Agreeing on Robust Decisions: New Processes for Decision Making Under Deep Uncertainty

37 Pages Posted: 20 Apr 2016

See all articles by Nidhi Kalra

Nidhi Kalra

RAND Corporation

S. Hallegatte

World Bank

Robert Lempert

RAND Corporation - Santa Monica CA Offices; Rand Graduate School

Casey Brown

University of Massachusetts Boston

Adrian Fozzard

World Bank

Stuart Gill

World Bank - Poverty Reduction and Economic Management Network (PRMVP)

Ankur Shah

Ecole des Hautes Etudes en Sciences Sociales (EHESS) - Centre International de Recherche sur l'Environnement et le Développement

Date Written: June 1, 2014

Abstract

Investment decision making is already difficult for any diverse group of actors with different priorities and views. But the presence of deep uncertainties linked to climate change and other future conditions further challenges decision making by questioning the robustness of all purportedly optimal solutions. While decision makers can continue to use the decision metrics they have used in the past (such as net present value), alternative methodologies can improve decision processes, especially those that lead with analysis and end in agreement on decisions. Such "Agree-on-Decision" methods start by stress-testing options under a wide range of plausible conditions, without requiring us to agree ex ante on which conditions are more or less likely, and against a set of objectives or success metrics, without requiring us to agree ex ante on how to aggregate or weight them. As a result, these methods are easier to apply to contexts of large uncertainty or disagreement on values and objectives. This inverted process promotes consensus around better decisions and can help in managing uncertainty. Analyses performed in this way let decision makers make the decision and inform them on (1) the conditions under which an option or project is vulnerable; (2) the tradeoffs between robustness and cost, or between various objectives; and (3) the flexibility of various options to respond to changes in the future. In doing so, they put decision makers back in the driver's seat. A growing set of case studies shows that these methods can be applied in real-world contexts and do not need to be more costly or complicated than traditional approaches. Finally, while this paper focuses on climate change, a better treatment of uncertainties and disagreement would in general improve decision making and development outcomes.

Keywords: Climate Change Economics, Climate Change Mitigation and Green House Gases, Science of Climate Change, Transport Economics Policy & Planning, Debt Markets

Suggested Citation

Kalra, Nidhi and Hallegatte, Stephane and Lempert, Robert and Brown, Casey and Fozzard, Adrian and Gill, Stuart and Shah, Ankur, Agreeing on Robust Decisions: New Processes for Decision Making Under Deep Uncertainty (June 1, 2014). World Bank Policy Research Working Paper No. 6906. Available at SSRN: https://ssrn.com/abstract=2446310

Nidhi Kalra (Contact Author)

RAND Corporation ( email )

1776 Main Street
P.O. Box 2138
Santa Monica, CA 90407-2138
United States

Stephane Hallegatte

World Bank ( email )

1818 H Street NW
Washington, DC 20433
United States

Robert Lempert

RAND Corporation - Santa Monica CA Offices ( email )

P.O. Box 2138
1776 Main Street
Santa Monica, CA 90407-2138
United States

Rand Graduate School

Santa Monica, CA 90407
United States

Casey Brown

University of Massachusetts Boston

100 Morrissey Blvd
Boston, MA 02125
United States

Adrian Fozzard

World Bank ( email )

1818 H Street, NW
Washington, DC 20433
United States

Stuart Gill

World Bank - Poverty Reduction and Economic Management Network (PRMVP)

Washington, DC 20433
United States

Ankur Shah

Ecole des Hautes Etudes en Sciences Sociales (EHESS) - Centre International de Recherche sur l'Environnement et le Développement

45bis, avenue de la Belle Gabrielle
Nogent sur Marne CEDEX, 94736
France

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