Results for 'Scientific models'

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  1. The Fictional Character of Scientific Models.Stacie Friend - 2019 - In Arnon Levy & Peter Godfrey-Smith (eds.), The Scientific Imagination. New York, US: Oup Usa. pp. 101-126.
    Many philosophers have drawn parallels between scientific models and fictions. In this paper I will be concerned with a recent version of the analogy, which compares models to the imagined characters of fictional literature. Though versions of the position differ, the shared idea is that modeling essentially involves imagining concrete systems analogously to the way that we imagine characters and events in response to works of fiction. Advocates of this view argue that imagining concrete systems plays an (...)
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  2. Scientific Models.Stephen M. Downes - 2011 - Philosophy Compass 6 (11):757-764.
    This contribution provides an assessment of the epistemological role of scientific models. The prevalent view that all scientific models are representations of the world is rejected. This view points to a unified way of resolving epistemic issues for scientific models. The emerging consensus in philosophy of science that models have many different epistemic roles in science is presented and defended.
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  3. Are Scientific Models of life Testable? A lesson from Simpson's Paradox.Prasanta S. Bandyopadhyay, Don Dcruz, Nolan Grunska & Mark Greenwood - 2020 - Sci 1 (3).
    We address the need for a model by considering two competing theories regarding the origin of life: (i) the Metabolism First theory, and (ii) the RNA World theory. We discuss two interrelated points, namely: (i) Models are valuable tools for understanding both the processes and intricacies of origin-of-life issues, and (ii) Insights from models also help us to evaluate the core objection to origin-of-life theories, called “the inefficiency objection”, which is commonly raised by proponents of both the Metabolism (...)
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  4. The Modal Basis of Scientific Modelling.Tuomas E. Tahko - 2023 - Synthese 201 (75):1-16.
    The practice of scientific modelling often resorts to hypothetical, false, idealised, targetless, partial, generalised, and other types of modelling that appear to have at least partially non-actual targets. In this paper, I will argue that we can avoid a commitment to non-actual targets by sketching a framework where models are understood as having networks of possibilities as their targets. This raises a further question: what are the truthmakers for the modal claims that we can derive from models? (...)
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  5. Scientific Models and Representation.Gabriele Contessa - 2011 - In Steven French & Juha Saatsi (eds.), Continuum Companion to the Philosophy of Science. Continuum. pp. 120--137.
    My two daughters would love to go tobogganing down the hill by themselves, but they are just toddlers and I am an apprehensive parent, so, before letting them do so, I want to ensure that the toboggan won’t go too fast. But how fast will it go? One way to try to answer this question would be to tackle the problem head on. Since my daughters and their toboggan are initially at rest, according to classical mechanics, their final velocity will (...)
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  6. Scientific Model between Imagination and Reality (In Arabic).Salah Osman - 2000 - Alexandria, Egypt: Al Maaref Establishment Press.
    يناقش الكتاب دور النماذج الفكرية والمادية في اكتساب وتشكيل كافة أنماط المعارف الإنسانية، بداية من المعرفة العادية التي يسعى بها عامة الناس إلى فهم ما يدور حولهم من أمور الحياة، ومرورًا بالمعارف الفلسفية والدينية والفنية التي تحكم توجهات الإنسان العقلانية والوجدانية، ووصولاً إلى المعرفة العلمية الرامية إلى فهم ظواهر الكون وترويضها وفقًا لقوانين حاكمة. ويطرح الكتاب فرضًا أساسيًا مؤداه أن ما يتلفظ به العلماء من كلمات مثل «الفرض» و«القانون» و«النظرية» ما هي إلا أسماء مترادفة لشيء واحد يصب في خانة «النموذج»، (...)
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  7. On the dangers of making scientific models ontologically independent: Taking Richard Levins' warnings seriously.Rasmus Grønfeldt Winther - 2006 - Biology and Philosophy 21 (5):703-724.
    Levins and Lewontin have contributed significantly to our philosophical understanding of the structures, processes, and purposes of biological mathematical theorizing and modeling. Here I explore their separate and joint pleas to avoid making abstract and ideal scientific models ontologically independent by confusing or conflating our scientific models and the world. I differentiate two views of theorizing and modeling, orthodox and dialectical, in order to examine Levins and Lewontin’s, among others, advocacy of the latter view. I compare (...)
