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  1. Cognitive Metascience: A New Approach to the Study of Theories.Miłkowski Marcin - 2023 - Przeglad Psychologiczny 66 (1):185-207.
    In light of the recent credibility crisis in psychology, this paper argues for a greater emphasis on theorizing in scientific research. Although reliable experimental evidence, preregistration, methodological rigor, and new computational frameworks for modeling are important, scientific progress also relies on properly functioning theories. However, the current understanding of the role of theorizing in psychology is lacking, which may lead to future crises. Theories should not be viewed as mere speculations or simple inductive generalizations. To address this issue, the author (...)
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  • Interdisciplinarity in the Making: Models and Methods in Frontier Science.Nancy J. Nersessian - 2022 - Cambridge, MA: MIT.
    A cognitive ethnography of how bioengineering scientists create innovative modeling methods. In this first full-scale, long-term cognitive ethnography by a philosopher of science, Nancy J. Nersessian offers an account of how scientists at the interdisciplinary frontiers of bioengineering create novel problem-solving methods. Bioengineering scientists model complex dynamical biological systems using concepts, methods, materials, and other resources drawn primarily from engineering. They aim to understand these systems sufficiently to control or intervene in them. What Nersessian examines here is how cutting-edge bioengineering (...)
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  • Taking model pursuit seriously.HyeJeong Han - 2023 - European Journal for Philosophy of Science 13 (2):1-24.
    This paper aims to develop an account of the pursuitworthiness of models based on a view of models as epistemic tools. This paper is motivated by the historical question of why, in the 1960s, when many scientists hardly found QSAR models attractive, some pharmaceutical scientists pursued Quantitative Structure–Activity Relationship (QSAR) models despite the lack of potential for theoretical development or empirical success. This paper addresses this question by focusing on how models perform their heuristic functions as epistemic tools rather than (...)
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  • The puzzle of model-based explanation.N. Emrah Aydinonat - 2024 - In Tarja Knuuttila, Natalia Carrillo & Rami Koskinen (eds.), The Routledge Handbook of Philosophy of Scientific Modeling. New York, NY: Routledge.
    Among the many functions of models, explanation is central to the functioning and aims of science. However, the discussions surrounding modeling and explanation in philosophy have largely remained separate from each other. This chapter seeks to bridge the gap by focusing on the puzzle of model-based explanation, asking how different philosophical accounts answer the following question: if idealizations and fictions introduce falsehoods into models, how can idealized and fictional models provide true explanations? The chapter provides a selective and critical overview (...)
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  • (1 other version)Materials Selection in Economic Modeling.Marcel Boumans - manuscript
    Templates travel because they offer a tractable format that can be used for model-building in a variety of domains. It is often because of this quality that a particular template is chosen. But one cannot assume that there are always templates ready to model a new phenomenon, and moreover, templates have also been designed at some point. A critical aspect of this designing process is the choice of the mathematical objects with which one hopes to capture this phenomenon. This means (...)
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  • Understanding metaphorical understanding (literally).Michael T. Stuart & Daniel Wilkenfeld - 2022 - European Journal for Philosophy of Science 12 (3):1-20.
    Metaphors are found all throughout science: in published papers, working hypotheses, policy documents, lecture slides, grant proposals, and press releases. They serve different functions, but perhaps most striking is the way they enable understanding, of a theory, phenomenon, or idea. In this paper, we leverage recent advances on the nature of metaphor and the nature of understanding to explore how they accomplish this feat. We attempt to shift the focus away from the epistemic value of the content of metaphors, to (...)
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  • Mapping representational mechanisms with deep neural networks.Phillip Hintikka Kieval - 2022 - Synthese 200 (3):1-25.
    The predominance of machine learning based techniques in cognitive neuroscience raises a host of philosophical and methodological concerns. Given the messiness of neural activity, modellers must make choices about how to structure their raw data to make inferences about encoded representations. This leads to a set of standard methodological assumptions about when abstraction is appropriate in neuroscientific practice. Yet, when made uncritically these choices threaten to bias conclusions about phenomena drawn from data. Contact between the practices of multivariate pattern analysis (...)
