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.
This report describes RAND's Multi-Purpose Assessment of Force Flow (MPAFF) tool for conducting quick, first-order time-phased analysis of force sufficiency under a variety of assumptions on force generation policies, readiness policies, and force employment policies for the U.S. Army. This tool enables exploring the effect of policy changes on force generation.
This is a peer review of Robust Decision Making, an exploratory modeling approach for informing decisions under deep uncertainty, and the Exploratory Analysis and Modeling Tool, focusing applications for the Sacramento Area Council of Governments.
Mexico's third-largest metropolitan area, Monterrey, faces future water security challenges as the region grows. Tecnológico de Monterrey and RAND Corporation researchers used RAND's Robust Decision Making (RDM) framework to organize an analysis of long-term trends and vulnerabilities in water management in Monterrey and to help design a long-term water strategy.
This study evaluates the performance of Lima, Peru's current drought management plan against future droughts and proposes augmentations, and it recommends ways to ensure that the drought management plan performs will in the uncertain future.
This open access book focuses on both the theory and practice associated with the tools and approaches for decisionmaking in the face of deep uncertainty.
Decisionmakers often seek predictions about the future to inform policy choices. But a reliance upon analytic methods that require them can prove counter-productive and sometimes dangerous in a fast-changing, complex world.
This chapter presents two case studies that demonstrate how DMDU methods, in particular Robust Decision Making (RDM) (see Chap. 2), can help develop robust long-term strategies.
This chapter seeks to provide a thematic connection between the theory and application chapters that preceded it and the emerging needs of public policy decisionmaking processes.
