In this paper we will offer a few examples to illustrate the orientation of contemporary research in data analysis and we will investigate the corresponding role of mathematics. We argue that the modus operandi of data analysis is implicitly based on the belief that if we have collected enough and sufficiently diverse data, we will be able to answer most relevant questions concerning the phenomenon itself. This is a methodological paradigm strongly related, but not limited to, biology, (...) and we label it the microarray paradigm. In this new framework, mathematics provides powerful techniques and general ideas which generate new computational tools. But it is missing any explicit isomorphism between a mathematical structure and the phenomenon under consideration. This methodology used in data analysis suggests the possibility of forecasting and analyzing without a structured and general understanding. This is the perspective we propose to call agnostic science, and we argue that, rather than diminishing or flattening the role of mathematics in science, the lack of isomorphisms with phenomena liberates mathematics, paradoxically making more likely the practical use of some of its most sophisticated ideas. (shrink)

In this document, we explore the Philosophy of Data and its roots amongst other disciplines. The Philosophy of Data seeks to understand the nature of data through experimental philosophy. In order to understand the many different ontologies of data, information, and knowledge out there, this paper will describe part of the problem space in terms of other disciplines and make an argument for the establishment of this new philosophical field. Furthermore, we will show (...) how the PoD is very important to IS scholars and practitioners. (shrink)

This paper attempts to define Exploratory Data Analysis (EDA) more precisely than usual, and to produce the beginnings of a philosophy of this topical and somewhat novel branch of statistics. A data set is, roughly speaking, a collection of k-tuples for some k. In both descriptive statistics and in EDA, these k-tuples, or functions of them, are represented in a manner matched to human and computer abilities with a view to finding patterns that are not "kinkera". A (...) kinkus is a pattern that has a negligible probability of being even partly potentially explicable. A potentially explicable pattern is one for which there probably exists a hypothesis of adequate "explicativity", which is another technical probabilistic concept. A pattern can be judged to be probably potentially explicable even if we cannot find an explanation. The theory of probability understood here is one of partially ordered (interval-valued), subjective (personal) probabilities. Among other topics relevant to a philosophy of EDA are the "reduction" of data; Francis Bacon's philosophy of science; the automatic formulation of hypotheses; successive deepening of hypotheses; neurophysiology; and rationality of type II. (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 (...) class='Hi'>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)

This paper explores recent research done into the philosophy of data. The research utilizes experimental philosophy ideas combined with Information Technology methodologies to assess participants' philosophies of data. Reusing the concept of the Data Flow Diagram, I suggest a methodology of experimental philosophy that allows participants to categorize flows into data, information, and knowledge in order to explore their practical understanding instead of their theoretical understanding. My research has found three distinct philosophies: " (...) class='Hi'>data as bits" "data as hard numbers," and "data as recorded observations. (shrink)

This paper explores the transformation of Twitter from the traditional developer based command and control into something strangely democratic: a social construction of utility, a twisting of this once unique service to serve the needs and desires, ever evolving, of its users. We explore changes in the social constructions of Twitter and use recent research in the Philosophy of Data to suggest potential explanations.

_Philosophy of Molecular Medicine: Foundational Issues in Theory and Practice_ aims at a systematic investigation of a number of foundational issues in the field of molecular medicine. The volume is organized around four broad modules focusing, respectively, on the following key aspects: What are the nature, scope, and limits of molecular medicine? How does it provide explanations? How does it represent and model phenomena of interest? How does it infer new knowledge from data and experiments? The essays collected here, (...) authored by prominent scientists and philosophers of science, focus on a handful of mainstream topics in the philosophical literature, such as _causation_, _explanation_, _modeling_, and _scientific inference_. These previously unpublished contributions shed new light on these traditional topics by integrating them with problems, methods, and results from three prominent areas of contemporary biomedical science: _basic research_, _translational_ and _clinical research_, and _clinical practice_. (shrink)

Experimental philosophy of religion is the project of taking the tools and resources of the human sciences—especially psychology and cognitive science—and bringing them to bear on issues within philosophy of religion toward explicit philosophical ends. This paper introduces readers to experimental philosophy of religion. §1 explores the contours of experimental philosophy of religion by contrasting it with a few related fields: the psychology of religion and cognitive science of religion, on the one hand, and natural theology, (...) on the other. §2 offers a brief history of experimental philosophy of religion. The goal in this section is to highlight the ancient pedigree of this emerging area of research; as the contemporary experimental philosophy of religion literature expands and proliferates, it’s important to remember that this field has deep historical roots. Then, §3 focuses on the following questions: Why should we care about experimental philosophy of religion? And why is it needed? (shrink)