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  • Revisiting abstraction and idealization: how not to criticize mechanistic explanation in molecular biology.Martin Zach - 2022 - European Journal for Philosophy of Science 12 (1):1-20.
    Abstraction and idealization are the two notions that are most often discussed in the context of assumptions employed in the process of model building. These notions are also routinely used in philosophical debates such as that on the mechanistic account of explanation. Indeed, an objection to the mechanistic account has recently been formulated precisely on these grounds: mechanists cannot account for the common practice of idealizing difference-making factors in models in molecular biology. In this paper I revisit the debate and (...)
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  • Reviving Frequentism.Mario Hubert - 2021 - Synthese 199:5255–5584.
    Philosophers now seem to agree that frequentism is an untenable strategy to explain the meaning of probabilities. Nevertheless, I want to revive frequentism, and I will do so by grounding probabilities on typicality in the same way as the thermodynamic arrow of time can be grounded on typicality within statistical mechanics. This account, which I will call typicality frequentism, will evade the major criticisms raised against previous forms of frequentism. In this theory, probabilities arise within a physical theory from statistical (...)
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  • ‘A mechanistic interpretation, if possible’: How does predictive modelling causality affect the regulation of chemicals?François Thoreau - 2016 - Big Data and Society 3 (2).
    The regulation of chemicals is undergoing drastic changes with the use of computational models to predict environmental toxicity. This particular issue has not attracted much attention, despite its major impacts on the regulation of chemicals. This raises the problem of causality at the crossroads between data and regulatory sciences, particularly in the case models known as quantitative structure–activity relationship models. This paper shows that models establish correlations and not scientific facts, and it engages anew the way regulators deal with uncertainties. (...)
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  • Connectomes as constitutively epistemic objects: critical perspectives on modeling in current neuroanatomy.Philipp Haueis & Jan Slaby - 2017 - In Philipp Haueis & Jan Slaby (eds.), Progress in Brain Research Vol 233: The Making and Use of Animal Models in Neuroscience and Psychiatry. Amsterdam: pp. 149–177.
    in a nervous system of a given species. This chapter provides a critical perspective on the role of connectomes in neuroscientific practice and asks how the connectomic approach fits into a larger context in which network thinking permeates technology, infrastructure, social life, and the economy. In the first part of this chapter, we argue that, seen from the perspective of ongoing research, the notion of connectomes as “complete descriptions” is misguided. Our argument combines Rachel Ankeny’s analysis of neuroanatomical wiring diagrams (...)
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  • On Method: The Fact of Science and the Distinction between Natural Science and the Humanities.Brigitte Falkenburg - 2020 - Kant Yearbook 12 (1):1-31.
    This article examines Cohen’s “transcendental method”, Windelband’s “critical method”, the neo-Kantian distinctions between natural science and the humanities (i. e., human or cultural sciences), and Weber’s account of ideal-typical explanations. The Marburg and the Southwest Schools of neo-Kantianism have in common that their respective philosophies of science focused on method, but they substantially differ in their approaches. Cohen advanced the “transcendental method”, which was taken up and transformed by Natorp and Cassirer; later, it became influential in neo-Kantian approaches to 20th (...)
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  • Giere’s instrumental Perspectivism.Kane Baker - 2020 - European Journal for Philosophy of Science 10 (3):1-19.
    When Ron Giere introduced perspectivism into philosophy of science, he provided a perspectivist analysis of both scientific instruments and scientific theorizing. Today, there is a burgeoning literature that extends Giere’s analysis of theorizing, with many philosophers examining the perspectivist approach to aspects of theorizing such as models, laws, explanations, and so on. However, relatively little attention has been paid to Giere’s analysis of instruments. In this article, I hope to fill this gap. I argue that the perspectivist analysis of instruments (...)
