Use of modelling tools and outcomes by EU policy makers

Icône de l'outil pédagogique Use of modelling tools

Policy developers are not rarely reluctant to use models as a basis for policy making as will briefly be described below there are many and interlinked reasons for their scepticism. Policy developers comments on the developed tool were generally of two types, i.e. requests concerning the technical performance of the tool and strategic comments. The technical comments were often possible to meet by development of the components of the tool, workflow for IA and the Graphical User Interface. The strategic comments have a more profound political dimension linked to the above mentioned three concepts, highlighting the differences in agendas, culture and dynamics of the political and scientific communities. When analysing the stakeholder strategic comments using the concepts Credibility, Salience and Legitimacy it becomes evident that these attributes are tightly coupled and efforts to enhance one may lead to trade offs with the others.


Icône de l'outil pédagogique Credibility

When the Commission proposes a regulation, the DG’s argumentation for the policy recommendation that accompanies the proposal has to come across very convincing to decision makers and the public. The DG officers are under strong pressure from lobby groups which ask questions about the knowledge base for the recommendation made. It is therefore underlined by several of the policy developers SEAMLESS have been interacting with that the transparency of the modelling process is very important. “As we are constantly questioned by interest groups our assessments have to be transparent – concise and detailed!” Advanced analysis has in some cases been perceived as more confusing than helpful. This doesn’t mean that the modelling is weak, but that it is not comprehensible and therefore not useful for the officer in charge. Another important issue raised is that the models, the model chains and the assumptions made have to be understood by the assessment leaders in order for them to be able to explain and defend the conclusions made.

The issue of uncertainty of the modeling outcome is very important to the users, particularly when dealing with politically hot issues. A participant stated: “If I do not get precise information how could I otherwise motivate the results to an angry stakeholder phoning me up”. To deal with this demand we engaged with users to assess their perspectives as to uncertainty analysis (Gabbert et al, 2009). However here in lies the dilemma to he need for simplification might be a source of conflict between the ambitions of a scientific modeller and the IA leader.


Icône de l'outil pédagogique Salience
The approached policy developers also argued that is important that tools give answers to policy relevant questions. Similarly, it was argued that it is important that these tools do not produce too much or irrelevant information. The continued stakeholder interactions have been a way to identify this demand. However, the models used in SEAMLESS have a particular scope which to a certain extent can be enlarged but for some issues additional models are needed or science simply lacks the methods to provide an analysis.

Icône de l'outil pédagogique Legitimacy

In order to meet the demand for legitimacy a tool has to be flexible as to what to assess. It must be possible to incorporate stakeholders’ views and be sensitive to the political process. It was also essential that a tool assisting in IA is transparent, i.e. it should be easy understandable how the assessment has been done and what the underlying assumptions are. The participants repeatedly expressed their concern for lack of transparency of the modeling system. One official argued that “Scientists are lining up arguing that they have a new model that can assist me in assessing this or that. If I am not able to understand the underlying assumptions of a model how could I judge which model to use?” Legitimacy is achieved when the production of knowledge has been conducted in an unbiased way and has treated opposing interests in a fair manner. If the modeling system is a black box, where assumptions and other critical parameters are not clear and understandable, the outcome might not be perceived as legitimate. We have dealt with this demand in SEAMLESS through spending significant resources on a transparent user interface, extensive documentation of each of the models, a possibility to use and assess each of the models standalone and providing access to intermediate results of an analysis performed through a model chain. At some stage of the IA process the results will be subject to public examination. This examination will by nature be politically as opposed to scientifically motivated.


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