Papers in experimental philosophy rarely offer an account of what it would take to reveal a philosophically significant effect. In part, this is because experimental philosophers tend to pay insufficient attention to the hierarchy of models that would be required to justify interpretations of their data; as a result, some of their most exciting claims fail as explanations. But this does not impugn experimental philosophy. My aim is to show that experimental philosophy could be made more (...) successful by developing, articulating, and advancing plausible models of the data that are collected and the analyses that are employed. (shrink)

Research in psychopathology is booming in an unprecedented way, at least, in terms of increasing number of publications. Yet, a few questions arise: Does quantity also give us quality? Are the collected data generally of sound quality? How are data typically collected in psychopathology? Are the applied methods of data collection appropriate for this particular field of study? This article explores three different methods of data collection in psychopathology, namely self-rating scales, structured interviews, and semi-structured, phenomenological (...) interviews. To identify the most adequate methodological approach, we first establish the nature of the object of psychopathology and then we critically assess each method’s appropriateness to this field of study. We emphasize fundamental issues that make self-rating scales and structured interviews unfit for the task of adequately examining psychopathology. By contrast, we propose that a semi-structured, phenomenological interview presents a more appropriate method. Finally, we describe two types of semi-structured, phenomenological interviews that can be applied to assess and explore psychopathology, respectively. (shrink)

The theory of knowledge in early twentieth-century Anglo American philosophy was oriented toward phenomenally described cognition. There was a healthy respect for the mind-body problem, which meant that phenomena in both the mental and physical domains were taken seriously. Bertrand Russell's developing position on sense-data and momentary particulars drew upon, and ultimately became like, the neutral monism of Ernst Mach and William James. Due to a more recent behaviorist and physicalist inspired "fear of the mental", this development has (...) been down-played in historical work on early analytic philosophy. Such neglect assumes that the "linguistic turn" is a proper and permanent effect of twentieth-century philosophy, an assumption that distorts early analytic historiography, and begs a substantive philosophical question about thought and cognition. (shrink)

Linkage (or genetic) maps are graphs, which are intended to represent the linear ordering of genes on the chromosomes. They are constructed on the basis of statistical data concerning the transmission of genes. The invention of this technique in 1913 was driven by Morgan's group's adoption of a set of hypotheses concerning the physical mechanism of heredity. These hypotheses were themselves grounded in Morgan's defense of the chromosome theory of heredity, according to which chromosomes are the physical basis of (...) genes. In this paper, I analyze the 1919 debate between William Castle and Morgan's group, about the construction of genetic maps. The official issue of the debate concerns the arrangement of genes on chromosomes. However, the disputants tend to carry out the discussions about how one should model the data in order to draw predictions concerning the transmission of genes; the debate does not bear on the data themselves, nor does it focus on the hypotheses explaining these data. The main criteria that are appealed to by the protagonists are simplicity and predictive efficacy. However, I show that both parties' assessments of the simplicity and predictive efficacy of different ways of modeling the data themselves depend on background theoretical positions. I aim at clarifying how preference for a given model and theoretical commitments articulate. (shrink)

This bibliography aims to gather together studies in the philosophy of literature by Finnish researchers. It consists of articles and monographs which treat i) philosophical literary theory, ii) philosophical literature, or iii) literary philosophy and philosophers’ use of literary devices. The bibliography, collected by requests of publication data and from several Finnish publication databases, is not intended inclusive. Nevertheless, it is being throughout updated, and all kinds of suggestions, updates and corrections are most welcome.

