Developing RDM and Similar Methods

This page presents information about and links to several RDMlab research projects that focus on developing the methodology.

RDM Development

  • Robust Decision Making: Enabling Policymakers to Plan for the Future

    How do we know today which course of action will work tomorrow? To make long-term decisions, policymakers are often called upon to anticipate future needs, resources, and circumstances. Robust Decision Making is a major methodological innovation in the science of decision support that allows policymakers to plan for the future without having to predict it.

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    New Methods to Implement Multi-Attribute RDM

    Many objective robust decision making (MORDM) combines concepts and methods from many objective evolutionary optimization and robust decision making (RDM), along with extensive use of interactive visual analytics, to facilitate the management of complex environmental systems.

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    Using Scenario Elicitation Methodology to Map Possible Future Challenges to Mitigation and Adaptation

    A new set of scenarios, referred to as Shared Socio-economic Pathways (SSPs), examines challenges to mitigation and challenges to adaptation. Developing SSPs with a "backwards" approach could help inform the development of SSPs to ensure the storylines focus on the driving forces most relevant to distinguishing between the SSPs.

  • SCRiM Examines Sustainable Climate Risk Management

    An $11.9 million award from the NSF will establish a multi-institution research network on Sustainable Climate Risk Management strategies. RAND's Rob Lempert is the co-principal investigator of the network.

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    Robust Decision Making Aids Planning Under Deep Uncertainty

    Quantitative analysis is often indispensable to sound planning, but with deep uncertainty, predictions can lead decisionmakers astray. Robust Decision Making supports good decisions without predictions by testing plans against many futures.

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    Scenario Discovery Toolkit

    Scenarios provide an intuitively appealing means to communicate and characterize uncertainty in a variety of decision support applications. But a number of challenges can cause scenarios to fall short of their potential, especially in those public sector applications with diverse audiences. Scenario Discovery is a new analytic approach that addresses these challenges.

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    Testing the Scenario Hypothesis: The Effect of Alternative Characterizations of Uncertainty on Decision Structuring

    Decision support processes employing scenarios to characterize deep uncertainty may help contentious groups make more effective decisions than processes that characterize uncertainty using a single set of best-estimate probability distributions. Researchers are testing this hypothesis with an experiment using a fisheries model.