The research ecosystem is in constant evolution. Funding policy tools, however, have not evolved as fast as the research activity itself. At the macroscopic scale, the policy shaping the way research funding is allocated could be improved by gaining more precise evidence-base of the potential effect of policy choices in achieving desired research objectives. Indeed, the science underpinning the research funding policy—also known as the science of science policy—is in infancy.
It is clear that many factors contribute to policy choices, which are often the object of compromises. However, on a mere practical level, having adequate tools to inform policy is of growing importance. The trouble is that research funding policy choices have, for years, been informed by static indicators of R&D performance. Thus, funding policy decisions have been based on incomplete and imperfect information. It is therefore time for science policy to take advantage of the improved simulations and modelling tools than can be applied to analyse the vast array of R&D input and output indicators and take into account their evolution over time.
Using big data, complex systems and network analysis could, for example, support the achievement of specific research policy objective. This is because such highly analytical tools make it possible to better evaluate the condition of their realisation than before. For example, one of the most advances model in this field, the agent-based SKIN model, has been tailored to analyse the effect of investment on the basis of historical performance. It can also be used to predict the effects of policy choices over time. So far, it has been tested in EU funding in the ICT sector.
It is likely that improvement to such policy models will emerge in the future, as the science of science policy matures. For example, to refine this bottom-up agent-based modelling, one option would be to combine it with a top-down optimisation algorithm. This, in turn, would improve the chance of getting the most from available resources. And get thus one step closer to reaching desired research objectives.
Such tools could provide the more precise evidence-base that has been lacking to further refine R&D funding policies. In the context of increasingly limited resources, it is now time to be a bit more demanding on the quality of the evidence base related to how funding is allocated.
Go back to the Special Issue: Alternative research funding
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