Modern science’s ability to produce, store, and analyze big datasets is changing the way that scientific research is practiced. Philosophers have only begun to comprehend the changed nature of scientific reasoning in this age of “big data.” We analyze data-focused practices in biology and climate modeling, identifying distinct species of data-centric science: phenomena-laden in biology and phenomena-agnostic in climate modeling, each better suited for its own domain of application, though each entail trade-offs. We argue that data-centric (...) practices in science are not monolithic because the opportunities and challenges presented by big data vary across scientific domains. (shrink)

Scientists have rules pertaining to data fabrication and falsification that are enforced with significant punishments, such as loss of funding, termination of employment, or imprisonment. These rules pertain to data that describe observable and unobservable entities. In this commentary I argue that scientists would not adopt rules that impose harsh penalties on researchers for data fabrication or falsification unless they believed that an aim of scientific research is to develop true theories and hypotheses about entities that exist, (...) including unobservable ones. This argument presents a challenge for constructive empiricists, such as van Fraassen. Constructive empiricists need to be able to explain why rules pertaining to data fabrication and falsification do not threaten their philosophy of science. (shrink)

This article is a prelude to an experimental study of the preference concept in economics. I argue that a new empirical approach called experimental philosophy of science is a promising approach to advance the philosophy of economics. In particular, I discuss two debates in the field, the neuroeconomics controversy and the commonsensible realism debate, and suggest how experimental and survey techniques can generate data that will inform these debates. Some of the likely objections from philosophers and economists (...) are addressed, and possible ways of operationalizing different preference concepts are illustrated. (shrink)

This paper contributes to the underdeveloped field of experimental philosophy of science. We examine variability in the philosophical views of scientists. Using data from Toolbox Dialogue Initiative, we analyze scientists’ responses to prompts on philosophical issues (methodology, confirmation, values, reality, reductionism, and motivation for scientific research) to assess variance in the philosophical views of physical scientists, life scientists, and social and behavioral scientists. We find six prompts about which differences arose, with several more that look promising for future (...) research. We then evaluate the difference between the natural and social sciences and the challenge of interdisciplinary integration across scientific branches. (shrink)

This paper proposes an original definition of samples as a kind of data within the relational framework of data. The distinction between scientific objects (e.g., samples, data, models) often needs to be clarified in the philosophy of science to understand their role in the scientific inquiry. The relational framework places data at the forefront of knowledge construction. Their epistemic status depends on their evaluation as potential evidence in a research situation and their ability to circulate (...) among researchers. While samples are significant in data-generating science, their role has been underexplored in the philosophy of data literature. I draw on a case study from data-centric microbiology, viz. amplicon sequencing, to introduce specifications of the relational framework. These specifications capture the distinctive epistemic role of samples, allowing the discussion of their significance in the inquiry process. I argue that samples are necessarily transformed to be considered as evidence, portable in the limits of a situation, and they act as world anchors for claims about a phenomenon. I compare these specifications with other data and evidence frameworks and suggest they are compatible. The paper concludes by considering the extension of these criteria in the context of biobanking. The specifications proposed here help analyze other life sciences cases and deepen our understanding of samples and their epistemological role in scientific research. (shrink)

This paper presents an analysis of the forms of response that scientists make when confronted with anomalous data. We postulate that there are seven ways in which an individual who currently holds a theory can respond to anomalous data: (1) ignore the data; (2) reject the data; (3) exclude the data from the domain of the current theory; (4) hold the data in abeyance; (5) reinterpret the data; (6) make peripheral changes to the (...) current theory; or (7) change the theory. We analyze psychological experiments and cases from the history of science to support this proposal. Implications for the philosophy of science are discussed. (shrink)

In this paper we argue that data science is a coherent and novel approach to empirical problems that, in its most general form, does not build understanding about phenomena. Within the new type of mathematization at work in data science, mathematical methods are not selected because of any relevance for a problem at hand; mathematical methods are applied to a specific problem only by `forcing’, i.e. on the basis of their ability to reorganize the data for further (...) analysis and the intrinsic richness of their mathematical structure. In particular, we argue that deep learning neural networks are best understood within the context of forcing optimization methods. We finally explore the broader question of the appropriateness of data science methods in solving problems. We argue that this question should not be interpreted as a search for a correspondence between phenomena and specific solutions found by data science methods; rather, it is the internal structure of data science methods that is open to precise forms of understanding. (shrink)

Originally published as Scientific Research, this pair of volumes constitutes a fundamental treatise on the strategy of science. Mario Bunge, one of the major figures of the century in the development of a scientific epistemology, describes and analyzes scientific philosophy, as well as discloses its philosophical presuppositions. This work may be used as a map to identify the various stages in the road to scientific knowledge. Philosophy of Science is divided into two volumes, each with two parts. Part (...) 1 offers a preview of the scheme of science and the logical and semantical took that will be used throughout the work. The account of scientific research begins with part 2, where Bunge discusses formulating the problem to be solved, hypothesis, scientific law, and theory. The second volume opens with part 3, which deals with the application of theories to explanation, prediction, and action. This section is graced by an outstanding discussion of the philosophy of technology. Part 4 begins with measurement and experiment. It then examines risks in jumping to conclusions from data to hypotheses as well as the converse procedure. Bunge begins this mammoth work with a section entitled "How to Use This Book." He writes that it is intended for both independent reading and reference as well as for use in courses on scientific method and the philosophy of science. It suits a variety of purposes from introductory to advanced levels. Philosophy of Science is a versatile, informative, and useful text that will benefit professors, researchers, and students in a variety of disciplines, ranging from the behavioral and biological sciences to the physical sciences. (shrink)

