Ian Hacking’s Representing and Intervening is often credited as being one of the first works to focus on the role of experimentation in philosophy of science, catalyzing a movement which is sometimes called the “philosophy of experiment” or “new experimentalism”. In the 1980s, a number of other movements and scholars also began focusing on the role of experimentation and instruments in science. Philosophical study of experimentation has thus seemed to be an invention of the 1980s whose central figure is Hacking. (...) This article aims to assess this historical claim, made by Hacking himself as well as others. It does so first by highlighting how a broader perspective on the history of philosophy reveals this invention narrative to be incorrect, since experimentation was a topic of interest for earlier philosophers. Secondly, the article evaluates a revision of this historical claim also made by some philosophers of experiment: the rediscovery narrative, which frames Hacking and others as having rediscovered the work of these earlier authors. This second narratives faces problems as well. Therefore we develop a third narrative which we call the contextualist narrative. Rather than considering experimentation in an essentialist manner as a fixed research object that is either present or not in the work of specific authors, experimentation should be addressed through a narrative that asks in what way it becomes a philosophical problem for certain authors and for what purpose. Such contextualization enables a repositioning of Hacking’s philosophy of experiment in relation to the specific debates in which he intervened, such as the realism-antirealism debate, the Science Wars and the debate on incommensurability. (shrink)

Philosophy of Physics has emerged recently as a scholarly important subfield of philosophy of science. However outside the small community of experts it is not a well-known field. It is not clear even to experts the exact nature of the field: how much philosophical is it? What is its relation to physics? In this work it is presented an overview of philosophy of physics that tries to answer these and other questions.

Abstract : A measurement result is never absolutely accurate: it is affected by an unknown “measurement error” which characterizes the discrepancy between the obtained value and the “true value” of the quantity intended to be measured. As a consequence, to be acceptable a measurement result cannot take the form of a unique numerical value, but has to be accompanied by an indication of its “measurement uncertainty”, which enunciates a state of doubt. What, though, is the value of measurement uncertainty? What (...) is its numerical value: how does one calculate it? What is its epistemic value: how one should interpret a measurement result? Firstly, we describe the statistical models that scientists make use of in contemporary metrology to perform an uncertainty analysis, and we show that the issue of the interpretation of probabilities is vigorously debated. This debate brings out epistemological issues about the nature and function of physical measurements, metrologists insisting in particular on the subjective aspect of measurement. Secondly, we examine the philosophical elaboration of metrologists in their technical works, where they criticize the use of the notion of “true value” of a physical quantity. We then challenge this elaboration and defend such a notion. The third part turns to a specific use of measurement uncertainty in order to address our thematic from the perspective of precision physics, considering the activity of the adjustments of physical constants. In the course of this activity, physicists have developed a dynamic conception of the accuracy of their measurement results, oriented towards a future progress of knowledge, and underlining the epistemic virtues of a never-ending process of identification and correction of measurement errors. (shrink)

The use of neuroscientific evidence in criminal trials has been steadily increasing. Despite progress made in recent decades in understanding the mechanisms of psychological and behavioral functioning, neuroscience is still in an early stage of development and its potential for influencing legal decision-making is highly contentious. Scholars disagree about whether or how neuroscientific evidence might impact prescriptions of criminal culpability, particularly in instances in which evidence of an accused’s history of mental illness or brain abnormality is offered to support a (...) plea of not criminally responsible. In the context of these debates, philosophers and legal scholars have identified numerous problems with admitting neuroscientific evidence in legal contexts. To date, however, less has been said about the challenges of evaluating the evidence upon which integrative mechanistic explanations that bring together evidence from different areas of neuroscience are based. As we explain, current criteria for evaluating such evidence to determine its admissibility in legal contexts are inadequate. Appealing to literature in the philosophy of scientific experimentation and theoretical work in the social, cognitive and behavioral sciences, we lay the groundwork for reforming these criteria and identify some of the implications of modifying them. (shrink)

Several advocates of the lively field of “metaphysics of science” have recently argued that a naturalistic metaphysics should be based solely on current science, and that it should replace more traditional, intuition-based, forms of metaphysics. The aim of the present paper is to assess that claim by examining the relations between metaphysics of science and general metaphysics. We show that the current metaphysical battlefield is richer and more complex than a simple dichotomy between “metaphysics of science” and “traditional metaphysics”, and (...) that it should instead be understood as a three dimensional “box”, with one axis distinguishing “descriptive metaphysics” from “revisionary metaphysics”, a second axis distinguishing a priori from a posteriori metaphysics, and a third axis distinguishing “commonsense metaphysics”, “traditional metaphysics” and “metaphysics of science”. We use this three-dimensional figure to shed light on the project of current metaphysics of science, and to demonstrate that, in many instances, the target of that project is not defined with enough precision and clarity. (shrink)

Observation and experiment as categories for analysing scientific practice have a long pedigree in writings on science. There has, however, been little attempt to delineate observation and experiment with respect to analysing scientific practice; in particular, scientific experimentation, in a systematic manner. Someone who has presented a systematic account of observation and experiment as categories for analysing scientific experimentation is Ian Hacking. In this paper, I present a detailed analysis of Hacking’s observation versus experiment account. Using a range of cases (...) from various fields of scientific enquiry, I argue that the observation versus experiment account is not an adequate framework for delineating scientific experimentation in a systematic manner. (shrink)

Experiments are actions, performed in order to gain information. Like other acts, there are virtues of performing them well. I discuss one virtue of experimentation, that of knowing how to trade its information-gaining potential against other goods.

