Robust Cost-Benefit Analysis Under Deep Uncertainty: A Multi-Model Ensemble Approach Using Info-Gap Decision Theory

This research addresses the challenge of deep uncertainty where probability distributions of key parameters are unknown, limiting traditional CBA methods like Monte Carlo simulation. It introduces a framework combining multi-model ensembles with info-gap decision theory to develop "robust CBA" that performs adequately across a wide range of possible futures rather than optimizing for a single expected outcome. The methodology involves developing multiple independent models of the same policy problem using different assumptions, quantifying uncertainty tolerance using info-gap theory, and identifying "robustly optimal" policies that perform reasonably well across all models and uncertainty ranges. For example, in veterinary disease preparedness, the approach identifies investment levels providing acceptable protection across plausible outbreak scenarios. This framework shifts policymaking from fragile optimal solutions to resilient strategies maintaining value under unexpected futures.

References:

1. Incorporation of Cost-Benefit Analysis Considering Epistemic Uncertainty for Calculating the Optimal Design Flood. S. Kim, Cheol-Eung Lee (2021). Water resources management.
2. Hydropower development in the lower Mekong basin: alternative approaches to deal with uncertainty. I. Kubiszewski, R. Costanza, P. Paquet, Shpresa Halimi (2013). Regional Environmental Change.
3. A Cost–Benefit Analysis of Preparing National Veterinary Services for Transboundary Animal Disease Emergencies. W. Gilbert, David Adamson, D. Donachie, Keith Hamilton, J. Rushton (2023). Transboundary and Emerging Diseases.
4. The Uncertainty Problem in Cost-Benefit Analysis Expanded: A Current Review. Derek Linton (2024). Journal of economic impact.