On Balance: Dynamic Benefit-Cost Analysis for Uncertain Futures

Policymakers are called to act in the present to protect the public against future risks while operating under the constraints of doing so in an economically efficient yet effective manner. Unfortunately, these risks tend to present the most difficulty for traditional analytical tools in support of policymaking. This post describes a recently published article, “Dynamic Benefit-Cost Analysis for Uncertain Futures,” co-authored with Susan E. Dudley, Brian F. Mannix, and Christopher Carrigan as part of the open access Symposium on Analysis for Uncertain Futures in the Journal of Benefit Cost Analysis. The article explores the challenges “uncertain futures” pose for analytical tools to satisfactorily support policymaking and proposes the use of “dynamic benefit-cost analysis” frameworks as a necessary approach to address these challenges.


Benefit-cost analysis has enjoyed widespread support as a useful framework for comparing the effects of different policy options—partly due to its ability to incorporate uncertainty surrounding estimates into ex ante policy analysis (Boardman et al. 2011). As traditionally applied, benefit-cost analysis relies on the assumption that it is appropriate to estimate the marginal effects of discrete policy proposals in isolation. However, this marginal approach may break down when addressing issues such as climate change, nuclear war, or cyber-attacks against critical infrastructure.

We refer to such issues as “uncertain futures.” These problems appear to be intractable because of some combination of the following characteristics:

  • They potentially cause irreversible changes;

  • They are widespread, so that policy responses may make sense only on a global scale;

  • Network effects are difficult to understand and may amplify (or moderate) consequences;

  • Time horizons are long; and

  • The likelihood of catastrophic outcomes is unknown or even unknowable.

Although policy decision tools like benefit-cost analysis incorporate some degree of uncertainty about the future, a combination of the aforementioned characteristics presents greater analytical hurdles. For instance, policy prescriptions to invest in hardening physical infrastructure against terrorist attacks might increase the risk of cyber terrorism—with a highly uncertain net effect unless both issues are jointly considered.

Policymaking that effectively addresses uncertain futures demands the use of dynamic benefit-cost analysis to address these challenges by building on the successes of “standard” benefit-cost analysis (Weitzman 2011) Such approaches facilitate interdisciplinary learning and prioritize opportunities for experimentation with the goal of incrementally generating evidence in support of future policymaking. Our article presents dynamic benefit-cost analysis as a framework that prioritizes the consideration of alternatives that expand learning and experimentation; it is not a mandate to apply a particular methodological approach (e.g., requiring the use of general equilibrium modeling).

For instance, we describe the advantages of using a “real options” approach to address the uncertain future of climate change. Here, policy analysis considers the value of options that minimize initial investments in mitigation while allowing decision makers to build on the initial investment, if necessary, as they gather more evidence about the future harm. Prioritizing the consideration of policy interventions like a real options approach fits squarely within a dynamic benefit-cost analysis mindset.

Consistent with this idea, Linquiti and Vonortas (2012) compared alternative approaches to building barriers to protect communities against storm surges. They found that smaller up-front investments in storm wall height which included built-in options to extend the height in the future offered greater net social benefits relative to static decision-making. In short, considering flexible options that prize future learning and adaptation often result in more efficient and effective public policy to address uncertain futures. The authors’ findings highlight the advantages of applying a dynamic benefit-cost mindset to address uncertain futures.

Our article also details the added complexity in conducting policy analysis intended to model risks posed by intelligent adversaries (e.g., terrorism, the use of nuclear weapons). Here, the strategic, responsive, and adaptive behavior of actors generates additional difficulty (and potentially greater uncertainty) for analysts to consider. As our article describes in greater detail, policy analysis can be improved by extending long-standing decision-making principles with a focus on interdisciplinary collaboration and more careful consideration of network effects (i.e., the connection between physical and cyber vulnerabilities to terrorist attacks).

In addition to our own research, the Symposium on Analysis for Uncertain Futures contains four articles with insights from leading experts in different fields as part of a project supported by the GW Regulatory Studies Center to produce interdisciplinary scholarship to address uncertain futures. Drafts of these manuscripts were jointly presented as part of a Presidential Session during the 2019 Society for Benefit-Cost Analysis Annual Conference & Meeting. The articles are intended as a first step toward greater use of dynamic benefit-cost analysis. Our hope is that this research supports policymaking that lowers the likelihood and mitigates the consequences of uncertain futures while encouraging economic growth and increasing resilience.

Note: This article has been selected as the article of the month for October.

Boardman, Anthony E., David H. Greenberg, Aidan R. Vining, and David L. Weimer. 2011. Cost-Benefit Analysis: Concepts and Practice. Vol. 4. Upper Saddle River, NJ: Prentice Hall.
Climate Change Economics, 3(2): 801-833.
Weitzman, Martin L. 2011. “Fat-Tailed Uncertainty in the Economics of Catastrophic Climate Change.” Review of Environmental Economics and Policy, 5(2): 275-292.
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