My dissertation explores the ways in which Rudolf Carnap sought to make philosophy scientific by further developing recent interpretive efforts to explain Carnap’s mature philosophical work as a form of engineering. It does this by looking in detail at his philosophical practice in his most sustained mature project, his work on pure and applied inductive logic. I, first, specify the sort of engineering Carnap is engaged in as involving an engineering design problem and then draw out the complications of design (...) problems from current work in history of engineering and technology studies. I then model Carnap’s practice based on those lessons and uncover ways in which Carnap’s technical work in inductive logic takes some of these lessons on board. This shows ways in which Carnap’s philosophical project subtly changes right through his late work on induction, providing an important corrective to interpretations that ignore the work on inductive logic. Specifically, I show that paying attention to the historical details of Carnap’s attempt to apply his work in inductive logic to decision theory and theoretical statistics in the 1950s and 1960s helps us understand how Carnap develops and rearticulates the philosophical point of the practical/theoretical distinction in his late work, offering thus a new interpretation of Carnap’s technical work within the broader context of philosophy of science and analytical philosophy in general. (shrink)

This inaugural handbook documents the distinctive research field that utilizes history and philosophy in investigation of theoretical, curricular and pedagogical issues in the teaching of science and mathematics. It is contributed to by 130 researchers from 30 countries; it provides a logically structured, fully referenced guide to the ways in which science and mathematics education is, informed by the history and philosophy of these disciplines, as well as by the philosophy of education more generally. The first handbook to cover the (...) field, it lays down a much-needed marker of progress to date and provides a platform for informed and coherent future analysis and research of the subject. -/- The publication comes at a time of heightened worldwide concern over the standard of science and mathematics education, attended by fierce debate over how best to reform curricula and enliven student engagement in the subjects There is a growing recognition among educators and policy makers that the learning of science must dovetail with learning about science; this handbook is uniquely positioned as a locus for the discussion. -/- The handbook features sections on pedagogical, theoretical, national, and biographical research, setting the literature of each tradition in its historical context. Each chapter engages in an assessment of the strengths and weakness of the research addressed, and suggests potentially fruitful avenues of future research. A key element of the handbook’s broader analytical framework is its identification and examination of unnoticed philosophical assumptions in science and mathematics research. It reminds readers at a crucial juncture that there has been a long and rich tradition of historical and philosophical engagements with science and mathematics teaching, and that lessons can be learnt from these engagements for the resolution of current theoretical, curricular and pedagogical questions that face teachers and administrators. (shrink)

The literature on the indispensability argument for mathematical realism often refers to the ‘indispensable explanatory role’ of mathematics. I argue that we should examine the notion of explanatory indispensability from the point of view of specific conceptions of scientific explanation. The reason is that explanatory indispensability in and of itself turns out to be insufficient for justifying the ontological conclusions at stake. To show this I introduce a distinction between different kinds of explanatory roles—some ‘thick’ and ontologically committing, others ‘thin’ (...) and ontologically peripheral—and examine this distinction in relation to some notable ‘ontic’ accounts of explanation. I also discuss the issue in the broader context of other ‘explanationist’ realist arguments. (shrink)

Scientific knowledge production is currently affected by the dissemination of data on an unprecedented scale. Technologies for the automated production and sharing of vast amounts of data have changed the way in which data are handled and interpreted in several scientific domains, most notably molecular biology and biomedicine. In these fields, the activity of data gathering has become increasingly technology-driven, with machines such as next generation genome sequencers and mass spectrometers generating billions of data points within hours, and with little (...) need for human supervision. Given the relative ease and low costs with which datasets can be produced (that is, once a laboratory has been able to afford .. (shrink)

The mechanistic approach in philosophy of science contributes to our understanding of experimental design. Applying the mechanistic approach to experimentation in computing is beneficial for two reasons. It connects the methodology of experimentation in computing with the methodology of experimentation in established sciences, thereby strengthening the scientific reputability of computing and the quality of experimental design therein. Furthermore, it pinpoints the idiosyncrasies of experimentation in computing: computing deals closely with both natural and engineered mechanisms. Better understanding of the idiosyncrasies, which (...) manifest in terms of a nonstandard role for experimentation, are interesting both for computer scientists and for philosophers of science. Computer scientists can think more clearly about their experimental choices. The role of experimentation elucidated by computer science merits further study from philosophers of science generally, as it highlights a role for experimentation hitherto unrecognized by philosophers: demonstration that activities exist. (shrink)