This work first explicates the philosophy of classical and quantum fields and particles. I am interested in determining how science can have a metaphysical dimension, and then with the claim that the quantum revolution has an important metaphysical component. I argue that the metaphysical implications of a theory are properties of its models, as classical mechanics determines properties of atomic diversity and temporal continuity with its representations of distinct, continuous trajectories. ;It is often suggested that classical statistical physics requires (...) that many particle states be represented so that permuting properties leads to distinct states; this implies that individuals can be reidentified across possible worlds in a non-qualitative way. I show there is no evidence for this conclusion, an important result, for it is claimed that quantum particles are not individuals. This claim is based on the misconception about classical statistics, but also on a conflation of notions of identity; I show that, while transworld identity is incompatible with quantum mechanics, other classical notions may be consistently ascribed. I also give a field-particle distinction that applies usefully in both quantum and classical domains. In the former the distinction helps defeat claims of underdetermined by data, in the latter it helps provide a minimal field metaphysics. ;Next I tackle renormalisation: I show how divergences occur in approximate, perturbative calculations, and demonstrate how finite, empirically verified, answers are obtained. These techniques seem to show that the predictions are not logical consequences of the exact theory. I use the techniques of the renormalisation group to establish that perturbative renormalised quantum field theory does indeed approximate the consequences of field theory. ;Finally, I discuss the idea that renormalisation proves that there can be no quantum theory of everything, only a patchwork of effective theories. The preceding chapter shows that renormalisation demonstrates only that the picture is consistent, and this is insufficient to show that physics must be phenomenological. (shrink)

By means of a citation analysis I aim to determine which scholarly journals are most important in the sub-field of philosophy of science. My analysis shows that the six most important journals in the sub-field are Philosophy of Science , British Journal for the Philosophy of Science , Journal of Philosophy , Synthese , Studies in History and Philosophy of Science , and Erkenntnis . Given the data presented in this study, there is little (...) evidence that there is such a field as the history and philosophy of science (HPS). Rather, philosophy of science is most properly conceived of as a sub-field of philosophy. (shrink)

Using web standards, such as uniform resource identifiers (URIs), XML and HTTP, for naming and describing resources which are not information objects is the key difference between the Web as we know it today and the Semantic Web. Naming and interlinking this type of resources by HTTP URIs (instead of individual constants in a formal language) is the key feature which distinguishes traditional knowledge representation from web-scale knowledge representation. However, this use of URIs brought back attention to the old philosophical (...) problem of identity and reference in a new form. In this paper, we analyze the new version of the problem, provide a formal model for dealing with it when interlinking knowledge on the Web, and argue for the need of a distinction between the use of URIs for describing and accessing resources, and the use of URIs for fixing the reference . We show that in the current practice of linking data these roles are not clearly distinguished, and that this fact may cause unwanted effects and prevent some basic forms of data integration. We also discuss the role of an entity name system as a potential piece of infrastructure for fixing the reference in the Semantic Web. (shrink)

The modern abundance and prominence of data have led to the development of “data science” as a new field of enquiry, along with a body of epistemological reflections upon its foundations, methods, and consequences. This article provides a systematic analysis and critical review of significant open problems and debates in the epistemology of data science. We propose a partition of the epistemology of data science into the following five domains: (i) the constitution of data science; (...) (ii) the kind of enquiry that it identifies; (iii) the kinds of knowledge that data science generates; (iv) the nature and epistemological significance of “black box” problems; and (v) the relationship between data science and the philosophy of science more generally. (shrink)

