Technical Policy Briefing Notes - 3
Robust Decision Making
Key Messages
Policy Briefs
Robust Decision Making
Key Messages
- There is increasing interest in the appraisal of options, as adaptation moves from theory to practice. In response, a number of existing and new decision support tools are being considered, including methods that address uncertainty.
- The
FP7 MEDIATION project has undertaken a detailed review of
these tools, and has tested them in a series of case studies.
It has assessed their applicability for adaptation
and analysed how they consider uncertainty.
The findings have been used to provide information
and guidance for the MEDIATIONAdaptation Platform and are summarised in
a set of policy briefing notes.
- One
of the tools widely recommended for adaptation is Robust Decision
Making (RDM). RDM aims to identify robust
options or strategies, i.e. those which perform
well over a wide range of futures. It aims to support
decision making under conditions of deep uncertainty, i.e.
when little or no probabilistic information is available.
- RDM
has been widely applied as analytic, scenario-based approach
for decision support. The formal application is
undertaken in a computer modelling interface that
adopts data sampling algorithms to analyse strategies
over very large ensembles. However, the concepts of the
approach can also be used in a simpler application,
which tests how options or strategies perform against
climate uncertainty.
- RDM
has high relevance for adaptation, and aligns strongly with
the concepts of adaptive management, by targeting policies or
options that are robust rather than optimal.
- The
review has considered the strengths and weakness of the
approach for adaptation. The key strength is the quantitative
analysis of robustness, and the fact that the method
can be applied when future uncertainties are
poorly characterised or probabilistic information
is limited or unavailable. The approach can also work
with quantitative or economic data.
- The
potential weaknesses of the formal application relate to the
high data and resource needs (for quantitative
information, computing power, stakeholder
engagement and analysis) and the associated expert
input required. The data and scenario inputs can also
be somewhat subjective, influenced by stakeholders’
perception. However, many of these aspects can be overcome
with informal applications of the approach,
particularly when focused on climate uncertainty alone.
- •
Previous applications of ROA for adaptation have been
reviewed, and adaptation case studies are summarised. Most of
the recent adaptation applications have focused
on water management, and these include both formal
and informal examples.
- The
review and case studies provide useful information on
the types of adaptation problem types where RDM might
be appropriate, as well as data needs,
resource requirements and good practice lessons. RDM
is particularly applicable under situations of high
uncertainty, where probabilistic information is low or
missing. The approach can use physical or
economic information, thus it has broad
applicability from detailed economic appraisal through
to the consideration of non-market sectors where
valuation may be challenging. It has high potential for
identifying low and no regret options, and near-term
adaptation strategies that enhance long-term resilience.
- Ideally the approach should be used to consider multiple sources of uncertainty, but this increases the resources needed. The application to climate change uncertainty alone therefore provides a ‘lighter-touch’ approach to test options for climate robustness. In such applications, the larger the climate uncertainties explored, the better. Where resource constraints are high, such exercises can prove valuable for helping to identify robust solutions and move towards adaptive management.
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