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.
Good policy analysis needs routinely to address not only uncertainty about model inputs, but uncertainty and disagreements about the model itself—especially for complex systems. In the future, analysis that fails to do so may be regarded as fatally flawed.
This paper explores various strategies and business models for developing and deploying artificial intelligence-enabled products.
For the purposes of capability development planning, RAND Project AIR FORCE focuses on analytical methods that can be used for decisionmaking under conditions of deep uncertainty about the future threat environment, rate of technological advancement, and budgeting. Such methods can also inform investment trades across warfighting domains and functional areas, as well as other responsibilities of the Air Force Warfighting Integration Capability.
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.
