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.
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.
El presente estudio demuestra la utilidad del enfoque de Toma de Decisiones Robustas para evaluar la vulnerabilidad y las oportunidades de adaptación de la gestión de los recursos hídricos en Mendoza, Argentina.
This work presents a method for applying high-spatial resolution population-based scenarios to pre-existing stormwater models. We explore the impact of two long-term population growth scenarios for the Pittsburgh region.
Costa Rica's National Decarbonization Plan sets the ambitious goal for the country to become carbon-neutral by 2050. The authors of this report applied a novel methodology for planning under deep uncertainty to evaluate whether the benefits of the plan exceed its costs. They find that under the vast majority of plausible assumptions about the future, the plan would achieve or nearly achieve its goals and do so at a net economic benefit.
This brief describes a proposed system of green stormwater infrastructure (GSI) in Pittsburgh's Negley Run watershed, evaluates its potential benefits and costs, and presents recommendations to improve urban stormwater management.
Urban stormwater management is a growing challenge in many U.S. cities, and climate change is expected to add to this challenge. RAND researchers apply simulation modeling and economic valuation to estimate the potential benefits and costs of a green stormwater infrastructure (GSI) system in Pittsburgh's Negley Run watershed. The authors evaluate a series of GSI investments and make recommendations for how local actors should proceed.
This study builds on recent advances in methods and tools for decisionmaking under deep uncertainty to demonstrate analytic approaches that help reduce the tension between quantitative decision support tools and approaches for addressing wicked problems.
