Abstract

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.