Criterion 4: Selection mechanisms

Definitions of key strategy selection mechanism terms (Ranger et al., 2010a; Hall et al., 2012a; Matrosov et al., 2013):

 Satisficing: an adequate level of service or system performance is established across a broad range of future scenarios.

 Optimal: the highest level of system performance or security is sought.  Local Robustness: robustness to uncertainty is sought over a localised

region of increasing uncertainty.

 Global Robustness: robustness to uncertainty is sought over the full range of discrete futures.

 Regret: the system performance lost from selecting one strategy over another under an isolated future scenario.

 Worst-case: the most impacting future scenario or set of conditions projected on a strategies performance.

Conventional water resources planning within the UK identifies strategies that can maintain a desired (i.e. target) level of service (or system risk). This is typically a cost minimisation-optimisation process whereby an optimal strategy is selected that best fits to the established target headroom projection. However, in the face of widening climate change and demographic uncertainties an optimal solution becomes unfeasible. This implies a general mechanism of satisficing required across all the DMMs when applied to modern water resources adaptation problems. For instance, a WRM decision maker does not aim for the strategy that provides the most water output obtainable to increase system security or the cheapest possible solution to a singular future; they aim for the strategy that meets their target objectives over the most future scenarios

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for the lowest cost/environmental impact. In other words, an optimal state may not exist when uncertainties are deemed “deep” and multiple futures and objectives are being adhered to. The only exception is within decision theories MR and WMT where it can be argued that seeking the most cost-effective strategy that meets the worst-case scenario projected or minimises the worst- case regrets is an optimal performance mechanism. However, the desired level of service the system is targeting (i.e. its performance under given metrics) will still merely be satisficing in this worst-case scenario and thus always satisficing in one or more objectives, whilst seeking the optimal in another.

The theory of robust satisficing appears the dominant mechanism across the DMMs being developed. However there exists several ways to layout and map the uncertainties in order to establish the robustness functions. This is often separated into the concept of local or global robustness. Localised robustness is most widely associated with IG decision theory. Criticism has however been placed on IG’s handling of deep uncertainty (Sniedovich, 2012) because the localised robustness analysis centers on an initial ‘most likely’ projection (which may be challenging to establish) and requires the ordering of future scenarios into nested sets around this initial ‘most likely’ projection. The former issue tends to be subjective and the latter issue can present difficulties when the arrays of potential supply and demand scenarios are not monotonically increasing. However, increasing uncertainty out from a localised point of higher likelihood allows IG to perform a theoretically unbounded staged assessment of the uncertainty region (Korteling et al., 2013), in relation to the bounded assessment of a global analysis, although the IG robustness analysis must still remain within the boundaries of realistic projections.

A global robustness examination, such as the mechanism traditionally utilised by RO and RDM theories, do not require the ordering of future scenarios and so uncertainties inherent in the ordering process can be diluted out. However, a global robustness evaluation requires testing of all potential scenarios which extends computation time in relation to the localised alternative which will stop assessing a strategy to future scenarios once surrounding states have caused the system to fail.

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In recent literature, Matrosov et al. (2013) conducted a comparison of RDM and IG for water resources system planning. For the RDM analysis they used a regret-based form of MCDA to select the initial candidate strategy and to rank the subsequent strategy modifications and a fractional-error method over three uncertain system parameters to construct the IG model. This allowed all parameters to expand proportionately over the region of uncertainty, leading to a quicker run time for IG over RDM as fewer scenarios were sampled; however their IG method had the draw-back of ignoring un-proportional scenarios (i.e. times when both supply inflows and demands were low) and only 40 out of a potential 64000 scenario combinations were examined due to the way the fractional-error model incrementally sampled the uncertain space in a uniform pattern. They found both approaches sought to identify robust satisficing rather than optimal decisions and utilised fixed pre-specified adaptation strategies. However, each delivered slightly different final adaptation solutions due to the alternative forms of uncertainty assessment and selection mechanisms. The author’s final recommendations were for the joint use of IG and RDM for the planning of water resources systems as each method helped clarify the results of the other. Hall et al. (2012a) also carried out a quantitative comparison of IG with RDM for climate policy. They identified many similarities, including the incorporated concepts of robust satisficing over multiple plausible representations of the future and the fact that both can provide decision support in the form of trade-off curves when multi objectives are assessed on quantified system models (see section 3.4.6). IG differentiated by considering potential gains and losses if a situation should turn out better or worse than expected; however the decision process is largely dictated by the robustness function in application to WRM problems, as the function for an opportune outcome is difficult to firmly establish. Further evaluations of varying robustness measures from different DMMs were also conducted by (Herman et al., 2015) who discovered each DMM ranked solutions to differing performance levels and recommended further investigation.

Selection mechanisms can also include consideration of the performance metrics/indicators utilised. These indicate the performance of a water system to a single future scenario or set of conditions and thus can theoretically be employed by any DMM to quantify performance. Selected performance metrics

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are either established as individual criterions or combined into an aggregated metric and are used to analyse a system to a given future scenario via parameters such as the frequency, magnitude or duration of detrimental events (i.e. reliability, vulnerability and resilience (see section 2.5.7)). These aspects are often combined in some form to calculate a level of projected future risk to the system as presented in many risk-based planning methods (Borgomeo et al., 2014; Ghile et al., 2014; Hall et al., 2012b; Kasprzyk et al., 2012b; Kjeldsen and Rosbjerg, 2004; Turner et al., 2014a) or can be assessed individually to provide more detailed performance information. The alteration of performance metric utilised and its effect on optimal adaptation strategy selection for WRM has been examined (Kjeldsen and Rosbjerg, 2004) but is a topic with room for further study on real-life complex case studies, especially with regard to its impact on a detailed engineering level, i.e. the effect on the optimal timing and scale of interventions scheduled across a planning horizon. When dealing with a vast range of uncertainties (future scenarios) and wide choice of potential adaptation strategies, the varying level of system performance can then either be analysed as an array (or table) of regrets from one strategy to another or in the comparative adherence to target levels of performance (i.e. absolute- performance based criteria).

From Criterion 4 it is identified that further work could be carried out to compare the impact on adaptation strategy selection based on the contrasting selection mechanisms, namely the local vs global forms of robustness analysis (e.g. IG vs RO), the effect of varying the governing decision rules (e.g. regret vs non- regret (absolute-performance) based assessment criteria) and in the choice of performance metrics/indicators on ultimate adaptation strategy selection (i.e. risk-based vs single criterion (reliability/resilience)based performance metrics).