Planning for Everything (Besides Death and Taxes)

Adapting Policy Analysis for Uncertain Futures

By: Susan Dudley, Daniel R. Pérez, Brian Mannix, & Christopher Carrigan

April 03, 2019

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Policymakers frequently face demands to act now to protect against a wide range of future risks, and to do so without impeding economic growth. Yet, in some cases, traditional analytical tools—decision analysis, benefit-cost analysis (BCA), risk analysis, etc.—may be inadequate to support the analyst charged with modeling the relevant uncertainties and tradeoffs. Although some risks may be estimated using actuarial methods, others, particularly those with unknown probabilities and potentially severe and widespread consequences, may not.

For example, BCA has enjoyed both academic and bipartisan support as a valuable approach for examining different policy options and informing policy choices. Since at least the 1970s, it has been the preferred instrument for comparing incremental (marginal) alternatives and addressing discrete policy questions in isolation. As traditionally applied, however, BCA may be ill-equipped to cope with certain challenges now facing policymakers, including those associated with climate change, nuclear war, cyber-attacks against critical infrastructure, widespread natural disasters, global pandemics, and systemic financial crises.

Traditional methods of analysis that focus on marginal effects can break down when dealing with large irreversible changes, with policies that require large scale coordination to succeed or that only make sense on a global scale, in situations involving networked effects, or when confronting long time horizons. Furthermore, the likelihood that a particular problem will occur can be unknown or even unknowable. For these reasons, we refer to these issues as “uncertain futures.”

Marginal analysis is most valuable when examining one issue at a time. Experts who focus on a particular source of risk (e.g., war, climate change, cyber-terrorism, or financial system collapse) often consider policy approaches in isolation—the phenomenon of narrow framing.[1] However, committing vast resources to one problem may harm economic growth and make society less resilient and less able to cope with other (anticipated or unanticipated) events or challenges. Specialists in different policy areas, responding to the perceived crise du jour in their respective fields, may compete to bring attention to what each sees as the highest priority of the moment. That competition can become a “common pool” problem if it depletes resources and impairs an overall capacity to respond to the future as it unfolds.[2]

This does not mean that analysis is not critical to ensure policies support and enhance well-being. Rather, the diverse policy choices confronting decision-makers today call for broader frameworks that incorporate uncertainties and tradeoffs across policy decisions. More flexible and dynamic decision-analysis approaches that anticipate the need to learn from experience, and that encourage learning, are essential. To facilitate the development of methods for analyzing uncertain futures, the GW Regulatory Studies Center commissioned four papers[3] that explore, from different perspectives, analytical approaches to inform policies that address uncertain futures. The collective research output is premised on the idea that policy analysis of uncertain futures can benefit from cross-fertilization of ideas and interdisciplinary analytical tools.

In the first of these papers, Louis Anthony Cox, Jr. applies insights from machine learning—especially, deep multi-agent reinforcement learning—to reveal how incremental learning and improvement approaches (“muddling through”) can supplement and reinforce traditional decision analysis.[4] Second, Fred Roberts applies risk assessment to scenarios of terrorist attacks on critical infrastructure, including U.S. sporting venues and the international maritime transportation system. He finds that risk assessments of terrorist attacks traditionally treat physical and cyber attacks separately and, as a result, are inappropriate for considering the risks associated with combined attacks that include both a physical and cyber component. In response, he proposes a framework informed by expert judgement to determine whether an attacker would likely prefer executing a combined or traditional physical attack on a given target.[5]

In the third commissioned paper, James Scouras examines nuclear war as a global catastrophic risk and suggests that multidisciplinary studies combining insights from “historical case studies, expert elicitation, probabilistic risk assessment, complex systems theory, and other disciplines” can address many of the shortcomings of single analytic approaches. He argues that experts can address current gaps in their assessments of the consequences of nuclear weapons by further investigating understudied phenomena (e.g., the effects of electromagnetic pulse, nuclear winter, the cascading effects of nuclear war on the interdependent infrastructures that sustain societies).[6]

Finally, W. Kip Viscusi shows that adopting precautionary measures in the face of risk ambiguity can increase, rather than mitigate, the risk of adverse outcomes by undervaluing the information that can be gained through trial and error. Instead, policymakers should exploit risk ambiguity and opportunities for learning about uncertain risks—for example, by making incremental investments in the presence of irreversible effects. He also suggests that we resist the temptation to adopt prescriptive discounting procedures for temporally remote effects since standard discounting procedures without any ad hoc adjustment can properly weight future benefits and costs.[7]

This overview paper describes and illustrates what we mean by uncertain futures. It then lays out the challenges for anticipating these futures and developing more effective policies to address them. It concludes by discussing the advantages of policy approaches that learn from different disciplines and experimentation.

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[1]    Kahneman, Daniel (2003). “Maps of Bounded Rationality: Psychology for Behavioral Economics.” American Economic Review 93, no. 5: 1449–1475.

[2]    Ostrom, Elinor (1990). Governing the Commons: The Evolution of Institutions for Collective Action. Cambridge, UK: Cambridge University Press.

[4]    Cox, Louis Anthony Jr. (2019). “Decision Analysis, Muddling-Through, and Machine Learning for Managing Large-Scale Uncertain Risks.” Working Paper, the George Washington University Regulatory Studies Center. Available at: https://regulatorystudies.columbian.gwu.edu/decision-analysis-muddling-through-and-machine-learning-managing-large-scale-uncertain-risks.

[5]    Roberts, Fred S. (2019). “From Football to Oil Rigs: Risk Assessment for Combined Cyber and Physical Attacks.” Working Paper, the George Washington University Regulatory Studies Center. Available at: https://regulatorystudies.columbian.gwu.edu/football-oil-rigs-risk-assessment-combined-cyber-and-physical-attacks.

[6]    Scouras, James (2019). “Nuclear War as a Global Catastrophic Risk.” Working Paper, the George Washington University Regulatory Studies Center. Available at: https://regulatorystudies.columbian.gwu.edu/nuclear-war-global-catastrophic-risk.

[7]    Viscusi, W. Kip (2019). “Responsible Precautions for Uncertain Environmental Risks.” Working Paper, the George Washington University Regulatory Studies Center. Available at: https://regulatorystudies.columbian.gwu.edu/responsible-precautions-uncertain-environmental-risks.