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DecisionSupport 1is a method that analyses social networks andinstitutional actors (i.e. organizations, individuals,interest groups, etc.) and their linkages andsocio-institutional relationships. It allows amapping of the influence and the exchange ofinformation and can therefore help in assessingadaptive capacity.SNA explores socio-institutional processes, andidentifies the context and governance arounddecisions. It highlights institutional arrangementsand structures, decision framing of actors, theirapproach to dealing with information, thecompetence for action, and the laws, regulations,values and norms that are likely to guide decisions.SNA can be undertaken using qualitative orquantitative methods. The qualitative approachfocuses on network mapping and provides visualrepresentations of networks, actors andinformation flows. This is usually undertaken aspart of participatory analysis, revealing insightsabout these relationships, the various flowsbetween actors and the perceptions of influenceand power in the network.The quantitative analysis is more comprehensive,considering whole networks and undertakinganalysis using software and standard statisticaltests. This provides a deeper analysis ofinstitutional aspects, providing variety ofmeasures/indicators to help describe the overallrelational structure of a social network, as well asthe roles of individuals within it.SNA has high relevance for adaptation, reflectingthe growing consensus and the focus on buildingadaptive capacity. The organisational knowledge,responsibility and strategies can be assessed,and potential barriers to adaptation can berevealed and subsequently negotiated. It canalso investigate how actors and organisationaddress uncertainty, i.e. how decisions areframed and their choice of appraisal tools.Strengths and WeaknessesThe MEDIATION project has reviewed thestrengths and weaknesses of differentapproaches. A summary of the key strengths andweakness are outlined below in Table 1.Table 1. Strengths and Weaknesses of the Decision Support Tools.Tool Strengths WeaknessesCost-BenefitAnalysisCost-EffectivenessAnalysisMulti-criteriaanalysis– Provides direct analysis of economic benefits,justification for action, and optimal solutions.– Well known and widely applied.– Benefits expressed in physical terms (notmonetary) thus applicable to non-marketsectors.– Relatively simple to apply and easilyunderstandable ranking and outputs.– Use of cost curves can assess policy targetswith least-cost optimisation.– Used for mitigation, thus widely recognised andresonance with policy makers.– Combines quantitative and qualitative data, andmonetary and non-monetary units, thusapplicable where quantification is challenging.– Relatively simple and transparent, and relativelylow cost / time requirement.– Expert judgement can be used very efficiently,and involves stakeholders, thus can be basedon local knowledge.– Difficulty of monetary valuation for non-marketsectors and non-technical options.– Uncertainty usually limited to probabilistic risks.– Benefits can be difficult to identify and singlemetric does not capture all costs and benefits.Less applicable cross-sectoral / complex.– Works best with technical options, and oftenomits capacity building and soft measures.– Sequential nature of cost curves ignores interlinkagesand potential for portfolios.– Does not lend itself to the consideration ofuncertainty, as works with central tendency.– Results need further interpretation andelaboration in more detailed studies.– Different experts may have different opinions, i.e.subjectivity involved.– Stakeholders may have lack of knowledge andcan miss important options.– Analysis of uncertainty is often qualitative andsubjective.8
Method OverviewTable 1. Strengths and Weaknesses of the Decision Support Tools (Continued).Tool Strengths WeaknessesReal OptionsAnalysisRobustDecisionMakingPortfolioAnalysisAdaptiveManagement/Iterative RiskAssessment/Adaptationturning pointsAnalyticHierarchyProcessSocial NetworkAnalysis– Assesses value of flexibility and learning, inquantitative and economic terms.– Decision trees conceptualise and visualise theconcept of adaptive management.– Assesses robustness rather than optimisation.– Applicable where probabilistic information is lowor missing, or climate uncertainty is high.– Can work with physical or economic metrics,enhancing application across sectors.– Assesses portfolios, which analysis of individualadaptation options not allow.– Measures “returns” using various metrics,including physical or economic, thus broadapplicability.– Use of the efficiency frontier an effective way ofvisualising results and risk-return trade-offs.– Process of monitoring, research, evaluation andlearning that avoids irreversible decisions andencourages learning to adjust decisions overtime.– Uses scenarios to delineate uncertainties not topredict the future.– Is more policy orientated and flexible inobjectives and appraisal methods.– Encourages discussion about (un)acceptablechange and definition of critical indicators.– Can be applied where elements difficult toquantify or not directly comparable.– Relatively simple approach and produces simplerankings that are easy to communicate.– Does not require information on economicbenefits so wide applicability.– Can accommodate a wide range of disciplines,opinions and groups of people who do notnormally interact.– Understanding of socio-institutional structures,actors, linkages and decision framing, to improveinformation and knowledge transfer.– Qualitative SNA quick and easy and encouragesparticipation across diverse viewpoints andactors– Quantitative SNA provides quantitativeinformation and correlations to understandnetwork variables.– Data and resource intensive, with highcomplexity and expert input.– Data a potential barrier, (probabilistic climate,quantitative and economic information).– Identification decision points often complex.– Lack of quantitative probabilities can make moresubjective, influenced by stakeholders.– The formal application has a high demand forquantitative information, computing power, andrequires a high degree of expert knowledge.– Resource intensive and needs expertknowledge.– Relies on the availability of quantitative data(effectiveness and variance/co-variance).– Requires probabilistic climate information, or anassumption of likelihood equivalence.– Issues of inter-dependence between options.– Challenging when multiple risks acting together,or indirect links to CC.– Thresholds are not always easy to identify,especially those that are poorly defined.– Focuses on existing management objectives.Unknown impacts and new challenges may beoverlooked / difficult.– Loses simplicity for communication less-welldefined thresholds and multiple drivers.– Results change as new options are considered.– Becomes complicated if lots of criteria andoptions are considered.– Subjective scale can lead to biases.– Trans-disciplinary capacity building can beundermined at the cost of the expediency.– Software can conceal conflicting valuejudgments.– Subjective bias.– Networks have artificial boundaries.– Does not have a temporal or spatial dimension.– Time-consuming, intensive process (quantitative).– Design of process is critical to get as manydiffering viewpoints as possible.9
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