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
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|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
Publisher: Springer, Cham
Collection: Decision making under deep uncertainty
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
• 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.