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DecisionSupport 3response-option analysis (Groves and Lempert,2007).The formal application of RDM involves a seriesof steps, set out in Figure 1 below (Groves et al.,2008). RDM analysis begins by structuring theproblem, but instead of characterising keyuncertainties (or more accurately, risks) as aprelude to optimally ranking strategies, theanalysis proposes alternative strategies.Uncertainties associated with the parametersdefining these strategies are then characterised,assigning a range of uncertainty values for eachvariable using stakeholder consultation or otherapproaches.Each strategy is then assessed over a widerange of (computer-generated) scenario futures.Combinations of uncertainty parameters that aremost important to the choice between strategiesare statistically derived and a summary of keytrade-offs among promising strategiesdeveloped (Groves et al, 2008).performing strategy is not identified, the iterativeprocess of strategy reformulation begins againwith stakeholders.The formal application of RDM tests manystrategies against computer-simulated futurescenarios, considering exogenous factors(outside the decision maker’s control) andpolicies or options (within their control), linkingthese with functional relationships, andassessing strategies against the performancemeasures in quantitative terms. The formermethod involves application of statistical or dataminingalgorithms.The Application to AdaptationRDM has many attributes that align with theconcept of adaptive management (Lempert andGroves, 2010) and the approach has been widelyrecommended for adaptation. The MEDIATIONproject has reviewed this potential application.RDM seeks strategies (or policies or options) thatare robust (‘good enough’). It therefore offers analternative to a conventional cost-benefitanalysis, which identifies optimal options on thebasis of economic efficiency (in the case ofclimate change adaptation, using impactassessment in a predict-then-optimiseframework).Source: Groves et al., 2008Figure 1. The RDM processThe analysis of strategies across the scenariofutures is measured with performance measures,which are used to measure pre-selected,desirable outcomes. Ideally, an RDM analysishelps to identify a robust strategy – one thatperforms well over a very wide range of scenariofutures. In the event that a robust, wellIt provides an alternative tool for decisionsupportwhich incorporates uncertainty explicitly,minimising regret (in contrast to maximisingexpected utility), and can play a role intranslating the theoretical concepts of adaptivemanagement into quantitative actions, byselecting options that are robust across a widerangeof plausible (climate) futures. This isparticularly valuable in cases where the climatemodel projections are highly uncertain, as isthe case for precipitation changes (see briefingnote 1.).The formal application of the approach can alsobe used to consider wider uncertainty (e.g. inrelation to socio-economic future, impactuncertainty) and thus is a potential powerful toolfor full uncertainty analysis. The formal approachcan also be used with interim performance(measurement) review or evaluation, which aligns2
Robust Decision Makingit more closely to the iterative adaptationmanagement concepts of monitoring, research,evaluation and learning. The informal applicationfor adaptation focuses on analysing climateuncertainty only, i.e. surrounding climate modelprojections or climate information.Strengths and WeaknessesA key part of the MEDIATION project has been toidentify the strengths and weaknesses ofdifferent approaches. A summary of these ispresented below.The main strength of the approach is that it helpsmake informed adaptation decisions possiblewithout relying on probabilistic predictions offuture climate change. Indeed, it is particularlyvaluable where future uncertainties are poorlycharacterised and/or probabilistic information islimited or not available.In such cases RDM provides a structured way ofassessing performance and identifying robustoptions in the face of uncertainty, whichconventional approaches do not allow. Theformal method provides the analytical power totest many strategies and sources of uncertaintyand identify trade-offs, synergies and robustdecisions.The approach can assess robustness (i.e.performance) using various metrics, includingphysical effectiveness or economic efficiency. Ittherefore offers greater flexibility in assessingmarket and non-market sectors, and can avoidvaluation of benefits enhancing applicabilitywhere valuation is difficult or contentious (e.g.ecosystems).The potential weaknesses relate to the lack ofquantitative probabilities associated withscenarios, which can make the analysis a moresubjective decision, influenced by stakeholders’perceptions.The (formal) application of the approach alsoinvolves very high demands for quantitativeinformation and computing power for modellingand analysis, and requires high expert input.Some of these aspects can be overcome bymore informal applications of the approach,particularly when limited to the analysis ofrobustness against climate model projections,though this then negates the benefits ofconsidering wider uncertainty and identifyingcomprehensive robustness (and a result, keyvulnerabilities (particularly those that accumulateor are cumulative) may go undetected.Case StudiesThe MEDIATION study has reviewed existingliterature examples that have applied RobustDecision Making to a number of adaptation casestudies.A number of these case studies are summarisedin the box below, including an example of oneformal and one informal application.Key strengthsThe strength of the RDM lies in the analyticalpower of testing many options or strategies andin the identification of robustness.Applicable under situations of uncertainty, whereprobabilistic information is low or missing, orclimate uncertainty is high.Potential weaknessesThe lack of quantitative probabilities can make itmore subjective, influenced by stakeholders’perceptions.The formal application has a high demand forquantitative information, computing power, andrequires a high degree of expert knowledge.Can work with physical or economic metrics,enhancing potential for application across nonmarketsectors.3
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