In this chapter I investigate the kinds of changes that psychiatric kinds undergo when they become explanatory targets of areas of sciences that are not “mature” and are in the early stages of discovering mechanisms. The two areas of science that are the targets of my analysis are cognitive neuroscience and cognitive neurobiology.

In computer simulations of physical systems, the construction of models is guided, but not determined, by theory. At the same time simulations models are often constructed precisely because data are sparse. They are meant to replace experiments and observations as sources of data about the world; hence they cannot be evaluated simply by being compared to the world. So what can be the source of credibility for simulation models? I argue that the credibility of a simulation model comes not only (...) from the credentials supplied to it by the governing theory, but also from the antecedently established credentials of the model building techniques employed by the simulationists. In other words, there are certain sorts of model building techniques which are taken, in and of themselves, to be reliable. Some of these model building techniques, moreover, incorporate what are sometimes called “falsifications.” These are contrary-to-fact principles that are included in a simulation model and whose inclusion is taken to increase the reliability of the results. The example of a falsification that I consider, called artificial viscosity, is in widespread use in computational fluid dynamics. Artificial viscosity, I argue, is a principle that is successfully and reliably used across a wide domain of fluid dynamical applications, but it does not offer even an approximately “realistic” or true account of fluids. Artificial viscosity, therefore, is a counter-example to the principle that success implies truth – a principle at the foundation of scientific realism. It is an example of reliability without truth. (shrink)

There are a variety of topics in the philosophy of science that need to be rethought, in varying degrees, after one pays careful attention to the ways in which computer simulations are used in the sciences. There are a number of conceptual issues internal to the practice of computer simulation that can benefit from the attention of philosophers. This essay surveys some of the recent literature on simulation from the perspective of the philosophy of science and argues that philosophers have (...) a lot to learn by paying closer attention to the practice of simulation. (shrink)

This paper discusses some philosophical aspects related to the recent publication of the experimental results of the 2017 black hole experiment, namely the first image of the supermassive black hole at the center of galaxy M87. In this paper I present a philosophical analysis of the 2017 Event Horizon Telescope (EHT) black hole experiment. I first present Hacking’s philosophy of experimentation. Hacking gives his taxonomy of elements of laboratory science and distinguishes a list of elements. I show that the EHT (...) experiment conforms to major elements from Hacking’s list. I then describe with the help of Galison’s Philosophy of the Shadow how the EHT Collaboration created the famous black hole image. Galison outlines three stages for the reconstruction of the black hole image: Socio-Epistemology, Mechanical Objectivity, after which there is an additional SocioEpistemology stage. I subsequently present my own interpretation of the reconstruction of the black hole image and I discuss model fitting to data. I suggest that the main method used by the EHT Collaboration to assure trust in the results of the EHT experiment is what philosophers call the Argument from Coincidence. I show that using this method for the above purpose is problematic. I present two versions of the Argument from Coincidence: Hacking’s Coincidence and Cartwright’s Reproducibility by which I analyse the EHT experiment. The same estimation of the mass of the black hole is reproduced in four different procedures. The EHT Collaboration concludes: the value we have converged upon is robust. I analyse the mass measurements of the black hole with the help of Cartwright’s notion of robustness. I show that the EHT Collaboration construe Coincidence/Reproducibility as Technological Agnosticism and I contrast this interpretation with van Fraassen’s scientific agnosticism. (shrink)

This article seeks rapprochement between the sociology of knowledge and philosophy of science by attempting to capture the best social constructionist insights within a strongly realistic philosophy of science. Key to doing so are separating the grounds for the individual scientist coming to know that P from those grounds for socially credentialing the claim that P within the relevant scientific subcommunity and showing how truth considerations can enter into the analysis of knowledge without interfering with social constructionist treatments of credentialing (...) claims. A detailed epistemology and associated account of the social credentialing of knowledge claims are presented. Close attention is paid to the epistemological and social aspects of the nature and role of observation reports, the interpretation of data, simplifications, idealizations, and approximations, and the nature of replication. The article contains a National Aeronautics and Space Administration remote-sensing case study and a detailed analysis of replication in the 1881–1933 Michelson-Morley-Miller interferometer ether studies. (shrink)

Starting with some illustrative examples, I develop a systematic account of a specific type of experimentation--an experimentation which is not, as in the "standard view", driven by specific theories. It is typically practiced in periods in which no theory or--even more fundamentally--no conceptual framework is readily available. I call it exploratory experimentation and I explicate its systematic guidelines. From the historical examples I argue furthermore that exploratory experimentation may have an immense, but hitherto widely neglected, epistemic significance.

