This article aims to answer what I call the “constitution question of engineering modeling”: in virtue of what does an engineering model model its target system? To do so, I will offer a category-theoretic, structuralist account of design, using the olog framework. Drawing on this account, I will conclude that engineering and scientific models are not only cognitively but also representationally indistinguishable. I will finally propose an axiological criterion for distinguishing scientific from engineering modeling.

Regarding the dichotomy between applied science and pure science, there are two apparently paradoxical facts. First, they are distinguishable. Second, the outcomes of pure sciences (e.g. scientific theories and models) are applicable to producing the outcomes of applied sciences (e.g. technological artefacts) and vice versa. Addressing the functional roles of applied and pure science, i.e. to produce design representation and science representation, respectively, I propose a new characterisation of the dichotomy that explains these two facts.

Thus far, the philosophical study of patenting has primarily focused on sociopolitical, legal, and ethical issues, such as the moral justifiability of patenting living organisms or the nature of (intellectual) property. In addition, however, the theory and practice of patenting entails many important problems that can be fruitfully studied from the perspective of the philosophy of science and technology. The principal aim of this article is to substantiate the latter claim. For this purpose, I first provide a concise review of (...) the main features of the theory and practice of the patenting of scientific and technological inventions. Second, I discuss several philosophical issues implied by these features and explore the possible contributions of the philosophy of science and technology to the clarification, or resolution, of these issues. The seven features discussed are: patents as commercial monopolies on scientific and technological inventions, the contrast between natural and non-natural subject matter, the distinction between inventions and discoveries, the reproducibility of inventions, the question of the sameness of two inventions, the distinction between the invented and the protected object, and the contrast between material objects versus concepts and theories. The article concludes with some observations on the problems and prospects of the philosophical study of the theory and practice of patenting scientific and technological inventions. (shrink)

Prandtl's work on the boundary layer theory is an interesting example for illustrating several important issues in philosophy of science such as the relation between theories and models and whether it is possible to distinguish, in a principled way, between pure and applied science. In what follows I discuss several proposals by the symposium participants regarding the interpretation of Prandtl's work and whether it should be characterized as an instance of applied science. My own interpretation of this example (1999) emphasised (...) the degree of autonomy embedded in Prandtl's boundary layer model and the way it became integrated in the larger theoretical context of hydrodynamics. In addition to extending that discussion here I also claim that the characterization of applied science which formed the basis for the symposium does not enable us to successfully distinguish applied science from the general practice of 'applying' basic scientific knowledge in a variety of contexts. (shrink)

This paper argues that scientific studies distinguish themselves from other studies by a combination of their processes, their (knowledge) elements and the roles of these elements. This is supported by constructing a process model. An illustrative example based on Newtonian mechanics shows how scientific knowledge is structured according to the process model. To distinguish scientific studies from research and scientific research, two additional process models are built for such processes. We apply these process models: (1) to argue that scientific progress (...) should emphasize both the process of change and the content of change; (2) to chart the major stages of scientific study development; and (3) to define “science”. (shrink)

Our concern is in explaining how and why models give us useful knowledge. We argue that if we are to understand how models function in the actual scientific practice the representational approach to models proves either misleading or too minimal. We propose turning from the representational approach to the artefactual, which implies also a new unit of analysis: the activity of modelling. Modelling, we suggest, could be approached as a specific practice in which concrete artefacts, i.e., models, are constructed with (...) the help of specific representational means and used in various ways, for example, for the purposes of scientific reasoning, theory construction and design of experiments and other artefacts. Furthermore, in this activity of modelling the model construction is intertwined with the construction of new phenomena, theoretical principles and new scientific concepts. We will illustrate these claims by studying the construction of the ideal heat engine by Sadi Carnot. (shrink)

