Chapter 1 FROM ORDER TO DISORDER 5 mins. John enters and goes into his office. He says something very quickly about having made a bad mistake. He had sent the review of a paper. . . . The rest of the sentence is inaudible. 5 mins.

This classic remains one of Karl Popper's most wide-ranging and popular works, notable not only for its acute insight into the way scientific knowledge grows, but also for applying those insights to politics and to history.

Bruno Latour was once asked : "Do you believe in reality?" This text is an attempt to answer this question.

In this sequence of philosophical essays about natural science, the author argues that fundamental explanatory laws, the deepest and most admired successes of modern physics, do not in fact describe regularities that exist in nature. Cartwright draws from many real-life examples to propound a novel distinction: that theoretical entities, and the complex and localized laws that describe them, can be interpreted realistically, but the simple unifying laws of basic theory cannot.

The distinction between basic and applied research is notoriously vague, despite its frequent use in science studies and in science policy. In most cases it is based on such pragmatic factors as the knowledge and intentions of the investigator or the type of research institute. Sometimes the validity of the distinction is denied altogether. This paper suggests that there are two ways of distinguishing systematically between basic and applied research: (i) in terms of the utilities that define the aims of (...) inquiry, and (ii) by reference to the structure of the relevant knowledge claims. An important type of applied research aims at results that are expressed by techical norms (in von Wright's sense): if you wish to achieveA, and you believe you are in a situationB, then you should doX. This conception of design sciences allows us to re-evaluate many issues in the history, philosophy, and ethics of science. (shrink)

A general account of modeling in physics is proposed. Modeling is shown to involve three components: denotation, demonstration, and interpretation. Elements of the physical world are denoted by elements of the model; the model possesses an internal dynamic that allows us to demonstrate theoretical conclusions; these in turn need to be interpreted if we are to make predictions. The DDI account can be readily extended in ways that correspond to different aspects of scientific practice.

Truth is standardly considered a requirement on epistemic acceptability. But science and philosophy deploy models, idealizations and thought experiments that prescind from truth to achieve other cognitive ends. I argue that such felicitous falsehoods function as cognitively useful fictions. They are cognitively useful because they exemplify and afford epistemic access to features they share with the relevant facts. They are falsehoods in that they diverge from the facts. Nonetheless, they are true enough to serve their epistemic purposes. Theories that contain (...) them have testable consequences, hence are factually defeasible. (shrink)

In this paper, I develop Mauricio Suárez’s distinction between denotation, epistemic representation, and faithful epistemic representation. I then outline an interpretational account of epistemic representation, according to which a vehicle represents a target for a certain user if and only if the user adopts an interpretation of the vehicle in terms of the target, which would allow them to perform valid (but not necessarily sound) surrogative inferences from the model to the system. The main difference between the interpretational conception I (...) defend here and Suárez’s inferential conception is that the interpretational account is a substantial account—interpretation is not just a “symptom” of representation; it is what makes something an epistemic representation of a something else. (shrink)

Science relies on models and idealizations that are known not to be true. Even so, science is epistemically reputable. To accommodate science, epistemology should focus on understanding rather than knowledge and should recognize that the understanding of a topic need not be factive. This requires reconfiguring the norms of epistemic acceptability. If epistemology has the resources to accommodate science, it will also have the resources to show that art too advances understanding.

The aim of this paper is to generalize a pair of concepts that are widely used in the history of science, in art history and in historical linguistics – the concept of internal and external history – and to replace the often very vague talk of ‘historical narratives’ with this conceptual framework of internal versus external history. I argue that this way of framing the problem allows us to see the possible alternatives more clearly – as a limited number of (...) possible relations between internal and external history. Finally, I argue that while external history is metaphysically prior to internal history, when it comes to historical explanations, we need both. (shrink)

The aim of this paper is to defend the structural concept of representation, as defined by homomorphisms, against its main objections, namely: logical objections, the objection from misrepresentation, theobjection from failing necessity, and the copy theory objection. The logical objections can be met by reserving the relation ‘to be homomorphic to’ for the explication of potential representation (or, of the representational content). Actual reference objects (‘targets’) of representations are determined by (intentional or causal) representational mechanisms. Appealing to the independence of (...) the dimensions of ‘content’ and ‘target’ also helps to see how the structural concept can cope with misrepresentation. Finally, I argue that homomorphic representations are not necessarily ‘copies’ of their representanda, and thus can convey scientific insight. (shrink)

