With the introduction of large commercial industrial laboratories at the end of the nineteenth century, many types of experiments were institutionalized that do not aim at testing hypotheses. This paper builds a typology of experiments in techno-science, by analysing more than two hundred and fifty real-life technical projects. This resulted in four testing types (tests of hypotheses, of designs, of means-end knowledge, and of models or software), three determining types (developing working principles, preferred actions, and determining values of variables or relationships between variables) and one trial-and-error type of pure exploration. The typology is developed by working back and forth between thick descriptions of the experiments including their goals, and the development of six criteria of differentiation, to wit: determining versus testing; measurement scales of (in)dependent variables; intrinsic versus instrumental value of the outcomes; proximate function of the outcome; distant role of the outcome in the embedded project; the descriptive or normative character of the proximate or distant outcomes. The typology opens up inspiring methodological and philosophical research questions.@en

ost engineering reasoning in practice is about how to achieve some predetermined end. Despite its paramount importance, this form of reasoning has hardly been investigated in the literature.a The aim of this paper is therefore to explore the question to what extent technical norms can be said to have a truth-value, and under what conditions practical inferences are deductively valid. We take technical norms to be sentences of the form ‘If you want A, and you are in a situation B, then you ought to do X’. Von Wright’s standard example of making a hut habitable is our paradigm for practical inferences, where an obligation to act is deduced from an intention to realize an end, and an empirical constraint on how this end can be achieved. Our instrument of analysis is dynamic logic (PDL), since actions are aimed at changing the world. PDL already suffices to provide truth-conditions for technical norms. To accommodate the obligation in practical inferences we draw on John Jules Meyer’s deontic version of PDL. By paraphrasing ‘person P wants’ with ‘person P imposes an obligation on herself,’ we can give a plausible definition of the validity of practical inferences. In the discussion section, we address the issues of the reliability instead of truth-value of technical norms, and of the defeasibility of practical inferences as they occur in engineering practice.@en

In this chapter we report on and discuss our empirical classification of innovative engineering projects. Basic innovative engineering projects are characterized by their overall goal and accompanying method. On the basis of this goal and method, we classify engineering projects as all falling in one of the following categories: (1) Descriptive knowledge as prevalent in the descriptive sciences; (2) Design of artefacts and processes; (3) Engineering Means-end knowledge; (4) Modeling (simulation serious gaming included); (5) Engineering optimization; and (6) Engineering mathematics. These categories are illustrated with examples drawn from our educational experiences. Formally our classification system is a partition: the categories are mutually exclusive and collectively exhaustive. Regarding its empirical power, we claim intra-departmental completeness for the projects that we have studied at the Departments of Mechanics and Applied Physics of Delft University of Technology; we hypothesize intra-academic completeness within Universities of Technology; and we hope for and encourage investigating extra-academic completeness regarding engineering in industry. Besides having significant consequences for the methodology of the engineering sciences, our categorization provides a new way to study empirically the relation between science and technology.@en

In the 1980s, philosophical, historical and social studies of science underwent a change which later evolved into a turn to practice. Analysts of science were asked to pay attention to scientific practices in meticulous detail and along multiple dimensions, including the material, social and psychological. Following this turn, the interest in scientific practices continued to increase and had an indelible influence in the various fields of science studies. No doubt, the practice turn changed our conceptions and approaches of science, but what did it really teach us? What does it mean to study scientific practices? What are the general lessons, implications, and new challenges?.This volume explores questions about the practice turn using both case studies and theoretical analysis. The case studies examine empirical and mathematical sciences, including the engineering sciences. The volume promotes interactions between acknowledged experts from different, often thought of as conflicting, orientations. It presents contributions in conjunction with critical commentaries that put the theses and assumptions of the former in perspective. Overall, the book offers a unique and diverse range of perspectives on the meanings, methods, lessons, and challenges associated with the practice turn.@en