ArgumentIn the theory-dominated view of scientific experimentation, all relations of theory and experiment are taken on a par; namely, that experiments are performed solely to ascertain the conclusions of scientific theories. As a result, different aspects of experimentation and of the relations of theory to experiment remain undifferentiated. This in turn fosters a notion of theory-ladenness of experimentation (TLE) that is toocoarse-grainedto accurately describe the relations of theory and experiment in scientific practice. By contrast, in this article, I suggest that (...) TLE should be understood as anumbrella conceptthat has different senses. To this end, I introduce a three-fold distinction among the theories of high-energy particle physics (HEP) as background theories, model theories, and phenomenological models. Drawing on this categorization, I contrast two types of experimentation, namely, “theory-driven” and “exploratory” experiments, and I distinguish between the “weak” and “strong” senses of TLE in the context of scattering experiments from the history of HEP. This distinction enables identifying the exploratory character of the deep-inelastic electron-proton scattering experiments – performed at the Stanford Linear Accelerator Center (SLAC) between the years 1967 and 1973 – thereby shedding light on a crucial phase of the history of HEP, namely, the discovery of “scaling,” which was the decisive step towards the construction of quantum chromo-dynamics as a gauge theory of strong interactions. (shrink)

A recent and growing discussion in philosophy addresses the construction of models and their use in scientific reasoning by comparison with fiction. This comparison helps to explore the problem of mediated observation and, hence, the lack of an unambiguous reference of representations. Examining the usefulness of the concept of fiction for a comparison with non-denoting elements in science, the aim of this paper is to present reasonable grounds for drawing a distinction between these two kinds of representation. In particular, my (...) account will suggest a demarcation between fictional and non-fictional discourse as involving two different ways of interpreting representations. This demarcation, leading me to distinguish between fictional and non-fictional forms of enquiry, will provide a useful tool to explore to what extent the descriptions given by a model can be justified as making claims about the world and to what degree they are a consequence of the model’s particular construction. (shrink)

In his pioneering sociological theory, which makes phenomenological concepts fruitful for the social sciences, Alfred Schütz has laid foundations for a characterization of an manifold of distinct domains of experience. My aim here is to further develop this pluralist theory of experience by buttressing and extending the elements of diversity that it includes, and by eliminating or minimizing lingering imbalances among the domains of experience. After a critical discussion of the criterion-catalogue Schütz develops for the purpose of characterizing different cognitive (...) styles, I move on to examine its application to one special style, the lifeworld. I appeal, on the one hand, to Husserl's characterization of the lifeworld as a world of perception, and on the other hand to the layer-model of the lifeworld developed by Schütz and Thomas Luckmann. A consequence of this approach is that the lifeworld appears as a socially definable context that is detached from other experiences but on an equal footing with them with respect to their claim of validity. The term "lifeworld" does not denote a category that encompasses culture or nature but refers to a delimited action-space. Finally, I draw upon Schütz' s criterion-catalogue to characterize two domains of experience outside of the lifeworld, which play a central role for the process of differentiation of experience in modernity and for the phenomenological analysis of types of experience: experimental science and subjectivity. (shrink)

The question about the scientific nature of computing has been widely debated with no universal consensus reached about its disciplinary status. Positions vary from acknowledging computing as the science of computers to defining it as a synthetic engineering discipline. In this paper, we aim at discussing the nature of computing from a methodological perspective. We consider, in particular, the nature and role of experiments in this field, whether they can be considered close to the traditional experimental scientific method or, instead, (...) they possess peculiar and unique features. We argue that this experimental perspective should be taken into account when discussing the status of computing. We critically survey how the experimental method has been conceived and applied in computing, and some open issues that could be tackled with the aid of the history of science, the philosophy of science, and the philosophy of technology. (shrink)

A pesquisa em informação quântica sugere uma íntima conexão entre o conceito de informação e a teoria quântica, mas essa conexão envolve nuances cuja análise é o objeto deste trabalho. A sabedoria comum nesse campo divide-se em duas grandes áreas, não excludentes entre si. Há os que são movidos pela possibilidade de uso da teoria quântica em um novo campo, o da computação, independentemente do esclarecimento de seus fundamentos, aqui incluído o conceito de "informação". Alguns consideram que estamos diante de (...) um grande problema conceitual sem resposta satisfatória no momento, enquanto que outros, dentre os que reconhecem a magnitude do problema, têm proposto formulações com a pretensão de solução do problema. Este artigo tem pretensões modestas. Não pretendemos aportar novas soluções ao problema, nem apoiar uma das soluções existentes. Temos a expectativa de através da análise histórico-conceitual do problema mapear as diversas possibilidades, apontando o que nos parecem ser aspectos fortes e fracos nessas possibilidades. Research in quantum information suggests a close connection between information and quantum theory. The aim of this article is to analyze nuances involved in this connection. Scientists in this field are divided into two overlapping camps. Some are motivated only by the use of quantum features to improve information processing, in spite of concerns about the foundations of the quantum theory, while others recognize deep conceptual problems of this theory, and attempt to solve them. This article has modest ambitions. It aims only to chart, by way of historical and conceptual analysis, the diverse possibilities available, indicating the strengths and weaknesses of each of them. (shrink)

I describe conventions not of correct reasoning but of giving and taking advice about reasoning. This article is asn anticipation of part of the first chapter of my forthcoming *Bounded Thinking*, OUP 2012.

Ronald Giere embraces the perspective of distributed cognition to think about cognition in the sciences. I argue that his conception of distributed cognition is flawed in that it bears all the marks of its predecessor; namely, individual cognition. I show what a proper (i.e. non-individual) distributed framework looks like, and highlight what it can and cannot do for the philosophy of science.

