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Mediation training module: Robust Decision Making

This MEDIATION training tool provides an introduction to Robust Decision Making, a decision support tool that is used in situations of deep uncertainty.
Multiple Authors
Tan Copsey
  • Level: Advanced
  • Time commitment: 1-2 hours
  • Learning product: Tool/method summary
  • Sector: Multi-sector
  • Language: English
  • Certificate available: No


Robust Decision Making is a decision support tool that is used in situations of deep uncertainty, i.e. in the absence of probabilistic information on scenarios and outcomes. The key aim of RDM is to seek strategies that are robust, rather than optimal, over many future outcomes. The focus on robustness aligns to the current focus on resilience in adaptation thinking.

*Download the technical brief from the right-hand column.

Ideal problem types

The approach has widescale application across different sectors, though many applications exist in the water sector. It can also be used at different scales, from project level to portfolios and strategy planning. The robustness principles can be applied to both current climate variability and future climate change.

Where are the strengths and weaknesses of this method/tool?

The strength of the RDM method lies in the analytical power of testing many strategies and in the identification of robust strategies. It is applicable under situations of uncertainty, where probabilistic information is low or missing, or climate uncertainty is high (e.g. in direction of change) and it can consider economic or non-economic (physical) benefits. However, a lack of quantitative probabilities can make it more subjective, influenced by stakeholders’ perceptions. The formal application also has a high demand for quantitative information, computing power, and expert resources.

Where has this been applied?

Lempert and Groves (2010) undertook an application of an Urban Water Management Plan in California, evaluating the plan across a range of climate and socio-economic scenarios. Principal performance measures and uncertainties were identified, and alternative management strategies were assessed using a decision-tree within the Water Evaluation And Planning model. Adaptive strategies were assessed against six key criteria through a succession of 5-year signposts to allow an iterative approach, with performance measured using projected present value (PV) costs against PV shortage costs. The analysis identified eight response strategies, four static and four adaptive, finding the latter led to fewer vulnerable states, and identifying a number of robust resource development options. Dessai and Hulme (2007) present an example that considered climate robustness only, focussing on an English water resource zone, and the implications of climate change uncertainty on water supply options. Their findings indicated that the existing water plan was robust across the range of scenarios evaluated against, primarily because it had already mainstreamed climate change risks by using an extreme dry scenario.

Process of applying this tool/method

RDM has developed as an analytic, scenario-based approach for decision support. The formal application has a series of steps beginning by structuring the problem, proposing alternative strategies and identifying performance measures. Uncertainties associated with parameters associated with these strategies are then characterised, assigning a range of uncertainty values for each variable, often using stakeholder consultation. Each strategy is then assessed over a wide range of future scenarios. Qualitative and quantitative information is incorporated in a computer modelling interface that adopts data sampling algorithms to analyse strategies over large ensembles (thousands or millions of runs) reflecting different plausible future conditions. Strategies are then “stress tested” to identify potential vulnerabilities or weaknesses. The combinations of uncertainty parameters that are most important to the choice can be statistically derived and a summary of key trade-offs across the most robust strategies can be constructed. The diagram below is a graphical representation of the process from Wilby and Dessai (2010).

Robust Decision Framework

Why is this method useful for the field of climate adaptation?

RDM has attributes that align with adaptive management and the technique has been applied to adaptation. It has particular application in cases where future uncertainties are large. It has been used to help identify low and no regret options, and for testing near -term options or strategies (e.g. infrastructure) across a large number of futures, or climate projections.

Useful resources

  • Dessai , S. and Hulme , M. 2007 . ‘Assessing the robustness of adaptation decisions to
  • climate change uncertainties: a case study on water resources management in the
  • East of England’ , Global Environmental Change 17 : 59 –72
  • Groves, D. G., Lempert, R., Knopman, D., and Berry, S. (2008a). “Preparing for an Uncertain Future Climate in the Inland Empire – Identifying Robust Water Management Strategies.” DB-550-NSF, RAND Corporation, Santa Monica, CA.
  • Groves, D.G., Knopman, D., Lempert, R.J., Berry, S.H., and Wainfan, L., 2008b. Presenting uncertainty about climate change to water-resource managers: a summary of workshops with the Inland Empire Utilities Agency. RAND, 2008.
  • Groves, D. G. and Lempert, R. J. (2007): A new analytic method for finding policy-relevant scenarios. Global Environmental Change. 17: 73-85.
  • Lempert, R.J. and Schlesinger, M.E., 2000.Robust strategies for abating climate change. Climatic Change, 45, 387-401.
  • Lempert et al. (2003). Shaping the next one hundred years: new methods for quantitative, long-term policy analysis. RAND, 2003. Santa Monica, CA. ISBN 0-8330-3275-5.
  • Lempert and Groves, 2010. Identifying and evaluating robust adaptive policy responses to climate change for water management agencies in the American west. Technological Forecasting & Social Change, 77, 960-974.
  • Wilby, R.L., & Dessai, S. (2010). Robust adaptation to climate change. Weather, 65(7), 180-185.

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