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  • What is a Simulation Model?Juan M. Durán - 2020 - Minds and Machines 30 (3):301-323.
    Many philosophical accounts of scientific models fail to distinguish between a simulation model and other forms of models. This failure is unfortunate because there are important differences pertaining to their methodology and epistemology that favor their philosophical understanding. The core claim presented here is that simulation models are rich and complex units of analysis in their own right, that they depart from known forms of scientific models in significant ways, and that a proper understanding of the type of model simulations (...)
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  • Semantic realism in the semantic conception of theories.Quentin Ruyant - 2020 - Synthese 198 (8):7965-7983.
    Semantic realism can be characterised as the idea that scientific theories are truth-bearers, and that they are true or false in virtue of the world. This notion is often assumed, but rarely discussed in the literature. I examine how it fares in the context of the semantic view of theories and in connection with the literature on scientific representation. Making sense of semantic realism requires specifying the conditions of application of theoretical models, even for models that are not actually used, (...)
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  • Scientific understanding and felicitous legitimate falsehoods.Insa Lawler - 2021 - Synthese 198 (7):6859-6887.
    Science is replete with falsehoods that epistemically facilitate understanding by virtue of being the very falsehoods they are. In view of this puzzling fact, some have relaxed the truth requirement on understanding. I offer a factive view of understanding that fully accommodates the puzzling fact in four steps: (i) I argue that the question how these falsehoods are related to the phenomenon to be understood and the question how they figure into the content of understanding it are independent. (ii) I (...)
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  • La valeur de l'incertitude : l'évaluation de la précision des mesures physiques et les limites de la connaissance expérimentale.Fabien Grégis - 2016 - Dissertation, Université Sorbonne Paris Cité Université Paris.Diderot (Paris 7)
    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 (...)
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  • Qualitative Models in Computational Simulative Sciences: Representation, Confirmation, Experimentation.Nicola Angius - 2019 - Minds and Machines 29 (3):397-416.
    The Epistemology Of Computer Simulation has developed as an epistemological and methodological analysis of simulative sciences using quantitative computational models to represent and predict empirical phenomena of interest. In this paper, Executable Cell Biology and Agent-Based Modelling are examined to show how one may take advantage of qualitative computational models to evaluate reachability properties of reactive systems. In contrast to the thesis, advanced by EOCS, that computational models are not adequate representations of the simulated empirical systems, it is shown how (...)
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  • On the interpretation of Feynman diagrams, or, did the LHC experiments observe H → γγ?Oliver Passon - 2019 - European Journal for Philosophy of Science 9 (2):20.
    According to the received view Feynman diagrams are a bookkeeping device in complex perturbative calculations. Thus, they do not provide a representation or model of the underlying physical process. This view is in apparent tension with scientific practice in high energy physics, which analyses its data in terms of “channels”. For example the Higgs discovery was based on the observation of the decay H → γγ – a process which can be easily represented by the corresponding Feynman diagrams. I take (...)
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  • Representationalism is a dead end.Guilherme Sanches de Oliveira - 2018 - Synthese 198 (1):209-235.
    Representationalism—the view that scientific modeling is best understood in representational terms—is the received view in contemporary philosophy of science. Contributions to this literature have focused on a number of puzzles concerning the nature of representation and the epistemic role of misrepresentation, without considering whether these puzzles are the product of an inadequate analytical framework. The goal of this paper is to suggest that this possibility should be taken seriously. The argument has two parts, employing the “can’t have” and “don’t need” (...)
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  • Tool Migration: A Framework for Analyzing Cross-disciplinary Use of Mathematical Constructs.Chia-Hua Lin - unknown
    Mathematical formalisms that are constructed for inquiry in one disciplinary context are sometimes applied to another, a phenomenon that I call ‘tool migration.’ Philosophers of science have addressed the advantages of using migrated tools. In this paper, I argue that tool migration can be epistemically risky. I then develop an analytic framework for better understanding the risks that are implicit in tool migration. My approach shows that viewing mathematical constructs as tools while also acknowledging their representational features allows for a (...)