In this article, I introduce the notion of horizon for scientific practice (HSP), representing limits or boundaries within which scientists ply their trade, to facilitate analysis of scientific discovery and progress. The notion includes not only constraints that delimit scientific practice, e.g. of bringing experimentation to a temporary conclusion, but also possibilities that open up scientific practice to additional scientific discovery and to further scientific progress. Importantly, it represents scientific practice as a dynamic and developmental integration of activities to investigate (...) and analyze the natural world. I use the discovery of the clotting factor, thrombin, and the experiments conducted by the Johns Hopkins physiologist, William Howell, on the enzymatic nature of thrombin to illustrate the notion of HSP. In a concluding section, I compare the notion of HSP to other notions for scientific practice proposed in the history and philosophy of science literature. (shrink)

Experimental activity is traditionally identified with testing the empirical implications or numerical simulations of models against data. In critical reaction to the ‘tribunal view’ on experiments, this essay will show the constructive contribution of experimental activity to the processes of modeling and simulating. Based on the analysis of a case in fluid mechanics, it will focus specifically on two aspects. The first is the controversial specification of the conditions in which the data are to be obtained. The second is conceptual (...) clarification, with a redefinition of concepts central to the understanding of the phenomenon and the conditions of its occurrence. (shrink)

The modern understanding of radiation got its start in 1895 with X-rays discovered by Wilhelm Röntgen, followed in 1896 by Henri Becquerel’s discovery of radioactivity. The development of the study of radiation opened a vast field of research concerning various disciplines: chemistry, physics, biology, geology, sociology, ethics, etc. Additionally, new branches of knowledge were created, such as atomic and nuclear physics that enabled an in-depth knowledge of the matter. Moreover, during the historical evolution of this body of knowledge a wide (...) variety of new technologies was emerging. This article seeks to analyze the characteristics of experimental research in radioactivity and microphysics, in particular the relationship experience-theory. It will also be emphasized that for more than two decades, since the discovery of radioactivity, experiments took place without the theory being able to follow experimental dynamics. Some aspects identified as structural features of scientific research in the area of radiation and matter will be addressed through historical examples. The inventiveness of experiments in parallel with the emergence of quantum mechanics, the formation of teams and their relationship with technology developed from the experiments, as well as the evolution of microphysics in the sense of “Big Science” will be the main structural characteristics here focused. The case study of research in radioactivity in Portugal that assumes a certain importance and has structural characteristics similar to those of Europe will be presented. (shrink)

We propose a new account of calibration according to which calibrating a technique shows that the technique does what it is supposed to do. To motivate our account, we examine an early twentieth-century debate about chlorophyll chemistry and Mikhail Tswett’s use of chromatographic adsorption analysis to study it. We argue that Tswett’s experiments established that his technique was reliable in the special case of chlorophyll without relying on either a theory or a standard calibration experiment. We suggest that Tswett broke (...) the experimenters’ regress by appealing to material facts in the common ground for chemists at the time. (shrink)

This paper defends the naïve thesis that the method of experiment has per se an epistemic superiority over the method of computer simulation, a view that has been rejected by some philosophers writing about simulation, and whose grounds have been hard to pin down by its defenders. I further argue that this superiority does not come from the experiment’s object being materially similar to the target in the world that the investigator is trying to learn about, as both sides of (...) dispute over the epistemic superiority thesis have assumed. The superiority depends on features of the question and on a property of natural kinds that has been mistaken for material similarity. Seeing this requires holding other things equal in the comparison of the two methods, thereby exposing that, under the conditions that will be specified, the simulation is necessarily epistemically one step behind the corresponding experiment. Practical constraints like feasibility and morality mean that scientists do not often face an other-things- equal comparison when they choose between experiment and simulation. Nevertheless, I argue, awareness of this superiority and of the general distinction between experiment and simulation is important for maintaining motivation to seek answers to new questions. (shrink)

Neuroimaging technology is the most widely used tool to study human cognition. While originally a promising tool for mapping the content of cognitive theories onto the structures of the brain, recently developed tools for the analysis, handling and sharing of data have changed the theoretical landscape of cognitive neuroscience. Even with these advancements philosophical analyses of evidence in neuroimaging remain skeptical of the promise of neuroimaging technology. These views often treat the analysis techniques used to make sense of data produced (...) in a neuroimaging experiment as one, attributing the inferential limitations of analysis pipelines to the technology as a whole. Situated against the neuroscientists own critical assessment of their methods and the limitations of those methods, this skepticism appears based on a misunderstanding of the role data analysis techniques play in neuroimaging research. My project picks up here, examining how data analysis techniques, such as pattern classification analysis, are used to assess the evidential value of neuroimaging data. The project takes the form of three papers. In the first I identify the use of multiple data analysis techniques as an important aspect of the data interpretation process that is overlooked by critics. In the second I develop an account of inferences in neuroimaging research that is sensitive to this use of data analysis techniques, arguing that interpreting neuroimaging data is a process of isolating and explaining a variety of data patterns. In the third I argue that the development and uptake of new techniques for analyzing data must be accompanied by changes in research practices and standards of evidence if they are to promote knowledge generation. My approach to this work is both traditionally philosophical, insofar as it involves reading and analyzing the work of philosophers and neuroscientists, and embedded insofar as most of the research was conducted while engaged in attending lab meetings and participating in the work of those scientists whose work is the object of my research. (shrink)

