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.

  • Robust Decision Making (RDM) April 10, 2019

    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.

  • Robust Decision Making (RDM): Application to Water Planning and Climate Policy April 10, 2019

    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.

  • Reflections: DMDU and Public Policy for Uncertain Times April 10, 2019

    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.

  • Do Differing Analyses Change the Decision? Using a Game to Assess Whether Differing Analytic Approaches Improve Decisionmaking April 8, 2019

    The decision analysis community faces obstacles in moving new methods, tools, and paradigms from theory to practice, partly because of the difficulties in demonstrating value proposition. In this report, RAND researchers use a structured comparison game to examine the value proposition of different analytic inputs (scenario-based versus Robust Decision Making) on a sample U.S. Department of Defense decision about force structure.

  • Bezos World or Levelers: Can We Choose Our Scenario? March 28, 2019

    This essay explores how AI might be used to enable fundamentally different future worlds and how one such future might be enabled by AI algorithms with different goals and functions than those most common today.

  • Resilience of the Eastern African Electricity Sector to Climate Driven Changes in Hydropower Generation January 29, 2019

    This study developed a framework consisting of long-term models for electricity supply and water systems management, to assess the vulnerability of potential electricity infrastructure expansion plans to the effects of climate change in Africa.

  • Deep Decarbonization as a Risk Management Challenge October 22, 2018

    As governments, organizations, businesses, and other institutions pursue deep decarbonization with the goal of reducing net human greenhouse gas emissions to zero by the second half of the 21st century, they will find risk management central to the challenge. This Perspective explores three concepts that are particularly important to the implementation of risk management for deep decarbonization -- risk governance, complexity, and robustness.

  • Priority Challenges for Social and Behavioral Research and Its Modeling April 16, 2018

    Modeling and simulation, if well rooted in social-behavioral (SB) science, can inform planning about some of the most vexing national problems of our day. Unfortunately, the current state of SB modeling and related analysis is not yet up to the job. This report diagnoses the problems, identifies the challenges, and recommends ways to move ahead so that SB modeling will be more powerfully useful for aiding decisionmaking.