How engineers know, and act on that knowledge, has a profound impact on society. Consequently, the analysis of engineering knowledge is one of the central challenges for the philosophy of engineering. In this article, we present a thematic multidisciplinary conceptual survey of engineering epistemology and identify key areas of research that are still to be comprehensively investigated. Themes are organized based on a survey of engineering epistemology including research from history, sociology, philosophy, design theory, and engineering itself. Five major interrelated (...) themes are identified: the relationship between scientific and engineering knowledge, engineering knowledge as a distinct field of study, the social epistemology of engineering, the relationship between engineering knowledge and its products, and the cognitive aspects of engineering knowledge. We discuss areas of potential future research that are underdeveloped or “undone.”. (shrink)

How engineers know, and act on that knowledge, has a profound impact on society. Consequently, the analysis of engineering knowledge is one of the central challenges for the philosophy of engineering. In this article, we present a thematic multidisciplinary conceptual survey of engineering epistemology and identify key areas of research that are still to be comprehensively investigated. Themes are organized based on a survey of engineering epistemology including research from history, sociology, philosophy, design theory, and engineering itself. Five major interrelated (...) themes are identified: the relationship between scientific and engineering knowledge, engineering knowledge as a distinct field of study, the social epistemology of engineering, the relationship between engineering knowledge and its products, and the cognitive aspects of engineering knowledge. We discuss areas of potential future research that are underdeveloped or “undone.”. (shrink)

Next SectionOne of the indisputable signs of the progress made in organic chemistry over the last two hundred years is the increased ability of chemists to manipulate, control, and design chemical reactions. The technological expertise manifest in contemporary synthetic organic chemistry is, at least in part, due to developments in the theory of organic chemistry. By appealing to a notable chemist's attempts to articulate and codify the heuristics of synthetic design, this paper investigates how understanding theoretical organic chemistry facilitates progress (...) in synthetic organic chemistry. The picture that emerges of how the applications of organic chemistry are grounded in its theory is contrasted with both standard and some more contemporary philosophical accounts of the applications of science. IntroductionTotal Synthesis as Applied ScienceUnderstanding Organic ChemistryThe Heuristics of Synthetic DesignAn Example: LongifoleneConclusion. (shrink)

This article investigates how understanding the theory of organic chemistry facilitates the total synthesis of organic compounds. After locating the philosophical significance of this question within the methodology or epistemology of applied science, I summarize the results of previous work on this issue—roughly that theoretical organic chemistry underwrites a sequence of heuristic policies that help to isolate plausible synthetic routes from the array of possibilities provided by structural or descriptive organic chemistry. While this prior account makes a solid start, it (...) does not capture all of the ways that the theory of organic chemistry contributes to total synthesis. This article aspires to enrich this account by exploring some additional ways that theory contributes. More specifically, I investigate how understanding the theory of organic chemistry can facilitate both the development of novel synthetic reactions and the implementation of a synthetic plan. The role of theory in these aspects of total synthesis will be explored by considering a particular, novel synthesis of longifolene. (shrink)

In this paper I argue that the appropriate analogy for “understanding what makes simulation results reliable” in global climate modeling is not with scientific experimentation or measurement, but—at least in the case of the use of global climate models for policy development—with the applications of science in applied design problems. The prospects for using this analogy to argue for the quantitative reliability of GCMs are assessed and compared with other potential strategies.

In this paper I argue that the appropriate analogy for “understanding what makes simulation results reliable” in Global Climate Modeling is not with scientific experimentation or measurement, but—at least in the case of the use of global climate models for policy development—with the applications of science in engineering design problems. The prospects for using this analogy to argue for the quantitative reliability of GCMs are assessed and compared with other potential strategies.