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)

The question as to whether Ian Hacking’s project of scientific styles of thinking entails epistemic relativism has received considerable attention. However, scholars have never discussed it vis-à-vis Wittgenstein. This is unfortunate: not only is Wittgenstein the philosopher who, together with Foucault, has influenced Hacking the most, but he has also faced the same accusation of ‘relativism’. I shall explore the conceptual similarities and differences between Hacking’s notion of style of thinking and Wittgenstein’s conception of form of life. It is a (...) fact that whether or not the latter entails epistemic relativism is still a controversial question. From my comparative analysis, it will emerge that there are stronger reasons to conclude that Hacking’s notion of style leads to epistemic relativism than there are to reach the same conclusion in the case of Wittgenstein’s conception of form of life. This point will be at odds with the anti-relativistic stance that Hacking has taken in his more recent writings. (shrink)

Economists study "The Economy," or so one might suppose. Yet this overarching entity is strikingly absent from mainstream theory. Since the 1950s, it has generally been described with a few mathematical propositions and not given a description that attends to institutions, power relations, or the emergent properties that form the leading indicators in macroeconomic theory. There is thus a significant divergence between folk economics and scientific economics on this theoretical entity. This article briefly addresses the history of this concept, noting (...) its heyday in the interwar years, and brings to bear some of the analytical apparatus on institutional facts devised by John Searle. Whatever is meant by "The Economy" in folk economics appears to be significantly divergent from what is posited in scientific economics. Key Words: economy • Searle • theoretical entities • macroeconomics • economic indicators • folk economics. (shrink)

Contrary to the assumptions of empiricist philosophies of science, the theory-laden character of data will not imply the inherent failure (subjectivity, circularity, or rationalization) of instruments to expose nature's secrets. The success of instruments is credited to scientists' capacity to create artificial technological analogs to familiar physical systems. The design of absorption spectrometers illustrates the point: Progress in designing many modern instruments is generated by analogically projecting theoretical insights from known physical systems to unknown terrain. An experimental realism is defended.

Computer simulation and philosophy of science Content Type Journal Article Pages 1-4 DOI 10.1007/s11016-011-9567-8 Authors Wendy S. Parker, Department of Philosophy, Ellis Hall 202, Ohio University, Athens, OH 45701, USA Journal Metascience Online ISSN 1467-9981 Print ISSN 0815-0796.

A strong case has been made for the role and value of mechanistic reasoning in process-oriented sciences, such as molecular biology and neuroscience. This paper shifts focus to assess the role of mechanistic reasoning in an area where it is neither obvious nor expected: population genetics. Population geneticists abstract away from the causal-mechanical details of individual organisms and, instead, use mathematics to describe population-level, statistical phenomena. This paper, first, develops a framework for the identification of mechanistic reasoning where it is (...) not obvious: mathematical and mechanistic styles of scientific reasoning. Second, it applies this framework to demonstrate that both styles are integrated in modern investigations of evolutionary biology. Characteristic of the former, applied population genetic techniques provide statistical evidence for associations between genotype, phenotype, and fitness. Characteristic of the latter, experimental interventions provide causal-mechanical evidence for associations between the very same relationships, often in the same model organisms. The upshot is a richer perspective of how evolutionary biologists build evidence for hypotheses regarding adaptive evolution and a general framework for assessing the scope of mechanistic reasoning across the sciences. (shrink)

Ian Hacking defines a “style of scientific thinking” loosely as a “way to find things out about the world” characterised by five hallmark features of a number of scientific template styles. Most prominently, these are autonomy and “self-authentication”: a scientific style of thinking, according to Hacking, is not good because it helps us find out the truth in some domain, it itself defines the criteria for truth-telling in its domain. I argue that Renaissance medicine, Mediaeval “demonology”, and magical thinking pass (...) muster as scientific according to Hacking's criteria. However, application of these thought styles to the entities they introduce generates statements that logically imply a set of “ordinary” statements—or what Bernard Williams calls “plain truths”—which, contra the claims of autonomy and self-authentication, allows styles to be assessed from a style- independent perspective. Using Williams’ notion of plain truth, I show that Renaissance medicine, demonology, and magical thinking, in reality issue in many plain falsehoods. This confronts us with what I call Hacking's dilemma: either define stricter necessary conditions on being a style of scientific thinking, or concede that some styles albeit scientific are not as good at finding things out about the world as others. I make three suggestions to deal with the dilemma. (shrink)

This paper discusses a series of case studies on observations, experiments, and the theoretical interpretation between 1890 and 1960 of a shift of dark Fraunhofer lines in the solar spectrum. I argue for the use of flow charts to analyze interconnections and to identify sequences of research strategies. Also I advocate using a newly-developed tool called "block diagram" representation of experimental systems as an appropriate method to identify recurrent patterns in the interplay of instrumentation, experiment, and theory in research episodes.

