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 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.
Robust Decision Making offers a promising complement and possibly future alternative to current prediction-based and scenario approaches that could help planners better manage in today's conditions of fast-paced change.
To address concerns that warfare could extend to space, the national security space community has taken steps to enhance space mission assurance (SMA), leading to an increasing demand for assessments of SMA. The authors describe decisionmaker needs for assessments of SMA, challenges for conducting assessments, the shortfalls that may result from the challenges, and options for addressing the shortfalls.
Robust decisionmaking (RDM) is a method for aiding decision making under deep uncertainty that uses models not as predictors but as generators of cases exploring assumptions and outcomes.
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.