ABSTRACT The term “applied science,” as it came to be popularly used in the 1870s, was a hybrid of three earlier concepts. The phrase “applied science” itself had been coined by Samuel Taylor Coleridge in 1817, translating the German Kantian term “angewandte Wissenschaft.” It was popularized through the Encyclopaedia Metropolitana, which was structured on principles inherited from Coleridge and edited by men with sympathetic views. Their concept of empirical as opposed to a priori science was hybridized with an earlier English (...) concept of “practical science” and with “science applied to the arts,” adopted from the French. Charles Dupin had favored the latter concept and promoted it in the reconstruction of the Conservatoire Nationale des Arts et Métiers. The process of hybridization took place from the 1850s, in the wake of the Great Exhibition, as a new technocratic government favored scientific education. “Applied science” subsequently was used as the epistemic basis for technical education and the formation of new colleges in the 1870s. (shrink)

“Pure science” and “applied science” have peculiar histories in the United States. Both terms were in use in the early part of the nineteenth century, but it was only in the last decades that they took on new meanings and became commonplace in the discourse of American scientists. The rise in their currency reflected an acute concern about the corruption of character and the real possibilities of commercializing scientific knowledge. “Pure” was the preference of scientists who wanted to emphasize their (...) nonpecuniary motives and their distance from the marketplace. “Applied” was the choice of scientists who accepted patents and profits as other possible returns on their research. In general, the frequent conjoining of “pure” and “applied” bespoke the inseparable relations of science and capitalism in the Gilded Age. (shrink)

The way in which knowledge progresses, and especially our scientific knowledge, is by unjustified anticipations, by guesses, by tentative solutions to our problems, by conjectures. These conjectures are controlled by criticism: that is, by attempted refutations, which include severely critical tests. They may survive these tests; but they can never be positively justified: they can neither be established as certainly true nor even as 'probable'. Criticism of our conjectures is of decisive importance: by bringing out our mistakes it makes us (...) understand the difficulties of the problems which we try to solve. This is how we become better acquainted with our problem, and able to propose more mature solutions: the very refutation of a theory - that is, of a tentative solution to our problem - is always a step forward that takes us nearer the truth. And this is how we can learn from our mistakes. As we learn from our mistakes our knowledge grows, even though we may never know - that is, know for certain. Since our knowledge can grow, there can be no reason here for despair of reason. And since we can never know for certain, the can be no authority here for any claim to authority, for conceit over our knowledge, or for smugness. The essays and lectures of which this book is composed apply this thesis to many topics, ranging from problems of the philosophy and history of the physical and social sciences to historical and political problems. (shrink)

Recent work in the philosophy of science has generated an apparent conflict between theories attempting to explicate the nature of scientific representation. On one side, there are what one might call 'informational' views, which emphasize objective relations (such as similarity, isomorphism, and homomorphism) between representations (theories, models, simulations, diagrams, etc.) and their target systems. On the other side, there are what one might call 'functional' views, which emphasize cognitive activities performed in connection with these targets, such as interpretation and inference. (...) The main sources of the impression of conflict here are arguments by some functionalists to the effect that informational theories are flawed: it is suggested that relations typically championed by informational theories are neither necessary nor sufficient for scientific representation, and that any theory excluding functions is inadequate. In this paper I critically examine these arguments, and contend that, as it turns out, informational and functional theories are importantly complementary. (shrink)

Continuing his exploration of the organization of complexity and the science of design, this new edition of Herbert Simon's classic work on artificial ...

In this book Bruno Latour brings together these different approaches to provide a lively and challenging analysis of science, demonstrating how social context..

Recent discussions of the nature of representation in science have tended to import pre-established decompositions from analyses of representation in the arts, language, cognition and so forth. Which of these analyses one favours will depend on how one conceives of theories in the first place. If one thinks of them in terms of an axiomatised set of logico-linguistic statements, then one might be naturally drawn to accounts of linguistic representation in which notions of denotation, for example, feature prominently. If, on (...) the other hand, one conceives of theories in non-linguistic terms, as in the model-theoretic approach, then one might look to analyses of representation in the arts where notions of resemblance tend to be brought to the fore. Thus van Fraassen, for example, has imported such an analysis into his discussion of representation in science and argued that an appropriate account of resemblance can be given in terms of the set-theoretic relation of isomorphism. This has been strongly criticised by Suarez, who argues that just as isomorphism cannot capture representation in art, so it is inappropriate in the scientific context as well. Similarly Hughes draws on Goodman`s rejection of resemblance in art in favour of denotation and, rather confusingly perhaps, favours the latter whilst also maintaining the model-theoretic view of theories. In this paper, I shall examine the debate in terms of four claims: 1. Isomorphism is not sufficient for representation; 2. Isomorphism is not necessary for representation; 3. Models represent but theories do not; 4. Models denote and do not resemble. Each of these claims will be questioned and I will conclude by suggesting that, through appropriate modifications, a form of isomorphism can serve to underpin representation in both the arts and science. (shrink)