This article outlines a philosophy of science in practice that focuses on the engineering sciences. A methodological issue is that these practices seem to be divided by two different styles of scientific reasoning, namely, causal-mechanistic and mathematical reasoning. These styles are philosophically characterized by what Kuhn called ?disciplinary matrices?. Due to distinct metaphysical background pictures and/or distinct ideas of what counts as intelligible, they entail distinct ideas of the character of phenomena and what counts as a scientific explanation. It is (...) argued that the two styles cannot be reduced to each other. At the same time, although they are incompatible, they must not be regarded as competing. Instead, they produce different kinds of epistemic results, which serve different kinds of epistemic functions. Moreover, some scientific breakthroughs essentially result from relating them. This view of complementary styles of scientific reasoning is supported by pluralism about metaphysical background pictures. (shrink)

The complex societal challenges of the twenty-first Century require scientific researchers and academically educated professionals capable of conducting scientific research in complex problem contexts. Our central claim is that educational approaches inspired by a traditional empiricist epistemology insufficiently foster the required deep conceptual understanding and higher-order thinking skills necessary for epistemic tasks in scientific research. Conversely, we argue that constructivist epistemologies provide better guidance to educational approaches to promote research skills. We also argue that teachers adopting a constructivist learning theory (...) do not necessarily embrace a constructivist epistemology. On the contrary, in educational practice, novel educational approaches that adopt constructivist learning theories often maintain traditional empiricist epistemologies. Philosophers of science can help develop educational designs focused on learning to conduct scientific research, combining constructivist learning theory with constructivist epistemology. We illustrate this by an example from a bachelor’s program in Biomedical Engineering, where we introduce conceptual models and modeling as an alternative to the traditional focus on hypothesis testing in conducting scientific research. This educational approach includes the so-called B&K method for constructing scientific models to scaffold teaching and learning conceptual modeling. (shrink)

In science policy, it is generally acknowledged that science-based problem-solving requires interdisciplinary research. For example, policy makers invest in funding programs such as Horizon 2020 that aim to stimulate interdisciplinary research. Yet the epistemological processes that lead to effective interdisciplinary research are poorly understood. This article aims at an epistemology for interdisciplinary research, in particular, IDR for solving ‘real-world’ problems. Focus is on the question why researchers experience cognitive and epistemic difficulties in conducting IDR. Based on a study of educational (...) literature it is concluded that higher-education is missing clear ideas on the epistemology of IDR, and as a consequence, on how to teach it. It is conjectured that the lack of philosophical interest in the epistemology of IDR is due to a philosophical paradigm of science, which prevents recognizing severe epistemological challenges of IDR, both in the philosophy of science as well as in science education and research. The proposed alternative philosophical paradigm entails alternative philosophical presuppositions regarding aspects such as the aim of science, the character of knowledge, the epistemic and pragmatic criteria for accepting knowledge, and the role of technological instruments. This alternative philosophical paradigm assume the production of knowledge for epistemic functions as the aim of science, and interprets ‘knowledge’ as epistemic tools that must allow for conducting epistemic tasks by epistemic agents, rather than interpreting knowledge as representations that objectively represent aspects of the world independent of the way in which it was constructed. The engineering paradigm of science involves that knowledge is indelibly shaped by how it is constructed. Additionally, the way in which scientific disciplines construct knowledge is guided by the specificities of the discipline, which can be analyzed in terms of disciplinary perspectives. This implies that knowledge and the epistemic uses of knowledge cannot be understood without at least some understanding of how the knowledge is constructed. Accordingly, scientific researchers need so-called metacognitive scaffolds to assist in analyzing and reconstructing how ‘knowledge’ is constructed and how different disciplines do this differently. In an engineering paradigm of science, these metacognitive scaffolds can also be interpreted as epistemic tools, but in this case as tools that guide, enable and constrain analyzing and articulating how knowledge is produced. In interdisciplinary research, metacognitive scaffolds assist interdisciplinary communication aiming to analyze and articulate how the discipline constructs knowledge. (shrink)

