Technical Policy Briefing Notes - 3
Robust Decision Making
Strengths and Weaknesses
Policy Briefs
Robust Decision Making
Strengths and Weaknesses
A key part of the MEDIATION project has been
to identify the strengths and weaknesses of different approaches. A
summary of these is presented below.
The main strength of the approach is that it helps make informed adaptation decisions possible without relying on probabilistic predictions of future climate change. Indeed, it is particularly valuable where future uncertainties are poorly characterised and/or probabilistic information is limited or not available.
In such cases RDM provides a structured way of assessing performance and identifying robust options in the face of uncertainty, which conventional approaches do not allow. The formal method provides the analytical power to test many strategies and sources of uncertainty and identify trade-offs, synergies and robust decisions.
The approach can assess robustness (i.e. performance) using various metrics, including physical effectiveness or economic efficiency. It therefore offers greater flexibility in assessing market and non-market sectors, and can avoid valuation of benefits enhancing applicability where valuation is difficult or contentious (e.g. ecosystems).
The potential weaknesses relate to the lack of quantitative probabilities associated with scenarios, which can make the analysis a more subjective decision, influenced by stakeholders’ perceptions.
The (formal) application of the approach also involves very high demands for quantitative information and computing power for modelling and analysis, and requires high expert input.
Some of these aspects can be overcome by more informal applications of the approach, particularly when limited to the analysis of robustness against climate model projections, though this then negates the benefits of considering wider uncertainty and identifying comprehensive robustness (and a result, key vulnerabilities (particularly those that accumulate or are cumulative) may go undetected.
The main strength of the approach is that it helps make informed adaptation decisions possible without relying on probabilistic predictions of future climate change. Indeed, it is particularly valuable where future uncertainties are poorly characterised and/or probabilistic information is limited or not available.
In such cases RDM provides a structured way of assessing performance and identifying robust options in the face of uncertainty, which conventional approaches do not allow. The formal method provides the analytical power to test many strategies and sources of uncertainty and identify trade-offs, synergies and robust decisions.
The approach can assess robustness (i.e. performance) using various metrics, including physical effectiveness or economic efficiency. It therefore offers greater flexibility in assessing market and non-market sectors, and can avoid valuation of benefits enhancing applicability where valuation is difficult or contentious (e.g. ecosystems).
The potential weaknesses relate to the lack of quantitative probabilities associated with scenarios, which can make the analysis a more subjective decision, influenced by stakeholders’ perceptions.
The (formal) application of the approach also involves very high demands for quantitative information and computing power for modelling and analysis, and requires high expert input.
Some of these aspects can be overcome by more informal applications of the approach, particularly when limited to the analysis of robustness against climate model projections, though this then negates the benefits of considering wider uncertainty and identifying comprehensive robustness (and a result, key vulnerabilities (particularly those that accumulate or are cumulative) may go undetected.
| Key strengths The strength of the RDM lies in the analytical power of testing many options or strategies and in the identification of robustness. Applicable under situations of uncertainty, where probabilistic information is low or missing, or climate uncertainty is high. Can work with physical or economic metrics, enhancing potential for application across nonmarket sectors. | Potential weaknesses The lack of quantitative probabilities can make it more subjective, influenced by stakeholders’ perceptions. The formal application has a high demand for quantitative information, computing power, and requires a high degree of expert knowledge. |
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