Science-based methods for assessing the practical rationality of a proposed public policy typically represent assumed future outcomes of policies and values attributed to these outcomes in an idealized, that is, intentionally distorted way and abstracted from aspects that are deemed irrelevant. Different types of methods do so in different ways. As a consequence, they instantiate the properties that result from abstraction and idealization such as conceptual simplicity versus complexity, or comprehensiveness versus selectivity of the values under consideration to different degrees. (...) I hold that none of these methods is best in general. Instead, I opt for the valuation method that is useful for the policy issue in question both in terms of its relevance and in terms of its practicability. Relevance requires that the method can represent and account for what is at stake in the policy issue. Practicability refers to aspects such as easy versus difficult handling of the method. To argue for the claim, I evaluate three types of valuation methods: cost–benefit analysis that rests on unidimensional measurement and ranking, multi-criteria decision analysis that applies multi-dimensional measurement but unidimensional ranking, and non-aggregate indicator systems that operate with multi-dimensional measurement and sometimes also multi-dimensional ranking. Second-order justification indicating whether and how the valuation method chosen is capable of accounting for the substantive value considerations that constitute the real-world policy issue in question renders the conditions on which the results of a proposed policy evaluation rest transparent. (shrink)

Is there such a thing as a “best scientific methodology” in regulatory science? By examining cases from varying regulatory processes, we argue that there is no best scientific method for generating decision-relevant data. In addition, in regulatory science, the most suitable methodologies often differ from what is considered best practice in knowledge-oriented science. In data generation for regulatory purposes, we are faced with a wide spectrum of preferred methodologies as well as controversy as to methodological choice. What goes by the (...) most adequate scientific method can and will—justifiably and rationally—vary significantly according to context and use. In order to make this argument, we analyze four case studies, two from risk assessment and two from benefit assessment. Our analysis shows that it is the noncognitive objectives of a particular regulatory process that determine what counts as the most appropriate scientific method. We use the concept of bounded rationality to indicate that those methodological choices, despite being context-dependent, can be interpreted as rational. (shrink)