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)

This paper examines the relationship between simulation and experiment. Many discussions of simulation, and indeed the term "numerical experiments," invoke a strong metaphor of experimentation. On the other hand, many simulations begin as attempts to apply scientific theories. This has lead many to characterize simulation as lying between theory and experiment. The aim of the paper is to try to reconcile these two points of viewto understand what methodological and epistemological features simulation has in common with experimentation, while at the (...) same time keeping a keen eye on simulation's ancestry as a form of scientific theorizing. In so doing, it seeks to apply some of the insights of recent work on the philosophy of experiment to an aspect of theorizing that is of growing philosophical interest: the construction of local models. (shrink)

The biological effects of low doses of toxic and carcinogenic chemicals are currently a matter of significant scientific controversy. This paper argues that philosophers of science can contribute to alleviating this controversy by examining it with the aid of a novel account of scientific anomaly. Specifically, analysis of contemporary research on chemical hormesis (i.e., alleged beneficial biological effects produced by low doses of substances that are harmful at higher doses) suggests that scientists may initially describe anomalous phenomena in terms of (...) multiple distinct "characterizations," each of which is compatible with current empirical evidence. By focusing attention on this feature of scientific anomalies, philosophers of science can alleviate the controversy over low-dose chemical effects in at least two ways: (1) they can pinpoint the significant ways in which particular characterizations frame the controversy, and (2) they can identify the methodological value judgments at stake in researchers' choice of characterizations. (shrink)

I argue that models enable us to understand reality in ways that we would be unable to do if we restricted ourselves to the unvarnished truth. The point is not just that the features that a model skirts can permissibly be neglected. They ought to be neglected. Too much information occludes patterns that figure in an understanding of the phenomena. The regularities a model reveals are real and informative. But many of them show up only under idealizing assumptions.

Several philosophical issues in connection with computer simulations rely on the assumption that results of simulations are trustworthy. Examples of these include the debate on the experimental role of computer simulations :483–496, 2009; Morrison in Philos Stud 143:33–57, 2009), the nature of computer data Computer simulations and the changing face of scientific experimentation, Cambridge Scholars Publishing, Barcelona, 2013; Humphreys, in: Durán, Arnold Computer simulations and the changing face of scientific experimentation, Cambridge Scholars Publishing, Barcelona, 2013), and the explanatory power of (...) computer simulations :277–292, 2008; Durán in Int Stud Philos Sci 31:27–45, 2017). The aim of this article is to show that these authors are right in assuming that results of computer simulations are to be trusted when computer simulations are reliable processes. After a short reconstruction of the problem of epistemic opacity, the article elaborates extensively on computational reliabilism, a specified form of process reliabilism with computer simulations located at the center. The article ends with a discussion of four sources for computational reliabilism, namely, verification and validation, robustness analysis for computer simulations, a history of successful implementations, and the role of expert knowledge in simulations. (shrink)

Modern science has undoubtedly become one the principal engines of economic growth, even though the epistemological status of scientific knowledge has been continuously contested. Leaving the philosophical problem of knowledge aside, this paper examines how scientific discovery contributes to the production of wealth. The analysis focuses on a recent achievement at the crossroads of chemistry, immunology and biotechnology: antibody catalysis. For this purpose, we develop a model of entrepreneurial work to explain how the discovery of natural products and processes generates (...) new economic opportunities. The proposed model is based on the assumption that scientists believe that the natural environment is a repository of 'natural capital'. Natural capital includes goods that are not made by humans but can be used to produce other goods and services. The belief in natural capital induces scientists to search for and identify a natural property that, in the specific cultural context of their work, is recognized as a valuable resource. The selection of such a property forms the initial phase of the discovery process. Certain research methods are then deployed to create novel empirical conditions within which the selected property is transformed into a specific good. The discovery of natural capital thus comprises a historically accountable entrepreneurial endeavour. (shrink)

In this volume, leading philosophers of psychiatry examine psychiatric classification systems, including the Diagnostic and Statistical Manual of Mental Disorders, asking whether current systems are sufficient for effective diagnosis, treatment, and research. Doing so, they take up the question of whether mental disorders are natural kinds, grounded in something in the outside world. Psychiatric categories based on natural kinds should group phenomena in such a way that they are subject to the same type of causal explanations and respond similarly to (...) the same type of causal interventions. When these categories do not evince such groupings, there is reason to revise existing classifications. The contributors all question current psychiatric classifications systems and the assumptions on which they are based. They differ, however, as to why and to what extent the categories are inadequate and how to address the problem. Topics discussed include taxometric methods for identifying natural kinds, the error and bias inherent in DSM categories, and the complexities involved in classifying such specific mental disorders as "oppositional defiance disorder" and pathological gambling. -/- Contributors George Graham, Nick Haslam, Allan Horwitz, Harold Kincaid, Dominic Murphy, Jeffrey Poland, Nancy Nyquist Potter, Don Ross, Dan Stein, Jacqueline Sullivan, Serife Tekin, Peter Zachar. (shrink)