In the Twentieth Century, Logic and Philosophy of Language are two of the few areas of philosophy in which philosophers made indisputable progress. For example, even now many of the foremost living ethicists present their theories as somewhat more explicit versions of the ideas of Kant, Mill, or Aristotle. In contrast, it would be patently absurd for a contemporary philosopher of language or logician to think of herself as working in the shadow of any figure who died before (...) the Twentieth Century began. Advances in these disciplines make even the most unaccomplished of its practitioners vastly more sophisticated than Kant. There were previous periods in which the problems of language and logic were studied extensively (e.g. the medieval period). But from the perspective of the progress made in the last 120 years, previous work is at most a source of interesting data or occasional insight. All systematic theorizing about content that meets contemporary standards of rigor has been done subsequently. (shrink)

This chapter looks at approaches developed by philosophers of science that may be useful to those working in religion, theology, and the philosophy of religion. Philosophers of science have spent a lot of time thinking about how theories change, what to do with surprising data and conflicting explanations, and what to say when we need more categories than true and false. Sometimes, all this is hidden behind terms such as antirealism, paradigm, verisimilitude, and inference to the best explanation. (...) The author unpacks a bit of jargon from the philosophy of science and considers some analogies and applications to matters of religion. The chapter also presents some of the tools developed in the philosophy of science. Finally, it discusses what is supposed to be the fundamental divide between religion and science: the former is based on faith, while the latter is based on reason. (shrink)

We address some of the epistemological challenges highlighted by the Critical Data Studies literature by reference to some of the key debates in the philosophy of science concerning computational modeling and simulation. We provide a brief overview of these debates focusing particularly on what Paul Humphreys calls epistemic opacity. We argue that debates in Critical Data Studies and philosophy of science have neglected the problem of error management and error detection. This is an especially important feature (...) of the epistemology of Big Data. In “Error” section we explain the main characteristics of error detection and correction along with the relationship between error and path complexity in software. In this section we provide an overview of conventional statistical methods for error detection and review their limitations when faced with the high degree of conditionality inherent to modern software systems. (shrink)

The Philosophy of Quantitative Methods undertakes a philosophical examination of a number of important quantitative research methods within the behavioral sciences in order to overcome the non-critical approaches typically provided by textbooks. These research methods are exploratory data analysis, statistical significance testing, Bayesian confirmation theory and statistics, meta-analysis, and exploratory factor analysis. Further readings are provided to extend the reader's overall understanding of these methods.

The essay proposes to use alienation as a heuristic and conceptual tool for the analysis of the impact of digital information and communication technologies (ICTs) on users. It follows a historical materialist understanding, according to which data can be considered as things produced in an industrial fashion. A representational interpretation, according to which data would merely reflect a given reality, is untenable. It will be argued instead to understand data as an additional layer which has a transformative (...) impact on reality as a whole. The analysis follows the early Marx in thinking of alienation as a structural condition which holds even if it is not experienced psychologically by users. Alienation means that the desire to express oneself and achieve social recognition, self-realization, and participation through the use of ICTs is incongruent to the inherent purposes and functions of these technologies. Digital platforms create the ideological illusion that users contribute to the production of data solely for their own gratification. Users, in turn, react to structural alienation either by internalizing it, in which case they may become enthusiastic data producers, or by imposing alienating conditions on other users. Finally, it will be shown that a strong notion of privacy is less suited to articulate concerns about the impact of ICTs because it expresses a desire for privilege and individual control which has become unfulfillable in the environment of data industries. The essay closes by showing some of the changes in institutional structures and behavior that could follow from the focus on alienation. (shrink)

In this article, we show how postphenomenology can be used to analyze the Affinity Map: a data visualization that reveals the hidden dynamics that exist between individuals within large organizations. We make use of the Affinity Map to expand the classic postphenomenology that privileges a ‘linear’ understanding of technological mediations and introduce the notions of ‘iterativity’ and ‘collectivity.’ In the first section of the paper, we discuss both classic and more recent descriptions of human-technology-world relations in order to transcendentally (...) approach the discipline of data visualization. In the second section, we use the Affinity Map case study to consider three elements: 1) the collection of data and the design process; 2) the visual grammar of the data visualization, and 3) the process of self-recognition for the map ‘reader.’ In the third section, we introduce the hermeneutic circle of data visualization. Finally, we suggest that the Affinity Map, because of its ethical and political multistability, might be seen as a material encounter between postphenomenology, actor-network theory (ANT), and hermeneutics. (shrink)