This paper is concerned with scientific reasoning in the engineering sciences. Engineering sciences aim at explaining, predicting and describing physical phenomena occurring in technological devices. The focus of this paper is on mathematical description. These mathematical descriptions are important to computer-aided engineering or design programs (CAE and CAD). The first part of this paper explains why a traditional view, according to which scientific laws explain and predict phenomena and processes, is problematic. In the second part, the reasons of these methodological (...) difficulties are analyzed. Ludwig Prandtl’s method of integrating a theoretical and empirical approach is used as an example of good scientific practice. Based on this analysis, a distinction is made between different types of laws that play a role in constructing mathematical descriptions of phenomena. A central assumption in understanding research methodology is that, instead of scientific laws, knowledge of capacities and mechanisms are primary in the engineering sciences. Another important aspect in methodology of the engineering sciences is that in explaining a phenomenon or process spatial regions are distinguished in which distinct physical behaviour occur. The mechanisms in distinct spatial regions are represented in a so-called diagrammatic model. The construction of a mathematical description of the phenomenon or process is based on this diagrammatic model. (shrink)

How is scientific knowledge used, adapted, and extended in deriving phenomena and real-world systems? This paper aims at developing a general account of 'applying science' within the exemplar-based framework of Data-Oriented Processing (DOP), which is also known as Exemplar-Based Explanation (EBE). According to the exemplar-based paradigm, phenomena are explained not by deriving them all the way down from theoretical laws and boundary conditions but by modelling them on previously derived phenomena that function as exemplars. To accomplish this, DOP proposes to (...) maintain a corpus of derivation trees of previous phenomena together with a matching algorithm that combines subtrees from the corpus to derive new phenomena. By using a notion of derivational similarity, a new phenomenon can be modelled as closely as possible on previously explained phenomena. I will propose an instantiation of DOP which integrates theoretical and phenomenological modelling and which generalises over various disciplines, from fluid mechanics to language technology. I argue that DOP provides a solution for what I call Kuhn's problem and that it redresses Kitcher's account of explanation. (shrink)

In science policy, it is generally acknowledged that science-based problem-solving requires interdisciplinary research. For example, policy makers invest in funding programs such as Horizon 2020 that aim to stimulate interdisciplinary research. Yet the epistemological processes that lead to effective interdisciplinary research are poorly understood. This article aims at an epistemology for interdisciplinary research, in particular, IDR for solving ‘real-world’ problems. Focus is on the question why researchers experience cognitive and epistemic difficulties in conducting IDR. Based on a study of educational (...) literature it is concluded that higher-education is missing clear ideas on the epistemology of IDR, and as a consequence, on how to teach it. It is conjectured that the lack of philosophical interest in the epistemology of IDR is due to a philosophical paradigm of science, which prevents recognizing severe epistemological challenges of IDR, both in the philosophy of science as well as in science education and research. The proposed alternative philosophical paradigm entails alternative philosophical presuppositions regarding aspects such as the aim of science, the character of knowledge, the epistemic and pragmatic criteria for accepting knowledge, and the role of technological instruments. This alternative philosophical paradigm assume the production of knowledge for epistemic functions as the aim of science, and interprets ‘knowledge’ as epistemic tools that must allow for conducting epistemic tasks by epistemic agents, rather than interpreting knowledge as representations that objectively represent aspects of the world independent of the way in which it was constructed. The engineering paradigm of science involves that knowledge is indelibly shaped by how it is constructed. Additionally, the way in which scientific disciplines construct knowledge is guided by the specificities of the discipline, which can be analyzed in terms of disciplinary perspectives. This implies that knowledge and the epistemic uses of knowledge cannot be understood without at least some understanding of how the knowledge is constructed. Accordingly, scientific researchers need so-called metacognitive scaffolds to assist in analyzing and reconstructing how ‘knowledge’ is constructed and how different disciplines do this differently. In an engineering paradigm of science, these metacognitive scaffolds can also be interpreted as epistemic tools, but in this case as tools that guide, enable and constrain analyzing and articulating how knowledge is produced. In interdisciplinary research, metacognitive scaffolds assist interdisciplinary communication aiming to analyze and articulate how the discipline constructs knowledge. (shrink)