Dynamic Benefit-Cost Analysis for Uncertain Futures

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

September 17, 2019

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ABSTRACT

“Uncertain futures” refers to a set of policy problems that possess some combination of the following characteristics: (i) they potentially cause irreversible changes; (ii) they are widespread, so that policy responses may make sense only on a global scale; (iii) network effects are difficult to understand and may amplify (or moderate) consequences; (iv) time horizons are long; and (v) the likelihood of catastrophic outcomes is unknown or even unknowable. These characteristics tend to make uncertain futures intractable to market solutions because property rights are not clearly defined and essential information is unavailable. These same factors also pose challenges for benefit-cost analysis (BCA) and other traditional decision analysis tools. The diverse policy decisions confronting decision-makers today demand “dynamic BCA,” analytic frameworks that incorporate uncertainties and trade-offs across policy areas, recognizing that: perceptions of risks can be uninformed, misinformed, or inaccurate; risk characterization can suffer from ambiguity; and experts’ tendency to focus on one risk at a time may blind policymakers to important trade-offs. Dynamic BCA – which recognizes trade-offs, anticipates the need to learn from experience, and encourages learning – is essential for lowering the likelihoods and mitigating the consequences of uncertain futures while encouraging economic growth, reducing fragility, and increasing resilience.

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This article is part of a working paper series convened by the GW Regulatory Studies Center. The other articles are also available from JBCA:

Responsible Precautions for Uncertain Environmental Risks - By: W. Kip Viscusi, Joel Huber, & Jason Bell

Nuclear War as a Global Catastrophic Risk - By: James Scouras

From Football to Oil Rigs: Risk Assessment for Combined Cyber and Physical Attacks - By: Fred S. Roberts

Muddling-Through and Deep Learning for Managing Large-Scale Uncertain Risks - By: Tony Cox