The first part of the paper is a metatheoretical consideration of such philosophy of nature which allows for using scientific results in philosophical analyses. An epistemological 'judgment' of those results becomes a preliminary task of this discipline: this involves taking a position in the controversy between realistic and antirealistic accounts of science. It is shown that a philosopher of nature has to be a realist, if his task to build true ontology of reality is to be achieved. At the (...) same time he cannot be a realist ˗ a possibility that science itself is capable of deciding what beings really exist (a typical realistic claim is that scientific notions refer to something external and truly describe its characteristics) has to be denied, if the philosophy of nature is seen as a discipline investigating the natural world, while being epistemologically different from the natural sciences. A possibility of weakening this opposition is explored in the second part of the paper, where the idea of so-called "postulated ontology" of scientific theories is brought to the consideration. This idea appears in the context of a well-known thesis of the underdetermination of scientific theories by empirical data. It is argued in the paper, that the conviction of the existence of some kind of relation between a given theory and ontological ideas can be derived from this thesis, regardless of its particular form. Therefore, certain solutions to classical philosophical questions can be obtained, in principle, by careful inspection of scientific achievements. However, if the thesis of underdetermination holds, such philosophical solutions are not imposed by science itself. In order to arrive at some kind of ontology based on science, it seems necessary to accept certain philosophical presuppositions in the first place. This and the fact that scientific theories change in time show that although such a kind of ontology is possible, and perhaps desirable, it can never be ultimate. (shrink)

La raccolta di dati nella nostra era dell’”Information Technology” ha generato una rivoluzione nella conoscenza. Nell’era dei “big data”, la conseguente crescita senza precedenti dei dati, ha reso necessari cambiamenti nella scala, nella natura e nello stato dei dati, portando quindi i ricercatori ad adottare nuovi paradigmi e metodologie nella ricerca filosofica. In particolare, l’attenzione teorica della filosofia del linguaggio si è spostata verso la conoscenza cognitiva, con un’enfasi sulla proposizione particolare del “data turn” nella cognizione cognitiva nell’era (...) dei “big data”. Il paper esplora la potenziale portata della ricerca quantitativa del “data turn” nella filosofia del linguaggio, tramite l’analisi della necessità di trasformare i paradigmi della ricerca qualitativa e quantitativa, ricostruendo l’approccio quantitativo della filosofia del linguaggio ed ampliando l’analisi delle relazioni uomo-dati nella filosofia dei “big data”. Il paper conclude affermando che sono necessarie ulteriori ricerche per esaminare in modo ancor più approfondito la relazione tra linguaggio, dati e filosofia. (shrink)

Philosophers of mind, both in the conceptual analysis tradition and in the empirical informed school, have been implicitly neglecting the potential conceptual role of the Peripheral Nervous System (PNS) in understanding sensory and perceptual states. Instead, the philosophical as well as the neuroscientific literature has been assuming that it is the Central Nervous System (CNS) alone, and more exactly the brain, that should prima facie be taken as conceptually and empirically crucial for a philosophical analysis of such states This is (...) the first monograph that focuses on the PNS and its constitutive role in sensory states, including pain, mechanoception, proprioception, tactile perception, and so forth. -/- The author argues that the brain-centeredness of current philosophy of mind is a prejudice, and proposes a series of original ways in which classic puzzles in the philosophy of mind can be solved once the hypothesis that PNS is a constitutive element of mental states is taken seriously. The author calls this “the Peripheral Mind Hypothesis”, and employs it in a vast range of issues, such as functionalism, physicalism, mental content, embodiment, as well as some issues in neuroethics. -/- Making equal use of conceptual analysis, empirical data from neuroscience, first-person phenomenological data, and philosophical speculation, this work offers a fresh look at, and novel solutions to many philosophical problems. (shrink)

The Computational History of Philosophy of Science Dataset aims to be a comprehensive set of article and book chapter metadata for philosophy of science. The dataset covers the full run of over 40 journals and 3 major book series in the field. An automated author disambiguation script is used to construct canonical names for each author, and a combination of gender attribution methods is used to attribute the gender of each author. The full code used to generate the (...) dataset is available at [URL not allowed]. See the file data_dictionary.txt for data dictionary and additional information. (shrink)

