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.

  • 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.

  • The Risks of Artificial Intelligence to Security and the Future of Work December 6, 2017

    This Perspective explores potential policy challenges ahead as artificial intelligence (AI) becomes more central in the private, commercial, and public spheres. It explores the implications of AI prevalence on two key policy-relevant areas: security and employment. Our focus was on highlighting the potential vulnerabilities and inequities that the use of AI imposes on these two dimensions of society.

  • Demonstrating the Applicability of a Robust Decision Making (RDM) to Conservation Decision-Making Under Uncertain Future Climate: Pilot Study Using the Northern Pygmy Salamander (Desmognathus Organi) October 3, 2017

    This study suggests initial ideas for managing climate uncertainty in conservation planning. Differences with previous RDM applications include focus on finer scale geography and significantly more uncertainty in the system (species response) model.

  • Robust Stormwater Management in the Pittsburgh Region: A Pilot Study April 24, 2017

    This report provides an independent study of how the stormwater problem in the Pittsburgh, Pennsylvania metropolitan region could grow with future climate, land use, or population change, and discusses potential long-term solutions using new analytical approaches developed by RAND. The analysis provides a baseline of scientific information intended to support ongoing regional coordination around stormwater management and water-quality planning.

  • An Intelligence in Our Image: The Risks of Bias and Errors in Artificial Intelligence April 5, 2017

    Machine learning algorithms and artificial intelligence influence many aspects of life today and have gained an aura of objectivity and infallibility. The use of these tools introduces a new level of risk and complexity in policy. This report illustrates some of the shortcomings of algorithmic decisionmaking, identifies key themes around the problem of algorithmic errors and bias, and examines some approaches for combating these problems.