It is sometimes said that simulation can serve as epistemic substitute for experimentation. Such a claim might be suggested by the fast-spreading use of computer simulation to investigate phenomena not accessible to experimentation. But what does that mean? The paper starts with a clarification of the terms of the issue and then focuses on two powerful arguments for the view that simulation and experimentation are ‘epistemically on a par’. One is based on the claim that, in experimentation, no less than (...) in simulation, it is not the system under study that is manipulated but a system that ‘stands-in’ for it. The other one highlights the pervasive use of models in experimentation. It will be argued that these arguments, as compelling as they might seem, are each based on a mistaken interpretation of experimentation and that, far from simulation and experimentation being epistemically on a par, they do not have the same epistemic function, do not produce the same kind of epistemic results. (shrink)

The main problems faced by a conception of perception as passive will be introduced through a critical examination of John McDowell's account of 'empirical thinking'. Overcoming these difficulties will lead to a conception of perception as involving an active cognitive participation of the perceiver, and an account of how observational judgment is warranted that is focused on the conditions of experience. In both cases, analogies to inquiry in scientific experimental practice will be explored.

Seit Beginn der frühen Neuzeit ist das naturwissenschaftliche Verfahren maßgeblich durch ein neues Konzept geprägt: das Konzept des experimentellen, gestalterischen Eingriffs in die Natur. Es geht nun nicht mehr darum, eine Geschichte der "freien und ungebundenen Natur" (Bacon) zu erzählen, die in ihrem eigenen Lauf belassen und als vollkommene Bildung betrachtet wird. Es geht vielmehr darum, der "gebundenen und bezwungenen Natur" (Bacon) vermittels der experimentellen Tätigkeit des Menschen die Geheimnisse zu entreißen. Diese technisch-praktische Konzeption grenzt sich explizit von den klassischen (...) kontemplativen Wissenschaftsvorstellungen der Antike ab. Wie es Kant paradigmatisch in Bezug auf Bacon formuliert hat, ist diese "Revolution der Denkart" maßgeblich durch ein gewandeltes Verständnis des Verhältnisses des Menschen zur Natur geprägt. Der Mensch als Experimentator hat für Kant nicht mehr die "Qualität eines Schülers", der sich passiv von der Natur belehren läßt und an ihrem "Leitbande" (Kant) gegängelt wird. Seine neu gewonnene Autorität verleiht ihm vielmehr den Status eines Richters, der nun die Natur nötigen kann, auf gestellte Fragen zu antworten. Die Laborforschung der modernen Naturwissenschaft ist von den Formen der alten Naturwissenschaft so weit entfernt, daß sie den Vorwurf auf sich zog, sie untersuche Artekakte, aber nicht Natur. Die grundlegenden Theorien über Natur können in der Regel nur unter den künstlichen Bedingungen des Labors aufgestellt werden. Daraus darf aber nicht geschlossen werden, die anhand von Laborphänomenen aufgestellten und getesteten Theorien handelten nicht von der Natur außerhalb der Labore. Aber ihrer exakten und detaillierten Anwendung auf Prozesse außerhalb der Labore stehen eine Fülle von Schwierigkeiten entgegen. Insofern markiert das Labor sehr wohl eine Grenze exakter Naturforschung, die für den Umgang der wissenschaftlich-technischen Zivilisation mit der Natur wichtige Konsequenzen hat. Inhalt: Kristian Köchy / Gregor Schiemann: Natur im Labor Lothar Schäfer: Die Erscheinung der Natur unter Laborbedingungen Holm Tetens: Das Labor als Grenze der exakten Naturforschung Christoph Rehmann-Sutter: Genes in Labs - Concepts of Development and the Standard Environment Kristian Köchy: Lebewesen im Labor. Das Experiment in der Biologie Jutta Weber: Mannigfaltige Techno-Naturen. Von epistemischen Modellsystemen und situierten Maschinen Thomas Sören Hoffmann: Gezeigte versus sich zeigende Natur. Eine Skizze im Blick auf das Verhältnis von Labor und Natur Klaus Michael Meyer-Abich: Laborforschung im Erkenntnishandeln der Experimentiergesellschaft. Eine holistisch-pragmatische Perspektive für die Wissenschaftstheorie. (shrink)

Synthetic biology is an umbrella term that covers a range of aims, approaches, and techniques. They are all brought together by common practices of analogizing, synthesizing, mechanicizing, and kludging. With a focus on kludging as the connection point between biology, engineering, and evolution, I show how synthetic biology’s successes depend on custom-built kludges and a creative, “make-it-work” attitude to the construction of biological systems. Such practices do not fit neatly, however, into synthetic biology’s celebration of rational design. Nor do they (...) straightforwardly embody Richard Feynman’s “last blackboard” statement that without creating something it cannot be understood. Reflecting further on the relationship between synthetic construction and knowledge making gives philosophy of science new avenues of insight into scientific practice. (shrink)