This book explores the results of applying empirical methods to the philosophy of logic and mathematics. Much of the work that has earned experimental philosophy a prominent place in twenty-first century philosophy is concerned with ethics or epistemology. But, as this book shows, empirical methods are just as much at home in logic and the philosophy of mathematics. -/- Chapters demonstrate and discuss the applicability of a wide range of empirical methods including experiments, surveys, interviews, and (...) data-mining. Distinct themes emerge that reflect recent developments in the field, such as issues concerning the logic of conditionals and the role played by visual elements in some mathematical proofs. -/- Featuring leading figures from experimental philosophy and the fields of philosophy of logic and mathematics, this collection reveals that empirical work in these disciplines has been quietly thriving for some time and stresses the importance of collaboration between philosophers and researchers in mathematics education and mathematical cognition. (shrink)

This research is an exploration into the realms of epistemology and philosophy of data science and data analysis. It introduces the conceptual aspects of data analysis in data science research. We describe the philosophical implications of data analysis and the role that epistemology and the expertise of analysts play in analysing data, big or small. The process of data analysis is a complex job as it involves advanced statistical tools that play a (...) crucial role in deciphering what the data tells. It also perceives the value of data analysis as a knowledge resource and wealth for organizations that are in the business of data analytics. (shrink)

This book contains programmatic essays that focus on broad-ranging proposals for re-envisioning a discipline of comparative philosophy of religions. It also contains a number of case studies focussing on the interpretation of particular religio-historical data from comparatively oriented philosophical perspectives.

Approximately one in six top economic research papers draws an explicitly causal conclusion. But what do economists mean when they conclude that A 'causes' B? Does 'cause' say that we can influence B by intervening on A, or is it only a label for the correlation of variables? Do quantitative analyses of observational data followed by such causal inferences constitute sufficient grounds for guiding economic policymaking? The Philosophy of Causality in Economics addresses these questions by analyzing the meaning (...) of causal claims made by economists and the philosophical presuppositions underlying the research methods used. The book considers five key causal approaches: the regularity approach, probabilistic theories, counterfactual theories, mechanisms, and interventions and manipulability. Each chapter opens with a summary of literature on the relevant approach and discusses its reception amongst economists. The text details case studies, and goes on to examine papers which have adopted the approach in order to highlight the methods of causal inference used in contemporary economics. It analyses the meaning of the causal claim put forward, and finally reconstructs the philosophical presuppositions accepted implicitly by economists. The strengths and limitations of each method of causal inference are also considered in the context of using the results as evidence for policymaking. This book is essential reading to those interested in literature on the philosophy of economics, as well as the philosophy of causality and economic methodology in general. (shrink)

Experimental philosophy of science gathers empirical data on how key scientific concepts are understood by particular scientific communities. In this paper we briefly describe two recent studies in experimental philosophy of biology, one investigating the concept of the gene, the other the concept of innateness. The use of experimental methods reveals facts about these concepts that would not be accessible using the traditional method of intuitions about possible cases. It also contributes to the study of conceptual change (...) in science, which we understand as the result of a form of conceptual ecology, in which concepts become adapted to specific epistemic niches. (shrink)

Scientists have used models for hundreds of years as a means of describing phenomena and as a basis for further analogy. In Scientific Models in Philosophy of Science, Daniela Bailer-Jones assembles an original and comprehensive philosophical analysis of how models have been used and interpreted in both historical and contemporary contexts. Bailer-Jones delineates the many forms models can take (ranging from equations to animals; from physical objects to theoretical constructs), and how they are put to use. She examines early (...) mechanical models employed by nineteenth-century physicists such as Kelvin and Maxwell, describes their roots in the mathematical principles of Newton and others, and compares them to contemporary mechanistic approaches. Bailer-Jones then views the use of analogy in the late nineteenth century as a means of understanding models and to link different branches of science. She reveals how analogies can also be models themselves, or can help to create them. The first half of the twentieth century saw little mention of models in the literature of logical empiricism. Focusing primarily on theory, logical empiricists believed that models were of temporary importance, flawed, and awaiting correction. The later contesting of logical empiricism, particularly the hypothetico-deductive account of theories, by philosophers such as Mary Hesse, sparked a renewed interest in the importance of models during the 1950s that continues to this day. Bailer-Jones analyzes subsequent propositions of: models as metaphors; Kuhn's concept of a paradigm; the Semantic View of theories; and the case study approaches of Cartwright and Morrison, among others. She then engages current debates on topics such as phenomena versus data, the distinctions between models and theories, the concepts of representation and realism, and the discerning of falsities in models. (shrink)