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  • Understanding the Progress of Science.C. D. McCoy - 2022 - In Insa Lawler, Kareem Khalifa & Elay Shech (eds.), Scientific Understanding and Representation: Modeling in the Physical Sciences. New York, NY: Routledge. pp. 353-369.
    Philosophical debates on how to account for the progress of science have traditionally divided along the realism-anti-realism axis. Relatively recent developments in epistemology, however, have opened up a new knowledge-understanding axis to the debate. This chapter presents a novel understanding-based account of scientific progress that takes its motivation from problem-solving practices in science. Problem-solving is characterized as a means of measuring degree of understanding, which is argued to be the principal epistemic (or cognitive) aim of science, over and against knowledge. (...)
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  • Is Captain Kirk a natural blonde? Do X-ray crystallographers dream of electron clouds? Comparing model-based inferences in science with fiction.Ann-Sophie Barwich - 2017 - In Otávio Bueno, Steven French, George Darby & Dean Rickles (eds.), Thinking About Science, Reflecting on Art: Bringing Aesthetics and Philosophy of Science Together. New York: Routledge.
    Scientific models share one central characteristic with fiction: their relation to the physical world is ambiguous. It is often unclear whether an element in a model represents something in the world or presents an artifact of model building. Fiction, too, can resemble our world to varying degrees. However, we assign a different epistemic function to scientific representations. As artifacts of human activity, how are scientific representations allowing us to make inferences about real phenomena? In reply to this concern, philosophers of (...)
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  • Functional Analyses, Mechanistic Explanations, and Explanatory Tradeoffs.Sergio Daniel Barberis - 2013 - Journal of Cognitive Science 14:229-251.
    Recently, Piccinini and Craver have stated three theses concerning the relations between functional analysis and mechanistic explanation in cognitive sciences: No Distinctness: functional analysis and mechanistic explanation are explanations of the same kind; Integration: functional analysis is a kind of mechanistic explanation; and Subordination: functional analyses are unsatisfactory sketches of mechanisms. In this paper, I argue, first, that functional analysis and mechanistic explanations are sub-kinds of explanation by scientific (idealized) models. From that point of view, we must take into account (...)
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  • The argument from surprise.Adrian Currie - 2018 - Canadian Journal of Philosophy 48 (5):639-661.
    I develop an account of productive surprise as an epistemic virtue of scientific investigations which does not turn on psychology alone. On my account, a scientific investigation is potentially productively surprising when results can conflict with epistemic expectations, those expectations pertain to a wide set of subjects. I argue that there are two sources of such surprise in science. One source, often identified with experiments, involves bringing our theoretical ideas in contact with new empirical observations. Another, often identified with simulations, (...)
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  • Structures in Real Theory Application: A Study in Feasible Epistemology.Robert H. C. Moir - 2013 - Dissertation, University of Western Ontario
    This thesis considers the following problem: What methods should the epistemology of science use to gain insight into the structure and behaviour of scientific knowledge and method in actual scientific practice? After arguing that the elucidation of epistemological and methodological phenomena in science requires a method that is rooted in formal methods, I consider two alternative methods for epistemology of science. One approach is the classical approaches of the syntactic and semantic views of theories. I show that typical approaches of (...)
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  • Measurement in Science.Eran Tal - 2015 - Stanford Encyclopedia of Philosophy.
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  • Credibility, Idealisation, and Model Building: An Inferential Approach.Xavier Donato Rodríguez & Jesús Zamora Bonilla - 2009 - Erkenntnis 70 (1):101-118.
    In this article we defend the inferential view of scientific models and idealisation. Models are seen as “inferential prostheses” (instruments for surrogative reasoning) construed by means of an idealisation-concretisation process, which we essentially understand as a kind of counterfactual deformation procedure (also analysed in inferential terms). The value of scientific representation is understood in terms not only of the success of the inferential outcomes arrived at with its help, but also of the heuristic power of representation and their capacity to (...)