The aim of this thesis dissertation is to propose a novel position in the debate on scientific realism, modal empiricism, and to show its fruitfulness when it comes to interpreting the cognitive content of scientific theories. Modal empiricism is an empiricist position, according to which the aim of science is to produce empirically adequate theories rather than true theories. However, it suggests adopting a broader comprehension of experience than traditional versions of empiricism, through a commitment to natural modalities. Following modal (...) empiricism, there are possibilities in nature, and constraints on what is possible, and a theory is empirically adequate if it correctly delimits the range of possible experiences. The position rests on a situated and pragmatic conception of natural modalities and of empirical confrontation. We claim that it can do justice to the empirical success of science, while not falling prey to the problem of theory change that undermines scientific realism. We explain how constraints of necessity on phenomena can be known by induction, and how this modal epistemology fits with scientific practice. Finally, we claim that a commitment to natural modalities allows for a rich interpretation of the cognitive content of theories. Modal empiricism could renew some metaphysical debates within a pragmatist framework, by tying them to experience and not being constrained by realist prejudices. -/- L'objet de cette thèse est de proposer une position originale dans le débat sur le réalisme scientifique, l'empirisme modal, et d'en démontrer la fructuosité quand il s'agit de tirer des enseignements du contenu cognitif des théories scientifiques. L'empirisme modal est une position empiriste, suivant laquelle le but de la science n'est pas de produire des théories vraies, mais des théories empiriquement adéquate. Cependant, il propose d'adopter un cadre plus large que les versions traditionnelles d'empirisme pour penser l'expérience, en incorporant un engagement envers les modalités naturelles, ou l'idée qu'il y a du possible dans la nature, et des contraintes sur les possibles. Nos théories sont empiriquement adéquates si elles délimitent correctement l'étendue des expériences possibles. Cette position s'appuie sur une conception située et pragmatique des modalités naturelles et de la confrontation empirique. Nous prétendons qu'elle est à même de rendre justice au succès empirique des sciences, sans pour autant faire face au problème du changement théorique qui mine le réalisme scientifique. Nous expliquons comment les contraintes de nécessité sur les phénomènes peuvent être connues à l'issue d'une induction, et en quoi cette façon de voir s'accorde avec la pratique scientifique. Enfin, nous affirmons qu'un engagement envers les modalités naturelles offre une richesse interprétative à même de renouveler, dans un cadre pragmatiste, plus ouvert que le réalisme, certaines questions métaphysiques tout en les ramenant à l'expérience . (shrink)

Computer simulations and experiments share many important features. One way of explaining the similarities is to say that computer simulations just are experiments. This claim is quite popular in the literature. The aim of this paper is to argue against the claim and to develop an alternative explanation of why computer simulations resemble experiments. To this purpose, experiment is characterized in terms of an intervention on a system and of the observation of the reaction. Thus, if computer simulations are experiments, (...) either the computer hardware or the target system must be intervened on and observed. I argue against the first option using the non-observation argument, among others. The second option is excluded by e.g. the over-control argument, which stresses epistemological differences between experiments and simulations. To account for the similarities between experiments and computer simulations, I propose to say that computer simulations can model possible experiments and do in fact often do so. (shrink)

Experiments are commonly thought to have epistemic privilege over simulations. Two ideas underpin this belief: first, experiments generate greater inferential power than simulations, and second, simulations cannot surprise us the way experiments can. In this article I argue that neither of these claims is true of experiments versus simulations in general. We should give up the common practice of resting in-principle judgments about the epistemic value of cases of scientific inquiry on whether we classify those cases as experiments or simulations, (...) per se. To the extent that either methodology puts researchers in a privileged epistemic position, this is context sensitive. (shrink)

Francis Bacon's experimental philosophy is discussed, and the way in which it not only shapes scientific methodology but also deeply pervades all philosophical and social learning. Bacon draws us in to participate in an experiment with experience. The central driving force is the idea that learning how to learn is necessary in order to know. To meet this requirement, he considers the relation of form and content of pivotal importance, and therefore the selection of the literary form and the form (...) of data inscription is decisive in the construction of a heuristic tool. His inductive method serves a dual purpose: first, the so-called indicative form aims at securing knowledge by a comprehensible procedure that controls and guides hypothetical thinking. Second, the literary forms "fragment" and "aphorism" embody the subjunctive, and invite intellectual openness and even speculation. In this article, special emphasis is put on Bacon's use and justification of the aphorism. Bacon's pervasive experimentalism meets in some sense today's broad adoption of the experimental mode. His philosophy calls for an ontology that is also at work in recent notions of co-action and co-working, or of affordance. (shrink)

Today, mechanisms and mechanistic explanation are very popular in philosophy of science and are deemed a welcome alternative to laws of nature and deductive‐nomological explanation. Starting from Mitchell's pragmatic notion of laws, I cast doubt on their status as a genuine alternative. I argue that (1) all complex‐systems mechanisms ontologically must rely on stable regularities, while (2) the reverse need not hold. Analogously, (3) models of mechanisms must incorporate pragmatic laws, while (4) such laws themselves need not always refer to (...) underlying mechanisms. Finally, I show that Mitchell's account is more encompassing than the mechanistic account *Received August 2008; revised January 2010. †To contact the author, please write to: Centre for Logic and Philosophy of Science, Ghent University, Blandijnberg 2, B‐9000 Belgium; e‐mail: [email protected]. (shrink)

