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.

  • 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.

  • Testing the Scenario Hypothesis: An Experimental Comparison of Scenarios and Forecasts for Decision Support in a Complex Decision Environment March 16, 2017

    Decision support tools are known to influence and facilitate decisionmaking through the thoughtful construction of the decision environment. However, little research has empirically evaluated the effects of using scenarios and forecasts.

  • Evaluation of the Jinan City Water Ecological Development Implementation Plan and Recommendations for Improvement March 9, 2017

    RAND evaluated potential effects of uncertain projections of demand and climate change on the ability of the Jinan Municipal Water Resources Bureau to meet its long-term water resources goals. This document describes RAND's approach and results, including development of a mathematical simulation model of the Jinan water supply system and analysis of the system's performance if new strategies and investments were to be implemented.