(Groves and Lempert 2007, Groves et al. 2008a, 2008b, 2008c, 2008d). It has been a useful framework for developing robust climate adaptation strategies for water agencies. Key challenges to deploying RDM include retooling existing models to be evaluated many more times than is typical, deploying new and often unfamiliar statistical approaches for identifying vulnerabilities, and ensuring that decision makers and stakeholders understand the new approach.
RDM proceeds through a series of steps that can be customized depending on the application. In the first step, analysts, often in conjunction with stakeholders and decision makers, specify the key uncertain exogenous factors (X) that are likely to be disputed by different parties to the decision, draw up a list of policy levers (L) that comprise strategies, identify measures (M) to consider when evaluating policy outcomes, and identify models and/ or relationships (R) that relate the uncertainties and strategies to outcomes. The resulting information, termed an “XLRM” chart, is used to assemble the quantitative models to be used to evaluate the performance of strategies under many alternative scenarios.
The resulting analysis is not used to identify a single “optimal” strategy. Instead, one or a few strategies are identified for a structured evaluation of their performance against a wide array of plausible scenarios (steps 2 and 3). In the fourth step, statistical tools are used to identify the key vulnerabilities, or sets of assumptions that lead the proposed strategy to fail. These vulnerabilities thus represent future conditions (or scenarios) that are critically important to the choice of strategies—they are the conditions that might lead the promising strategy to perform poorly. Under these conditions, alternative strategies would be preferred. The trade-offs among alternatives under these vulnerable conditions can be helpful in identifying new hedging options that can then be used to develop more robust strategies. These more robust strategies are then evaluated as before. Through iteration, RDM helps the analyst explore across a broad range of possible strategies without requiring the contentious specification of uncertain future parameters. The strategies identified become more robust, thus reducing the sensitivity of the strategy’s performance to the key uncertainties.
In contrast to probabilistic assessments, which typically provide rankings of strategies based on a set of underlying assumptions about climate change, RDM identifies the key uncertainties relevant to the choice of strategy and then provides trade-off curves that enable decision makers to assess the implications of different expectations of the key uncertainties to their choices. This information has been compelling to stakeholders and decision makers when evaluating climate change impacts on water-management systems (Groves et al. 2008c).