In contrast to China’s efforts to upgrade its system of governance around a stupefying amount of data collection and electronic scoring, countries committed to democracy and human rights did not upgrade their systems. Instead, those countries ended up with surveillance capitalism. It is vital for the survival of those ideas about governance to perform such an upgrade. This paper aims to contribute to that goal. I propose a framework of epistemic actorhood in terms of four roles and characterize digital lifeworlds (...) and what matters about them both in terms of how they fit in with Max Tegmark’s distinctions among stages of life and in terms of how they generate their own episteme, the data episteme, with its immense possibilities of infopower. Epistemic rights that strengthen existing human rights – as part of a fourth generation of rights – are needed to protect epistemic actorhood in those roles. In the long run, we might well need a new kind of right, the right to the exercise of genuinely human intelligence. (shrink)

Understanding nature of science is widely considered an important educational objective and views of NOS are closely linked to science teaching and learning. Thus there is a lively discussion about what understanding NOS means and how it is reached. As a result of analyses in educational, philosophical, sociological and historical research, a worldwide consensus about the content of NOS teaching is said to be reached. This consensus content is listed as a general statement of science, which students are supposed to (...) understand during their education. Unfortunately, decades of research has demonstrated that teachers and students alike do not possess an appropriate understanding of NOS, at least as far as it is defined at the general level. One reason for such failure might be that formal statements about the NOS and scientific knowledge can really be understood after having been contextualized in the actual cases. Typically NOS is studied as contextualized in the reconstructed historical case stories. When the objective is to educate scientifically and technologically literate citizens, as well as scientists of the near future, studying NOS in the contexts of contemporary science is encouraged. Such contextualizations call for revision of the characterization of NOS and the goals of teaching about NOS. As a consequence, this article gives two examples for studying NOS in the contexts of scientific practices with practicing scientists: an interview study with nanomodellers considering NOS in the context of their actual practices and a course on nature of scientific modelling for science teachers employing the same interview method as a studying method. Such scrutinization opens rarely discussed areas and viewpoints to NOS as well as aspects that practising scientists consider as important. (shrink)

It is argued that in deterministic contexts evidence for causal relations states whether a boundary condition makes a difference or not to a phenomenon. In order to substantiate the analysis, I show that this difference/indifference making is the basic type of evidence required for eliminative induction in the tradition of Francis Bacon and John Stuart Mill. To this purpose, an account of eliminative induction is proposed with two distinguishing features: it includes a method to establish the causal irrelevance of boundary (...) conditions by means of indifference making, which is called strict method of agreement, and it introduces the notion of a background against which causal statements are evaluated. Causal statements thus become three-place-relations postulating the relevance or irrelevance of a circumstance C to the examined phenomenon P with respect to a background B of further conditions. To underline the importance of evidence in terms of difference/indifference making, I sketch two areas, in which eliminative induction is extensively used in natural and engineering sciences. One concerns exploratory experiments, the other engineering design methods. Given that a method is discussed that has been used for centuries, I make no claims to novelty in this paper, but hope that the combined discussion of several topics that are still somewhat underrepresented in the philosophy of science literature is of some merit. (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)

This article argues for a different outlook on the concept of extension, especially for the reference of general terms in scientific practice. Scientific realist interpretations of the two predominant theories of meaning, namely Descriptivism and Causal Theory, contend that a stable cluster of descriptions or an initial baptism fixes the extension of a general term such as a natural kind term. This view in which the meaning of general terms is presented as monosemantic and the referents as stable, homogeneous, and (...) unchangeable, however, does not reflect the various practices involved in the investigation of research materials and the related application of general terms in scientific practice. By drawing on the taxonomic diversity, particularly of structure-based classifications in chemical databases, this article illustrates the limited utility of such a concept of extension. Research materials often exhibit a plurality of material dimensions that, within different research contexts, allow for various and often equally significant taxonomic demarcations. In light of this, the extension of a general term cannot be uniquely determined by a supposedly independent nature of the referent but is relative to the model context under which the materials are investigated. This significance and plurality of the model context, I claim, needs to be mirrored in an account of meaning that is supposed to reflect scientific reality. On this account, the aim of this article is to present an alternative perspective on the concept of extension to accommodate the diverse material practices that determine the application of general terms in science. (shrink)

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)

Experimentation represents today a ‘hot’ topic in computing. If experiments made with the support of computers, such as computer simulations, have received increasing attention from philosophers of science and technology, questions such as “what does it mean to do experiments in computer science and engineering and what are their benefits?” emerged only recently as central in the debate over the disciplinary status of the discipline. In this work we aim at showing, also by means of paradigmatic examples, how the traditional (...) notion of controlled experiment should be revised to take into account a part of the experimental practice in computing along the lines of experimentation as exploration. Taking inspiration from the discussion on exploratory experimentation in the philosophy of science—experimentation that is not theory-driven—we advance the idea of explorative experiments that, although not new, can contribute to enlarge the debate about the nature and role of experimental methods in computing. In order to further refine this concept we recast explorative experiments as socio-technical experiments, that test new technologies in their socio-technical contexts. We suggest that, when experiments are explorative, control should be intended in a posteriori form, in opposition to the a priori form that usually takes place in traditional experimental contexts. (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)

