Ilkka Niiniluoto (University of Helsinki): Basic vs. applied research - a useful distinction?
Ilkka Niiniluoto, professor of theoretical philosophy, University of Helsinki, and Chairman of the Philosophical Society of Finland, is visiting the Science Studies Colloquium. The lecture is open for everyone.
Niiniluoto has been rector and then chancellor of the University of Helsinki and has played central role in Finnish politics of science and higher education. As a scholar he has made important contributions to the philosophy of science, its epistemic as well as its social and policy aspects.
Photo: University of Helsinki
Basic vs. Applied Research - a useful distinction?
Against criticism of the distinction between basic and applied research, I argue that these types of research can be systematically separated by their aims, standards of success, and structures of knowledge. The relevance of the distinction for science policy issues is illustrated.
The distinction between basic and applied research is notoriously vague and ambiguous. I argue that there is nevertheless a viable and systematic way of separating these two types of research. While basic research seeks descriptive knowledge about reality, so that its success can be assessed by epistemic utilities (truth, information, truthlikeness, explanatory power, understanding, simplicity), applied research seeks knowledge that is useful for some human activity, so that its assessment involves – besides epistemic utilities - also considerations of social relevance. One type of applied research deals with prediction, which helps us to prepare for future contingent events. Another type is design science, or “science of the artificial” in the sense of Herbert Simon, which studies the manipulation of natural and social systems. Design sciences seek “technical norms” which express relations between means and ends. Such technical norms are value-laden, as they contain a value goal, but still their justification should be value-neutral on the basic of empirical theoretical information. Examples of such conditional norms can be found in engineering sciences, agricultural sciences, clinical medicine, and social policy studies. Their relevance for science policy can be illustrated by showing how they can be applied to sustain evidence based policies and to solve wicked problems within “mode 2” or “strategic” research.