Robust Decision Making (RDM)

Application to Water Planning and Climate Policy

Published in: Decision Making under Deep Uncertainty: From Theory to Practice, Chapter 7, pages 135-163. doi: 10.1007/978-3-030-05252-2_7

Posted on RAND.org on April 10, 2019

by David G. Groves, Edmundo Molina-Perez, Evan Bloom, Jordan R. Fischbach

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Long-term water planning is increasingly challenging, since hydrologic conditions appear to be changing from the recently observed past and water needs in many locales are highly relative to existing and easily developed supplies. Globally coordinated policymaking to address climate change also faces large uncertainties regarding underlying conditions and possible responses, making traditional prediction-based planning approaches to be inadequate for the purpose. Methods for Decision Making under Deep Uncertainty (DMDU) can be useful for addressing long-term policy challenges associated with multifaceted, nonlinear, natural and socio-economic systems. This chapter presents two case studies using Robust Decision Making (RDM). The first describes how RDM was used as part of a seven-state collaboration to identify water management strategies to reduce vulnerabilities in the Colorado River Basin. The second illustrates how RDM could be used to develop robust investment strategies for the Green Climate Fund (GCF)—an international global institution charged with making investments supporting a global transition toward more sustainable energy systems that will reduce GHG emissions. Both case studies describe the development of robust strategies that are adaptive, in that they identify both near-term decisions and guidance for how these responses should change or be augmented as the future unfolds.

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