The program of intervening, manipulating, constructing and creating is central to natural and engineering sciences. A renewed wave of interest in this program has emerged within the recent practices and discourse of nano-technoscience. However, it is striking that, framed from the perspective of well-established epistemologies, the constructed technoscientific objects and engineered things remain invisible. Their ontological and epistemological status is unclear. The purpose of the present paper is to support present-day approaches to techno-objects ( ontology ) insofar as they make (...) these hidden objects epistemologically perceivable. To accomplish this goal, it is inspiring to look back to the origin of the project of modernity and to its founding father: Francis Bacon. The thesis is that everything we need today for an adequate (dialectic-materialist), ontologically well-informed epistemology of technoscience can be found in the works of Bacon—this position will be called epistemological real-constructivism. Rather than describing it as realist or constructivist, empiricist or rationalist, Bacon’s position can best be understood as real-constructivist since it challenges modern dichotomies, including the dichotomy between epistemology and ontology. Such real-constructive turn might serve to promote the acknowledgement that natural and engineering sciences, in particular recent technosciences, are creating and producing the world we live in. Reflection upon the contemporary relevance of Bacon is intended as a contribution to the expanding and critical discussion on nano-technoscience. (shrink)

Many activities in Cognitive Science involve complex computer models and simulations of both theoretical and real entities. Artificial Intelligence and the study of artificial neural nets in particular, are seen as major contributors in the quest for understanding the human mind. Computational models serve as objects of experimentation, and results from these virtual experiments are tacitly included in the framework of empirical science. Simulations of cognitive functions, like learning to speak, or discovering syntactical structures in language, are the basis for (...) many claims about human capacities in language acquisition. This raises the question whether results obtained from experiments that are essentially performed on data structures are equivalent to results from "real" experiments. This paper examines some design methodologies for models of cognitive functions using artificial neural nets. The process of conducting the cognitive simulations is largely a projection of theories, or even unsubstantiated conjectures, onto simulated neural structures and an interpretation of the experimental results in terms of the human brain. The problem with this process is that results from virtual experiments are taken to refer unambiguously to the human brain; and the more the language of human cognitive function is deployed in both theory construction and (virtual) experimental interpretation, the more convincing the impression. Additionally, the complexity of the methodologies, principles, and visualization techniques, in the implementation of the computational model, masks the lack of actual similarities between model and real world phenomena. Some computational models involving artificial neural nets have had some success, even commercially, but there are indications that the results from virtual experiments have little value in explaining cognitive functions. The problem seems to be in relating computational, or mathematical, entities to real world objects, like neurons and brains. I argue that the role of Artificial Intelligence as a contributor to the knowledge base of Cognitive Science is diminished as a consequence. (shrink)

In the methodology of scientific research programs (MSRP) there are important features on the problem of prediction, especially regarding novel facts. In his approach, Imre Lakatos proposed three different levels on prediction: aim, process, and assessment. Chapter 5 pays attention to the characterization of prediction in the methodology of research programs. Thus, it takes into account several features: (1) its pragmatic characterization, (2) the logical perspective as a proposition, (3) the epistemological component, (4) its role in the appraisal of research (...) programs, and (5) its place as a value for scientific research. -/- The notion of “novel facts” is highly relevant in his conception, where several aspects are involved: the directions of novel facts, the different kinds of novelty, and the transition from six possible options of “novel facts” to four choices. Thereafter, the prediction of novel facts as the criterion of appraisal is considered. On the one hand, this requires analyzing the theoretical, empirical, and heuristic possibilities of appraisal; and, on the other, whether there is an overemphasis on the role of prediction in methodology of scientific research programs. As a consequence, there is an analysis of Lakatos’ criterion of appraisal in MSRP and economics. (shrink)

Although true in some aspects, the suggested characterization of today’s science as a dichotomy between traditional science and data-driven science misses some of the nuance, complexity, and possibility that exists between the two positions. Part of the problem is the claim that Data Science works without theories. There are many theories behind the data that are used in science. However, for data science, the only theories that matter are those in mathematics, statistics, and computer science. In this conceptual paper, we (...) add two other philosophy of science tenets, experiments and data, to the discussion to create a more nuanced view of how data science uses theories. Following Ihde’s concept of technoscience and the incessant quest for more precision, magnification, and resolution, we argue that technology-driven science created a need for more technology-driven science, culminating in data science. Further, we adapt Hacking and Galison’s views on physics to argue that data science is also an experimental science, which uses data objects in experiments. Drawing from Heelan, we called these objects “data-objects-for-knowing”. Finally, we conclude that data science is a science to study artificially created phenomena—a science to study the data manipulated by the equations and operations of AI. It disregards the connections between data and the real world that were carefully built by the theories from other sciences. In the experiments of data science, data are the world itself. The knowledge created by data science is purposely disconnected from any theory from other sciences; it is a knowledge for the sake of itself. (shrink)