Scholars have emphasized that decisions about technology can be influenced by philosophy of technology assumptions, and have argued for research that critically questions technological determinist assumptions. Empirical studies of technology management in fields other than K-12 education provided evidence that philosophy of technology assumptions, including technological determinism, can influence the practice of technology leadership. A qualitative study was conducted to a) examine what philosophy of technology assumptions are present in the thinking of K-12 technology leaders, b) investigate (...) how the assumptions may influence technology decision making, and c) explore whether technological determinist assumptions are present. The research design aligned with Corbin and Strauss qualitative data analysis, and employed constant comparative analysis, theoretical sampling, and theoretical saturation of categories. Subjects involved 31 technology directors and instructional technology specialists from Virginia school districts, and data collection involved interviews following a semi-structured protocol, and a written questionnaire with open-ended questions. The study found that three broad philosophy of technology views were widely held by participants, including an instrumental view of technology, technological optimism, and a technological determinist perspective that sees technological change as inevitable. The core category and central phenomenon that emerged was that technology leaders approach technology leadership through a practice of Keep up with technology (or be left behind). The core category had two main properties that are in conflict with each other, pressure to keep up with technology, and the resistance to technological change they encounter in schools. The study found that technology leaders are guided by two main approaches to technology decision making, represented by the categories Educational goals and curriculum should drive technology, and Keep up with Technology (or be left behind). As leaders deal with their perceived experience of the inevitability of technological change, and their concern for preparing students for a technological future, the core category Keep up with technology (or be left behind) is given the greater weight in technology decision making. The researcher recommends that similar qualitative studies be conducted involving technology leaders outside Virginia, and with other types of educators. It is also recommended that data from this or other qualitative studies be used to help develop and validate a quantitative instrument to measure philosophy of technology assumptions, for use in quantitative studies. (shrink)

The goal of the book is to present the latest research on the new challenges of data technologies. It will offer an overview of the social, ethical and legal problems posed by group profiling, big data and predictive analysis and of the different approaches and methods that can be used to address them. In doing so, it will help the reader to gain a better grasp of the ethical and legal conundrums posed by group profiling. The volume first (...) maps the current and emerging uses of new data technologies and clarifies the promises and dangers of group profiling in real life situations. It then balances this with an analysis of how far the current legal paradigm grants group rights to privacy and data protection, and discusses possible routes to addressing these problems. Finally, an afterword gathers the conclusions reached by the different authors and discuss future perspectives on regulating new data technologies. (shrink)

Philosophy of science is the object of metaphilosophical investigations. Metaphilosophy is the philosophy of philosophy. Philosophy is an archetypical thinking of being or an experience-of-being. History of Greek-European tradition of philosophy has three archetypes of thinking: objectivity, subjectivity, and inter-subjectivity. They are three archetypical contexts of interpretations of the concept of a philosophy of science too. Is philosophy of science part of philosophy? Is philosophy ofscience part of epistemology? What are methods (...) of philosophy of science? These questions are the topics of metaphilosophy. The topic of a scientific fact is a focal point of contemporary epistemology and philosophy of science. Is a scientific fact a fallible knowledge? The nature of a scientific fact is discussed in keeping with to the opposition of fallibilism and infallibilism. If fallibilism is universal quality of knowledge then there is a problem: is a scientific fact a fallible knowledge too? We are understanding and make clear the nature of a scientific fact by correlation of facts with: (1) data and evidence; (2) languages and theories; (3) methods of empirical investigations; (4) values, norms, and conventions of scientific investigations. Philosophy of science communicates with philosophy ofeconomics as the contemporary branch of philosophy. Its problems arise from the relationship of philosophy and philosophy of science with economics and practice. (shrink)

The paper is devoted to the subject status of the discipline “Philosophy of Sociology”. Philosophy of sociology is an interdisciplinary direction focusing on the main preconditions of sociological knowledge development as well as cognitive value of sociological facts, theories and conceptions. Its subject can be divided into “philosophical foundations of sociology” and “philosophical problems of sociology”. “Sociologism” as the concept of substitution of social philosophy by sociology as a science which is better empirically oriented and is able (...) to give more exact and proved picture of social reality also poses danger to social philosophy. “Sociologism” presents a positivist attack on social philosophy. Indeed, sociology is no less speculative science than social philosophy is, which is well illustrated by a lot of divergences among its theoretical constructions and empirical data. The main functions of philosophy of sociology in the world of contemporary social-humanitarian knowledge are critical function, integrative function and axiological function. (shrink)