Discussions of representation in science tend to draw on examples from art. However, such examples need to be handled with care given a) the differences between works of art and scientific theories and b) the accommodation of these examples within certain philosophies of art. I shall examine the claim that isomorphism is neither necessary nor sufficient for representation and I shall argue that there exist accounts of representation in both art and science involving isomorphism which accommodate the apparent counterexamples and, (...) moreover, allow us to understand how “impossible” artistic objects and inconsistent scientific theories can be said to represent. (shrink)

This book discusses the relationship between the philosophy of science and philosophy of engineering, and demonstrates how philosophers of engineering design as well as design researchers can benefit from the conceptual toolkit that the philosophy of science has to offer. In this regard, it employs conceptual tools from the philosophical literature on scientific explanation to address key issues in engineering design and philosophy of engineering design. Specifically, the book focuses on assessing the explanatory value of function ascriptions used in engineering (...) design and philosophy of technical functions; on elaborating the structure of explanation in engineering design; on assessing the role and value of design representations in engineering design and philosophy thereof; and on elaborating means for the testing of design methods. Presenting a novel and effective approach to tackling key issues in the field, philosophers of engineering and design alike will greatly benefit from this book. (shrink)

How should we understand scientific progress? Kuhn famously discussed science as its own internally driven venture, structured by paradigms. He also famously had a problem describing progress in science, as problem-solving ability failed to provide a clear rubric across paradigm change—paradigm changes tossed out problems as well as solving them. I argue here that much of Kuhn’s inability to articulate a clear view of scientific progress stems from his focus on pure science and a neglect of applied science. I trace (...) the history of the distinction between pure and applied science, showing how the distinction came about, the rhetorical uses to which the distinction has been put, and how pure science came to be both more valued by scientists and philosophers. I argue that the distinction between pure and applied science does not stand up to philosophical scrutiny, and that once we relinquish it, we can provide Kuhn with a clear sense of scientific progress. It is not one, though, that will ultimately prove acceptable. For that, societal evaluations of scientific work are needed. (shrink)

This paper defends an inferential conception of scientific representation. It approaches the notion of representation in a deflationary spirit, and minimally characterizes the concept as it appears in science by means of two necessary conditions: its essential directionality and its capacity to allow surrogate reasoning and inference. The conception is defended by showing that it successfully meets the objections that make its competitors, such as isomorphism and similarity, untenable. In addition the inferential conception captures the objectivity of the cognitive representations (...) used by science, it sheds light on their truth and completeness, and it explains the source of the analogy between scientific and artistic modes of representation. (shrink)

The aim of this paper is to defend the structural concept of representation, as defined by homomorphisms, against its main objections, namely: logical objections, the objection from misrepresentation, theobjection from failing necessity, and the copy theory objection. The logical objections can be met by reserving the relation.

In this book, Donald Stokes challenges Bush's view and maintains that we can only rebuild the relationship between government and the scientific community when we understand what is wrong with that view.Stokes begins with an analysis of the ...

The following article treats the 'applicational turn' of modern fluid dynamics as it set in at the beginning of the 20th century with Ludwig Prandtl's concept of the boundary layer. It seeks to show that there is much more to applying a theory in a highly mathematical field like fluid dynamics than deriving a special case from a general explanatory theory under particular antecedent conditions. In Prandtl's case, the decisive move was to introduce a model that provided a physical/causal conception (...) of viscous flow at high Reynolds numbers. It facilitated an approximate solution to the Navier-Stokes equations, which in turn gave rise to many special applications. After a detailed account of Prandtl's achievement, the article discusses the role of the physical model and its experimental and mathematical significance. It is shown that the mathematical simplification provided by the physical model greatly expanded the explanatory capacity of the theory which the Navier-Stokes equations alone could not provide. (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)