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  • To envision a new particle or change an existing law? Hypothesis formation and anomaly resolution for the curious case of the β decay spectrum.Tjerk Gauderis - 2014 - Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 45:27-45.
    This paper addresses the question of how scientists determine which type of hypothesis is most suitable for tackling a particular problem by examining the historical case of the anomalous β spectrum in early nuclear physics, a puzzle that occasioned the most diverse hypotheses amongst physicists at the time. It is shown that such determinations are most often implicitly informed by scientists' individual perspectives on the structural relations between the various elements of the theory and the problem at hand. In addition (...)
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  • (3 other versions)Models and representation.Roman Frigg & James Nguyen - 2017 - In Lorenzo Magnani & Tommaso Bertolotti (eds.), Springer Handbook of Model-Based Science. Springer. pp. 49-102.
    Scientific discourse is rife with passages that appear to be ordinary descriptions of systems of interest in a particular discipline. Equally, the pages of textbooks and journals are filled with discussions of the properties and the behavior of those systems. Students of mechanics investigate at length the dynamical properties of a system consisting of two or three spinning spheres with homogenous mass distributions gravitationally interacting only with each other. Population biologists study the evolution of one species procreating at a constant (...)
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  • Between Isolations and Constructions: Economic Models as Believable Worlds.Lukasz Hardt - 2016 - Poznan Studies in the Philosophy of the Sciences and the Humanities 106.
    As the title of this essay suggests, my concern is with the issue of what are economic models. However, the goal of the paper is not to offer an in-depth study on multiple approaches to modelling in economics, but rather to overcome the dichotomical divide between conceptualizing models as isolations and constructions. This is done by introducing the idea of economic models as believable worlds, precisely descriptions of mechanisms that refer to the essentials of the modelled targets. In doing so (...)
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  • How could Vygotsky inform an approach to scientific representations?Dimitris Kilakos - 2016 - Epistemology and Philosophy of Science 47 (1):140-152.
    In the quest for a new social turn in philosophy of science, exploring the prospects of a Vygotskian perspective could be of significant interest, especially due to his emphasis on the role of culture and socialisation in the development of cognitive functions. However, a philosophical reassessment of Vygotsky's ideas in general has yet to be done. As a step towards this direction, I attempt to elaborate an approach on scientific representations by drawing inspirations from Vygotsky. Specifically, I work upon Vygotsky’s (...)
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  • Keeping quiet on the ontology of models.Steven French - 2010 - Synthese 172 (2):231-249.
    Stein once urged us not to confuse the means of representation with that which is being represented. Yet that is precisely what philosophers of science appear to have done at the meta-level when it comes to representing the practice of science. Proponents of the so-called ‘syntactic’ view identify theories as logically closed sets of sentences or propositions and models as idealised interpretations, or ‘theoruncula, as Braithwaite called them. Adherents of the ‘semantic’ approach, on the other hand, are typically characterised as (...)
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  • Modeling and Inferring in Science.Emiliano Ippoliti, Thomas Nickles & Fabio Sterpetti - 2016 - In Emiliano Ippoliti, Fabio Sterpetti & Thomas Nickles (eds.), Models and Inferences in Science. Cham: Springer. pp. 1-9.
    Science continually contributes new models and rethinks old ones. The way inferences are made is constantly being re-evaluated. The practice and achievements of science are both shaped by this process, so it is important to understand how models and inferences are made. But, despite the relevance of models and inference in scientific practice, these concepts still remain contro-versial in many respects. The attempt to understand the ways models and infer-ences are made basically opens two roads. The first one is to (...)
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  • Theories: Tools versus models.Mauricio Suárez & Nancy Cartwright - 2008 - Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 39 (1):62-81.
    In “The Toolbox of Science” (1995) together with Towfic Shomar we advocated a form of instrumentalism about scientific theories. We separately developed this view further in a number of subsequent works. Steven French, James Ladyman, Otavio Bueno and Newton Da Costa (FLBD) have since written at least eight papers and a book criticising our work. Here we defend ourselves. First we explain what we mean in denying that models derive from theory – and why their failure to do so should (...)