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  8. Models as make-believe: imagination, fiction, and scientific representation.Adam Toon - 2012 - New York: Palgrave-Macmillan.
    Models as Make-Believe offers a new approach to scientific modelling by looking to an unlikely source of inspiration: the dolls and toy trucks of children's games of make-believe.
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  9. Fictionalism, Realism, Empiricism on Scientific Models.Chuang Liu - 2014
    This paper defends an approach to modeling and models in science that is against model fictionalism of a recent stripe (the “new fictionalism” that takes models to be abstract entities that are analogous to works of fiction). It further argues that there is a version of fictionalism on models to which my approach is neutral and which only makes sense if one adopts a special sort of antirealism (e.g. constructive empiricism). Otherwise, my approach strongly suggests that one (...)
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  10. Intentional Models as Essential Scientific Tools.Eric Hochstein - 2013 - International Studies in the Philosophy of Science 27 (2):199-217.
    In this article, I argue that the use of scientific models that attribute intentional content to complex systems bears a striking similarity to the way in which statistical descriptions are used. To demonstrate this, I compare and contrast an intentional model with a statistical model, and argue that key similarities between the two give us compelling reasons to consider both as a type of phenomenological model. I then demonstrate how intentional descriptions play an important role in scientific (...)
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  11. General Morphological Analysis as a Basic Scientific Modelling Method.Tom Ritchey - 2018 - Journal of Technological Forecasting and Social Change 126:81-91.
    General Morphological Analysis (GMA) is a method for structuring a conceptual problem space – called a morphospace – and, through a process of existential combinatorics, synthesizing a solution space. As such, it is a basic modelling method, on a par with other scientific modelling methods including System Dynamics Modelling, Bayesian Networks and various types graph-based “influence diagrams”. The purpose of this article is 1) to present the theoretical and methodological basics of morphological modelling; 2) to situate GMA within a (...)
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  12. The Epistemic Role of Fiction in Scientific Models.Ana Katić - 2020 - Theoria: Beograd 63 (3):5-16.
    Giere’s analysis of the epistemic role of fiction in science and literature is the representative of antifictionists. Our research finds the three inconsistencies in his main paper regarding the comparison of fiction in scientific models and literary works. We analyze his argument and offer our solution to the issue favoring the perspective of fictionalism. Further, we support a typological differentiation of false representation in science into fictional and fictitious. The value of this differentiation we demonstrate by giving the (...)
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  13. Which Models of Scientific Explanation Are (In)Compatible with Inference to the Best Explanation?Yunus Prasetya - 2024 - British Journal for the Philosophy of Science 75 (1):209-232.
    In this article, I explore the compatibility of inference to the best explanation (IBE) with several influential models and accounts of scientific explanation. First, I explore the different conceptions of IBE and limit my discussion to two: the heuristic conception and the objective Bayesian conception. Next, I discuss five models of scientific explanation with regard to each model’s compatibility with IBE. I argue that Kitcher’s unificationist account supports IBE; Railton’s deductive–nomological–probabilistic model, Salmon’s statistical-relevance model, and van (...)
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  14. The Nature of Model-World Comparisons.Fiora Salis - 2016 - The Monist 99 (3):243-259.
    Upholders of fictionalism about scientific models have not yet successfully explained how scientists can learn about the real world by making comparisons between models and the real phenomena they stand for. In this paper I develop an account of model-world comparisons in terms of what I take to be the best antirealist analyses of comparative claims that emerge from the current debate on fiction.
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  15. Formal models of the scientific community and the value-ladenness of science.Vincenzo Politi - 2021 - European Journal for Philosophy of Science 11 (4):1-23.
    In the past few years, social epistemologists have developed several formal models of the social organisation of science. While their robustness and representational adequacy has been analysed at length, the function of these models has begun to be discussed in more general terms only recently. In this article, I will interpret many of the current formal models of the scientific community as representing the latest development of what I will call the ‘Kuhnian project’. These models (...)
