Springer, Cham. |
| Resource type: Book Article BibTeX citation key: Lempert2019 View all bibliographic details  |
Categories: Artificial Intelligence, Cognitive Science, Complexity Science, Computer Science, Data Sciences, Decision Theory, General, Mathematics, Military Science Subcategories: Autonomous systems, Big data, Chaos theory, Decision making, Deep learning, Forecasting, Game theory, Human learning, JADC2, Machine learning, Machine recognition, Markov models, Military research, Q-learning, Systems theory Creators: Lempert Publisher: Springer, Cham Collection: Decision making under deep uncertainty |
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| Abstract |
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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 effective- ness of RDM-based decision support systems, and using RDM’s ability to reflect multiple world views and ethical frameworks to help improve the way organiza- tions use and communicate analytics for wicked problems. |