Experimental engineering models have been used both to model general phenomena, such as the onset of turbulence in fluid flow, and to predict the performance of machines of particular size and configuration in particular contexts. Various sorts of knowledge are involved in the method - logical consistency, general scientific principles, laws of specific sciences, and experience. I critically examine three different accounts of the foundations of the method of experimental engineering models (scale models), and examine how theory, practice, and experience (...) are involved in employing the method to obtain practical results. Models of machines and mechanisms can be (and generally are) involved in establishing criteria for similar phenomena, which provide guidance in using events to model other events. Conversely, models of phenomena such as events that model other events can be (and generally are) involved in experimentation on models of machines. I conclude that often it is not more detailed models or the more precise equations they engender that leads to better understanding, but rather an insightful use of knowledge at hand to determine which similarity principles are appropriate in allowing us to infer what we do not know from what we are able to observe. (shrink)

The traditional distinction between basic and applied science has been much criticized in recent decades. The criticism is based on a combination of historical and systematic epistemic argument. The present paper is mostly concerned with the historical aspect. I argue that the critics impose an understanding at odds with the way the distinction was understood by its supporters in debates on science education and science policy in the nineteenth and twentieth centuries. And I show how a distinction that refers to (...) difference on several epistemic and social dimensions makes good sense of representative historical cases. If this argument is tenable it suggests more continuity in the epistemology and politics of science than has been claimed by a new paradigm of science studies and politics during recent decades. (shrink)

The paper defends the structural concept of representation, defined by homomorphisms, against the main objections that have been raised against it: Logical objections, the objection from misrepresentation, the objection from failing necessity, and the copy theory objection. Homomorphic representations are not necessarily ‘copies’ of their representanda, and thus can convey scientific insight.

The use of the phrase “basic research” as a term used in science policy discussion dates only to about 1920. At the time the phrase referred to what we today commonly refer to as applied research in support of specific missions or goals, especially agriculture. Upon the publication of Vannevar Bush’s well-known report, Science – The Endless Frontier, the phrase “basic research” became a key political symbol, representing various identifications, expectations and demands related to science policy among scientists and politicians. (...) This paper tracks and evaluates the evolution of “basic research” as a political symbol from early in the 20th century to the present. With considerable attention having been paid to the on-going evolution of post-Cold War science policy, much less attention has focused on the factors which have shaped the dominant narrative of contemporary science policies. (shrink)

Unlike basic sciences, scientific research in advanced technologies aims to explain, predict, and (mathematically) 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. (shrink)

Following Herbert Simon’s idea of “the sciences of the artificial”, one may contrast descriptive sciences and design sciences: the former are concerned with “how things are”, the latter tell us “how things ought to be in order to attain goals, and to function”. Typical results of design sciences are thus expressions about means—ends relations or technical norms in G. H. von Wright’s sense. Theorizing and modeling are important methods of giving a value-free epistemic justification for such technical norms. The values (...) of design sciences are not criteria for the acceptance of theories or models, but rather antecedents of conditional recommendations of actions. Design sciences are thus value-neutral and value-laden at the same time. (shrink)

There is a long tradition of arguing that design and science are importantly different. One such argument is that the separation of science and design is an implication that can be drawn from the Simon–Kroes model of the nature of technical artifacts. This paper argues that the Simon–Kroes model does not imply a radical separation between science and design: if we accept the Simon–Kroes model of the nature of technical artifacts and their production, then we must also accept that all (...) the sciences also produce technical artifacts, and in importantly similar ways. Moreover, the placing of both science and design in a naturalist framework reinforces this conclusion and opens up new vistas for synergetic cross-disciplinary discussion of design and methodology. (shrink)

This book discusses the relationship between the philosophy of science and philosophy of engineering, and demonstrates how philosophers of engineering design as well as design researchers can benefit from the conceptual toolkit that the philosophy of science has to offer. In this regard, it employs conceptual tools from the philosophical literature on scientific explanation to address key issues in engineering design and philosophy of engineering design. Specifically, the book focuses on assessing the explanatory value of function ascriptions used in engineering (...) design and philosophy of technical functions; on elaborating the structure of explanation in engineering design; on assessing the role and value of design representations in engineering design and philosophy thereof; and on elaborating means for the testing of design methods. Presenting a novel and effective approach to tackling key issues in the field, philosophers of engineering and design alike will greatly benefit from this book. (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)