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  16. Models, Brains, and Scientific Realism.Fabio Sterpetti - 2006 - In Lorenzo Magnani & Claudia Casadio (eds.), Model Based Reasoning in Science and Technology. Logical, Epistemological, and Cognitive Issues. Cham, Switzerland: Springer International Publishing. pp. 639-661.
    Prediction Error Minimization theory (PEM) is one of the most promising attempts to model perception in current science of mind, and it has recently been advocated by some prominent philosophers as Andy Clark and Jakob Hohwy. Briefly, PEM maintains that “the brain is an organ that on aver-age and over time continually minimizes the error between the sensory input it predicts on the basis of its model of the world and the actual sensory input” (Hohwy 2014, p. 2). An interesting (...)
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  17. Minimal Models and the Generalized Ontic Conception of Scientific Explanation.Mark Povich - 2018 - British Journal for the Philosophy of Science 69 (1):117-137.
    Batterman and Rice ([2014]) argue that minimal models possess explanatory power that cannot be captured by what they call ‘common features’ approaches to explanation. Minimal models are explanatory, according to Batterman and Rice, not in virtue of accurately representing relevant features, but in virtue of answering three questions that provide a ‘story about why large classes of features are irrelevant to the explanandum phenomenon’ ([2014], p. 356). In this article, I argue, first, that a method (the renormalization group) (...)
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  18. Steps towards a unified basis for scientific models and methods.Inge S. Helland - 2010 - Hackensack, NJ: World Scientific.
    The book attempts to build a bridge across three cultures: mathematical statistics, quantum theory and chemometrical methods.
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  19. Remarks on Hansson’s model of value-dependent scientific corpus.Philippe Stamenkovic - 2023 - Lato Sensu: Revue de la Société de Philosophie des Sciences 10 (1):39-62.
    This article discusses Sven Ove Hansson’s corpus model for the influence of values (in particular, non-epistemic ones) in the hypothesis acceptance/rejection phase of scientific inquiry. This corpus model is based on Hansson’s concepts of scientific corpus and science ‘in the large sense’. I first present Hansson’s corpus model of value influence with some introductory comments about its origins, a detailed presentation of the model with a new terminology, an analysis of its limits, and an appreciation of its handling (...)
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  20. Otto Neurath's Scientific Utopianism Revisited - A Refined Model for Utopias in Thought Experiments.Alexander Linsbichler & Ivan Ferreira da Cunha - 2023 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie (2):1-26.
    Otto Neurath’s empiricist methodology of economics and his contributions to politi- cal economy have gained increasing attention in recent years. We connect this research with contemporary debates regarding the epistemological status of thought experiments by reconstructing Neurath’s utopias as linchpins of thought experiments. In our three reconstructed examples of different uses of utopias/dystopias in thought experiments we employ a reformulation of Häggqvist’s model for thought experiments and we argue that: (1) Our reformulation of Häggqvist’s model more adequately complies with many (...)
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  21. Computing, Modelling, and Scientific Practice: Foundational Analyses and Limitations.Philippos Papayannopoulos - 2018 - Dissertation,
    This dissertation examines aspects of the interplay between computing and scientific practice. The appropriate foundational framework for such an endeavour is rather real computability than the classical computability theory. This is so because physical sciences, engineering, and applied mathematics mostly employ functions defined in continuous domains. But, contrary to the case of computation over natural numbers, there is no universally accepted framework for real computation; rather, there are two incompatible approaches --computable analysis and BSS model--, both claiming to formalise (...)
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  22. Diagrammatic Reasoning and Modelling in the Imagination: The Secret Weapons of the Scientific Revolution.James Franklin - 2000 - In Guy Freeland & Anthony Corones (eds.), 1543 and All That: Image and Word, Change and Continuity in the Proto-Scientific Revolution. Kluwer Academic Publishers.
    Just before the Scientific Revolution, there was a "Mathematical Revolution", heavily based on geometrical and machine diagrams. The "faculty of imagination" (now called scientific visualization) was developed to allow 3D understanding of planetary motion, human anatomy and the workings of machines. 1543 saw the publication of the heavily geometrical work of Copernicus and Vesalius, as well as the first Italian translation of Euclid.
