Abstract

Regulatory science, which generates knowledge relevant for regulatory decision-making, is different from standard academic science in that it is oriented mainly towards the attainment of non-epistemic aims. The role of uncertainty and the limits to the relevance of academic science are being recognized more and more explicitly in regulatory decision-making. This has led to the introduction of regulation-specific scientific methodologies in order to generate decision-relevant data. However, recent practical experience with such non-standard methodologies indicates that they, too, may be subject to important limitations. We argue that the attainment of non-epistemic values and aims requires not only control of the quality of the data and the methodologies, but also the selection of the level of regulation deemed adequate in each specific case.