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  • (2 other versions)The philosophy of simulation: hot new issues or same old stew?Roman Frigg & Julian Reiss - 2008 - Synthese 169 (3):593-613.
    Computer simulations are an exciting tool that plays important roles in many scientific disciplines. This has attracted the attention of a number of philosophers of science. The main tenor in this literature is that computer simulations not only constitute interesting and powerful new science , but that they also raise a host of new philosophical issues. The protagonists in this debate claim no less than that simulations call into question our philosophical understanding of scientific ontology, the epistemology and semantics of (...)
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  • Computing the uncomputable; or, The discrete charm of second-order simulacra.Matthew W. Parker - 2009 - Synthese 169 (3):447-463.
    We examine a case in which non-computable behavior in a model is revealed by computer simulation. This is possible due to differing notions of computability for sets in a continuous space. The argument originally given for the validity of the simulation involves a simpler simulation of the simulation, still further simulations thereof, and a universality conjecture. There are difficulties with that argument, but there are other, heuristic arguments supporting the qualitative results. It is urged, using this example, that absolute validation, (...)
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  • Holistic modeling: an objection to Weisberg’s weighted feature-matching account.Wei Fang - 2017 - Synthese 194 (5):1743–1764.
    Michael Weisberg’s account of scientific models concentrates on the ways in which models are similar to their targets. He intends not merely to explain what similarity consists in, but also to capture similarity judgments made by scientists. In order to scrutinize whether his account fulfills this goal, I outline one common way in which scientists judge whether a model is similar enough to its target, namely maximum likelihood estimation method. Then I consider whether Weisberg’s account could capture the judgments involved (...)
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  • The Ontic Account of Scientific Explanation.Carl F. Craver - 2014 - In Marie I. Kaiser, Oliver R. Scholz, Daniel Plenge & Andreas Hüttemann (eds.), Explanation in the special science: The case of biology and history. Dordrecht: Springer. pp. 27-52.
    According to one large family of views, scientific explanations explain a phenomenon (such as an event or a regularity) by subsuming it under a general representation, model, prototype, or schema (see Bechtel, W., & Abrahamsen, A. (2005). Explanation: A mechanist alternative. Studies in History and Philosophy of Biological and Biomedical Sciences, 36(2), 421–441; Churchland, P. M. (1989). A neurocomputational perspective: The nature of mind and the structure of science. Cambridge: MIT Press; Darden (2006); Hempel, C. G. (1965). Aspects of scientific (...)
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  • Modelos, idealizaciones: una crítica del ficcionalismo.Alejandro Cassini - 2013 - Principia: An International Journal of Epistemology 17 (3):345.
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  • Modeling as a Case for the Empirical Philosophy of Science.Ekaterina Svetlova - 2015 - In Hanne Andersen, Nancy J. Nersessian & Susann Wagenknecht (eds.), Empirical Philosophy of Science: Introducing Qualitative Methods into Philosophy of Science. Cham: Springer International Publishing. pp. 65-82.
    In recent years, the emergence of a new trend in contemporary philosophy has been observed in the increasing usage of empirical research methods to conduct philosophical inquiries. Although philosophers primarily use secondary data from other disciplines or apply quantitative methods (experiments, surveys, etc.), the rise of qualitative methods (e.g., in-depth interviews, participant observations and qualitative text analysis) can also be observed. In this paper, I focus on how qualitative research methods can be applied within philosophy of science, namely within the (...)
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  • Theoretical Understanding in Science.Mark P. Newman - 2017 - British Journal for the Philosophy of Science 68 (2).
    In this article I develop a model of theoretical understanding in science. This is a philosophical theory that specifies the conditions that are both necessary and sufficient for a scientist to satisfy the construction ‘S understands theory T ’. I first consider how this construction is preferable to others, then build a model of the requisite conditions on the basis of examples from elementary physics. I then show how this model of theoretical understanding can be made philosophically robust and provide (...)