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  23. Modelling the truth of scientific beliefs with cultural evolutionary theory.Krist Vaesen & Wybo Houkes - 2014 - Synthese 191 (1).
    Evolutionary anthropologists and archaeologists have been considerably successful in modelling the cumulative evolution of culture, of technological skills and knowledge in particular. Recently, one of these models has been introduced in the philosophy of science by De Cruz and De Smedt (Philos Stud 157:411–429, 2012), in an attempt to demonstrate that scientists may collectively come to hold more truth-approximating beliefs, despite the cognitive biases which they individually are known to be subject to. Here we identify a major shortcoming in (...)
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  24. Constructing Models of Ethical Knowledge: A Scientific Enterprise.L. P. Steffe - 2014 - Constructivist Foundations 9 (2):262-264.
    Open peer commentary on the article “Ethics: A Radical-constructivist Approach” by Andreas Quale. Upshot: The first of my two main goals in this commentary is to establish thinking of ethics as concepts rather than as non-cognitive knowledge. The second is to argue that establishing models of individuals’ ethical concepts is a scientific enterprise that is quite similar to establishing models of individuals’ mathematical concepts. To accomplish these two primary goals, I draw from my experience of working scientifically (...)
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  25. Models as interpreters.Chuanfei Chin - 2011 - Studies in History and Philosophy of Science Part A 42 (2):303-312.
    Most philosophical accounts of scientific models assume that models represent some aspect, or some theory, of reality. They also assume that interpretation plays only a supporting role. This paper challenges both assumptions. It proposes that models can be used in science to interpret reality. (a) I distinguish these interpretative models from representational ones. They find new meanings in a target system’s behaviour, rather than fit its parts together. They are built through idealisation, abstraction and recontextualisation. (...)
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  26. (1 other version)Models and Scientific Explanations.Robert C. Richardson - 1986 - Philosophica 37:59-72.
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  27. Models and Maps: An Essay on Epistemic Representation.Gabriele Contessa - manuscript
    This book defends a two-tiered account of epistemic representation--the sort of representation relation that holds between representations such as maps and scientific models and their targets. It defends a interpretational account of epistemic representation and a structural similarity account of overall faithful epistemic representation.
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  28. Non-scientific Sources of the Big Bang Model and its Interpretations.Gregory Bugajak - 2000 - In Niels Henrik Gregersen, Ulf Görman & Willem B. Drees (eds.), Studies in Science and Theology, vol. 7(1999–2000), University of Aarhus, Aarhus. pp. 151–159.
    In considering relations between science and theology, the discussion of the Big Bang model plays a significant role. Amongst the sources of this model there are not only scientific achievements of recent decades taken as objective knowledge as seen in modern methodology, but also many non-scientific factors. The latter is connected with the quite obvious fact that the authors, as well as the recipients of the Model, are people who are guided in their activity - including obtaining their (...)
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  29. Models of Scientific Change.Benjamin Aguilar - manuscript
    This paper challenges premises regarding the ‘Kuhn vs Popper debate’ which is often introduced to students at a university level. Though I acknowledge the disagreements between Kuhn and Popper, I argue that their models of science are greatly similar. To begin, some preliminary context is given to point out conceptual and terminological barriers within this debate. The remainder of paper illuminates consistencies between the influential books The Logic of Scientific Discoveries (by Popper, abbreviated as Logic) and The Structure (...)
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  30. The Human Model: Polymorphicity and Scientific Method in Aristotle’s Parts of Animals.Emily Nancy Kress - manuscript
    [penultimate draft; prepared for publication in Aristotle’s Parts of Animals: A Critical Guide, ed. Sophia Connell – please cite final version] -/- Parts of Animals II.10 makes a new beginning in Aristotle’s study of animals. In it, Aristotle proposes to “now speak as if we are once more at an origin, beginning first with those things that are primary” (655b28-9). This is the start of his account of the non-uniform parts of blooded animals: parts such as eyes, noses, mouths, etc., (...)
