Marcel Boumans

Generally, rational decision-making is conceived as arriving at a decision by a correct application of the rules of logic and statistics. If not, the conclusions are called biased. After an impressive series of experiments and tests carried out in the last few decades, the view arose that rationality is tough for all, skilled field experts not excluded. A new type of planner's counsellor is called for: the normative statistician, the expert in reasoning with uncertainty par excellence. To unravel this view, the paper explores a specific practice of clinical decision-making, namely Evidence-Based Medicine. This practice is chosen, because it is very explicit about how to rationalize practice. The paper shows that whether a decision-making process is rational cannot be assessed without taking into account the environment in which the decisions have to be taken. To be more specific, the decision to call for new evidence should be rational too. This decision and the way in which this evidence is obtained are crucial to validate the base rates. Rationality should be model-based, which means that not only the isolated decision-making process should take a Bayesian updating process as its norm, but should also model the acquisition of evidence (priors and tests results) as a rational process.

Unlike basic sciences, scientific research in advanced technologies aims to explain, predict, and describe not phenomena in nature, but phenomena in technological artefacts, thereby producing knowledge that is utilized in technological design. This article first explains why the covering‐law view of applying science is inadequate for characterizing this research practice. Instead, the covering‐law approach and causal explanation are integrated in this practice. Ludwig Prandtl’s approach to concrete fluid flows is used as an example of scientific research in the engineering sciences. A methodology of distinguishing between regions in space and/or phases in time that show distinct physical behaviours is specific to this research practice. Accordingly, two types of models specific to the engineering sciences are introduced. The diagrammatic model represents the causal explanation of physical behaviour in distinct spatial regions or time phases; the nomo‐mathematical model represents the phenomenon in terms of a set of mathematically formulated laws.

Introduction: philosophy of science in practice Content Type Journal Article Category Editorial Article Pages 303-307 DOI 10.1007/s13194-011-0036-4 Authors Rachel Ankeny, School of History & Politics, University of Adelaide, Napier Building, The University of Adelaide, Adelaide, SA 5005, Australia Hasok Chang, Department of History and Philosophy of Science, University of Cambridge, Free School Lane, Cambridge, CB2 3RH UK Marcel Boumans, Faculty of Economics and Business, University of Amsterdam, Valckenierstraat 65-67, 1018 XE Amsterdam, The Netherlands Mieke Boon, Department of Philosophy, University of Twente, Postbox 217, 7500 AE Enschede, The Netherlands Journal European Journal for Philosophy of Science Online ISSN 1879-4920 Print ISSN 1879-4912 Journal Volume Volume 1 Journal Issue Volume 1, Number 3