Providing Decision Support for Adaptive Strategies using Robust Decision Making: Applications in the Colorado River Basin
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Climate change in the American West, including the Colorado River Basin, presents a new policy problem for natural resource planners. A wide range of conditions may unfold over an extended time horizon but scientists, stakeholders, and planners have not formed consensus regarding what the future may hold. Increasingly, water agencies recognize that adaptive strategies, designed to evolve in response to new information, may yield better solutions for climate change than strategies not designed to take advantage of learning. Planners still must make choices among near-term actions, contingencies, and responses to new information. As many choices are deferred until later, these strategies require a framework in which planners can integrate new information into an analytic process that supports on-going deliberations. Effective decision support is thus necessary to support the development of adaptive strategies, as planners consider near-term actions and prepare for future deliberations.
In this dissertation, I examine how Robust Decision Making (RDM) can provide decision support to planners as they create, evaluate, and deliberate about adaptive strategies. I provide a discussion of the structure of adaptive strategies, the choices that planners face when crafting an adaptive strategy, and the role that RDM can play in supporting planners' decisionmaking. I present a model describing RDM's application to policy contexts with multiple time-periods for decisionmaking and a mathematical definition of adaptive strategies. I then provide a policy application, extending a recent analysis for the Colorado River Basin Study. This analysis first explores choices that planners may make when considering how to respond to new information and tradeoffs between alternative responses. I also generate planning scenarios, identifying the conditions under which specific near-term actions or contingencies are necessary and long-term implementation schedules perform well. Finally, I propose a naïve-Bayes' model to assist planners in integrating new information with their current beliefs, providing guidance on what information in the next decade may cause them to adjust the strategy.
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This document was submitted as a dissertation in December 2014 in partial fulfillment of the requirements of the doctoral degree in public policy analysis at the Pardee RAND Graduate School. The faculty committee that supervised and approved the dissertation consisted of Rob Lempert (Chair), David Groves, and Craig Bond.
This publication is part of the RAND Corporation Dissertation series. Pardee RAND dissertations are produced by graduate fellows of the Pardee RAND Graduate School, the world's leading producer of Ph.D.'s in policy analysis. The dissertations are supervised, reviewed, and approved by a Pardee RAND faculty committee overseeing each dissertation.
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