the socio-economic impacts of the policy decisions. So, as models are becoming more and more important in the policy arena, the complexity of the relationship between their output and the consequent decisions together with the limited evaluation of their robustness can cause a lack of confidence, and a possible growing mistrust on the capability of systems to support important strategic choices. A riorous robustness analysis approach may be the answer to overcome this problem. The US Environmental Protection Agency indeed recommends that both model developers and users perform such an analysis to help determining when a model can be used to take reliable decisions (EPA, 2009). Similarly, in Europe, the FAIRMODE (Forum for Air Quality Modelling) initiative is dealing with guidelines on for purpose” to assess if an air quality model reaches the minimum level of quality to be used “fitness in the science-to-policy interface (Thunis et al., 2015). In the case of Integrated Assessment Models (IAMs), the analysis becomes even more complex, since it does not consider a single model, but a set of interlinked specific models. It means that the robustness analysis should assess the uncertainty and sensitivity not only for each single component of the system, but also for the system in its entirety (Amann et al. 2011). To be more specific, let’s consider the case of Integrated Assessment Modelling (IAM) in the air quality field. In this context, the analyst should answer questions such as: Does the preferable decision change, if it is assessed that the results of the model of the (physical, economic, social) system are inaccurate? How much risk affects the implementation of a certain decision, given that there is no certainty about the model result? The results of an air quality IAM may be inaccurate for a number of different reasons that are common to any modelling exercise, going from the too simplistic formulation to the impossibility of measuring the necessary inputs. In addition, uncertainty might derive from various sources, that are peculiar to this context. The space of feasible actions (options/alternatives/decisions) is normally very limited and, more
the socio-economic impacts of the policy decisions. So, as models are becoming more and more important in the policy arena, the complexity of the relationship between their output and the consequent decisions together with the limited evaluation of their robustness can cause a lack of confidence, and a possible growing mistrust on the capability of systems to support important strategic choices. A riorous robustness analysis approach may be the answer to overcome this problem. The US Environmental Protection Agency indeed recommends that both model developers and users perform such an analysis to help determining when a model can be used to take reliable decisions (EPA, 2009). Similarly, in Europe, the FAIRMODE (Forum for Air Quality Modelling) initiative is dealing with guidelines on “fitness for purpose” to assess if an air quality model reaches the minimum level of quality to be used in the science-to-policy interface (Thunis et al., 2015). In the case of Integrated Assessment Models (IAMs), the analysis becomes even more complex, since it does not consider a single model, but a set of interlinked specific models. It means that the robustness analysis should assess the uncertainty and sensitivity not only for each single component of the system, but also for the system in its entirety (Amann et al. 2011). To be more specific, let’s consider the case of Integrated Assessment Modelling (IAM) in the air quality field. In this context, the analyst should answer questions such as: Does the preferable decision change, if it is assessed that the results of the model of the (physical, economic, social) system are inaccurate? How much risk affects the implementation of a certain decision, given that there is no certainty about the model result? The results of an air quality IAM may be inaccurate for a number of different reasons that are common to any modelling exercise, going from the too simplistic formulation to the impossibility of measuring the necessary inputs. In addition, uncertainty might derive from various sources, that are peculiar to this context. The space of feasible actions (options/alternatives/decisions) is normally very limited and, more
Uncertainty_Paper-2015-11-12_CC_GG.docx