Publications

RAND has contributed more to thinking about how to deal with the longer-range future than any other organization. The methodological work at RAND started in the late 1950s and 1960s with the developments of the Delphi method and scenario analysis. This work continued sporadically through the 1970s and 1980s—and since the 1990s—computational technology has enabled new futures methodologies such as Robust Decision Making and Scenario Discovery.

RAND is again leading the way in putting these methods to use in dealing with the challenges of the future. Below is a list of publications that exemplifies RAND's work on Robust Decision Making methodology and applications.

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  Decision Aid Framework Development to Inform the Application of Federal Assistance: Improving Sharing of Criminal History Record Information for Personnel Vetting May 3, 2023

  Researchers identified factors that inhibit the provision of criminal history record information to the Defense Counterintelligence and Security Agency (DCSA) from law enforcement agencies and developed a decision aid framework that allows DCSA to prioritize these organizations according to DCSA goals when assessing which localities might benefit from targeted federal assistance.

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  Planning Tool Methods and Results: Attachment G1 April 4, 2023

  As part of the 2023 Coastal Master Plan, the Planning Tool was updated to use more detailed modeling data, including a structure-based asset inventory, and to respond to new CPRA planning priorities.

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  Optimizing Portfolio-Level Modernization Investment: An Overview of the Aim Point Investment Model (APIM) March 27, 2023

  This report introduces the Aim Point Investment Model, which allocates portfolio-level resources based on programs' utility and existing Army data sources, subject to constraints on the U.S. Army's ability to employ those resources productively. This model allows decisionmakers to quickly understand the implications of changing operational prioritization and budget constraints in terms of real effects on actual program-level funding.

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  A New Ethical Principle for Analysts Who Use Models March 3, 2023

  Analysts have long embraced the ethic of revealing assumptions that underlie their conclusions. Analysts should preemptively explain how their conclusions vary with changes of important assumptions reflecting uncertainties and disagreements.

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  Machine Learning in Public Policy: The Perils and the Promise of Interpretability November 15, 2022

  Machine learning (ML) can have a significant impact on public policy by modeling complex relationships and augmenting human decisionmaking. However, overconfidence in results and incorrectly interpreted algorithms can lead to peril, such as the perpetuation of structural inequities. In this Perspective, the authors discuss the importance of ML interpretability and offer recommendations that will help lead to better policy decisions.

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  Adaptive Water Management in the Face of Uncertainty: Integrating Machine Learning, Groundwater Modeling and Robust Decision Making December 21, 2021

  This study examines climate change and water resources management challenges facing water suppliers in drought-prone regions.

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  Recalibrating the Belt and Road Initiative Amidst Deep Uncertainties December 9, 2021

  We describe the deep uncertainties affecting Belt and Road Initiative decision making, and identify drawbacks of existing BRI policy analysis in light of its future challenges.

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  Reopening California: Seeking Robust, Non-Dominated COVID-19 Exit Strategies November 5, 2021

  This paper uses simulation models and the Robust Decision Making (RDM) approach to stress-test California's reopening strategy and other alternatives over a wide range of futures.

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