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.
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.
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.
This study examines climate change and water resources management challenges facing water suppliers in drought-prone regions.
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.
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.
The teaching and conduct of policy analysis needs updating to account better for the special challenges of social problems occurring in complex adaptive systems.
The coronavirus disease 2019 (COVID-19) pandemic required significant public health interventions from local governments. To help in those decisions, researchers used the Robust Decision Making approach to stress-test California's COVID-19 reopening strategy. This Perspective presents lessons learned from these experiments and outlines four characteristics of the best strategies.
This article is a commentary on a paper by Lustick and Tetlock (2021), on forecasting, decisionmaking, and the intelligence community.