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  • Laws of nature and the reality of the wave function.Mauro Dorato - 2015 - Synthese 192 (10):3179-3201.
    In this paper I review three different positions on the wave function, namely: nomological realism, dispositionalism, and configuration space realism by regarding as essential their capacity to account for the world of our experience. I conclude that the first two positions are committed to regard the wave function as an abstract entity. The third position will be shown to be a merely speculative attempt to derive a primitive ontology from a reified mathematical space. Without entering any discussion about nominalism, I (...)
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  • Iconic Representations and Representative Practices.Chiara Ambrosio - 2014 - International Studies in the Philosophy of Science 28 (3):255-275.
    I develop an account of scientific representations building on Charles S. Peirce's rich, and still underexplored, notion of iconicity. Iconic representations occupy a central place in Peirce's philosophy, in his innovative approach to logic and in his practice as a scientist. Starting from a discussion of Peirce's approach to diagrams, I claim that Peirce's own representations are in line with his formulation of iconicity, and that they are more broadly connected to the pragmatist philosophy he developed in parallel with his (...)
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  • Climate Simulations: Uncertain Projections for an Uncertain World.Rafaela Hillerbrand - 2014 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 45 (1):17-32.
    Between the fourth and the recent fifth IPCC report, science as well as policy making have made great advances in dealing with uncertainties in global climate models. However, the uncertainties public decision making has to deal with go well beyond what is currently addressed by policy makers and climatologists alike. It is shown in this paper that within an anthropocentric framework, a whole hierarchy of models from various scientific disciplines is needed for political decisions as regards climate change. Via what (...)
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  • International Handbook of Research in History, Philosophy and Science Teaching.Michael R. Matthews (ed.) - 2014 - Springer.
    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 (...)
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  • The Structure of Scientific Theories.Rasmus Grønfeldt Winther - 2015 - Stanford Encyclopedia of Philosophy.
    Scientific inquiry has led to immense explanatory and technological successes, partly as a result of the pervasiveness of scientific theories. Relativity theory, evolutionary theory, and plate tectonics were, and continue to be, wildly successful families of theories within physics, biology, and geology. Other powerful theory clusters inhabit comparatively recent disciplines such as cognitive science, climate science, molecular biology, microeconomics, and Geographic Information Science (GIS). Effective scientific theories magnify understanding, help supply legitimate explanations, and assist in formulating predictions. Moving from their (...)
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  • Modelling Beyond Application: Epistemic and Non-epistemic Values in Modern Science.Ekaterina Svetlova - 2014 - International Studies in the Philosophy of Science 28 (1):79-98.
    In recent years, philosophers of science have begun to realize that the clear separation of the creation of models in academia and the application of models outside science is not possible. When these philosophers address hybrid contexts in which science is entwined with policy, business, and other realms of society, these often practically oriented realms no longer represent ‘the surroundings’ of science but rather are considered an essential part of it. I argue—and demonstrate empirically—that the judgement of a theory or (...)
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  • Carnap's Forgotten Criterion of Empirical Significance.James Justus - 2014 - Mind 123 (490):415-436.
    The waning popularity of logical empiricism and the supposed discovery of insurmountable technical difficulties led most philosophers to abandon the project to formulate a formal criterion of empirical significance. Such a criterion would delineate claims that observation can confirm or disconfirm from those it cannot. Although early criteria were clearly inadequate, criticisms made of later, more sophisticated criteria were often indefensible or easily answered. Most importantly, Carnap’s last criterion was seriously misinterpreted and an amended version of it remains tenable.
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  • Feminist Philosophy of Science.Lynn Hankinson Nelson - 2002 - In Peter K. Machamer & Michael Silberstein (eds.), The Blackwell guide to the philosophy of science. Malden, Mass.: Blackwell. pp. 312–331.
    This chapter contains sections titled: Highlights of Past Literature Current Work Future Work.
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