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  31. The scientific demarcation problem: a formal and model-based approach to falsificationism.Attard Jeremy - manuscript
    The problem of demarcating between what is scientific and what is pseudoscientific or merely unscientific - in other words, the problem of defining scientificity - remains open. The modern debate was firstly structured around Karl Popper's falsificationist epistemology from the 1930's, before diversifying a few decades later. His central idea is that what makes something scientific is not so much how adequate it is with data, but rather to what extent it might not have been so. Since the (...)
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  32. Regulative Idealization: A Kantian Approach to Idealized Models.Lorenzo Spagnesi - 2023 - Studies in History and Philosophy of Science 99 (C):1-9.
    Scientific models typically contain idealizations, or assumptions that are known not to be true. Philosophers have long questioned the nature of idealizations: Are they heuristic tools that will be abandoned? Or rather fictional representations of reality? And how can we reconcile them with realism about knowledge of nature? Immanuel Kant developed an account of scientific investigation that can inspire a new approach to the contemporary debate. Kant argued that scientific investigation is possible only if guided by (...)
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  33. Learning through the Scientific Imagination.Fiora Salis - 2020 - Argumenta 6 (1):65-80.
    Theoretical models are widely held as sources of knowledge of reality. Imagination is vital to their development and to the generation of plausible hypotheses about reality. But how can imagination, which is typically held to be completely free, effectively instruct us about reality? In this paper I argue that the key to answering this question is in constrained uses of imagination. More specifically, I identify make-believe as the right notion of imagination at work in modelling. I propose the first (...)
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  34. 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 (...)
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  35. Scientific Realism, the Semantic View and Evolutionary Biology.Fabio Sterpetti - 1st ed. 2016 - In Emiliano Ippoliti, Fabio Sterpetti & Thomas Nickles (eds.), Models and Inferences in Science. Cham: Springer. pp. 55-76.
    The semantic view of theories is normally considered to be an ac-count of theories congenial to Scientific Realism. Recently, it has been argued that Ontic Structural Realism could be fruitfully applied, in combination with the semantic view, to some of the philosophical issues peculiarly related to bi-ology. Given the central role that models have in the semantic view, and the relevance that mathematics has in the definition of the concept of model, the fo-cus will be on population genetics, (...)
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  36. Models as make-believe.Adam Toon - 2008 - In Roman Frigg & Matthew Hunter (eds.), Beyond Mimesis and Convention: Representation in Art and Science. Boston Studies in Philosophy of Science.
    In this paper I propose an account of representation for scientific models based on Kendall Walton’s ‘make-believe’ theory of representation in art. I first set out the problem of scientific representation and respond to a recent argument due to Craig Callender and Jonathan Cohen, which aims to show that the problem may be easily dismissed. I then introduce my account of models as props in games of make-believe and show how it offers a solution to the (...)
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  37. Normative Formal Epistemology as Modelling.Joe Roussos - forthcoming - The British Journal for the Philosophy of Science.
    I argue that normative formal epistemology (NFE) is best understood as modelling, in the sense that this is the reconstruction of its methodology on which NFE is doing best. I focus on Bayesianism and show that it has the characteristics of modelling. But modelling is a scientific enterprise, while NFE is normative. I thus develop an account of normative models on which they are idealised representations put to normative purposes. Normative assumptions, such as the transitivity of comparative credence, (...)
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  38. Models, information and meaning.Marc Artiga - 2020 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 82:101284.
    There has recently been an explosion of formal models of signalling, which have been developed to learn about different aspects of meaning. This paper discusses whether that success can also be used to provide an original naturalistic theory of meaning in terms of information or some related notion. In particular, it argues that, although these models can teach us a lot about different aspects of content, at the moment they fail to support the idea that meaning just is (...)
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  39. Extending Similarity-based Epistemology of Modality with Models.Ylwa Sjölin Wirling - 2022 - Ergo: An Open Access Journal of Philosophy 8 (45).
    Empiricist modal epistemologies can be attractive, but are often limited in the range of modal knowledge they manage to secure. In this paper, I argue that one such account – similarity-based modal empiricism – can be extended to also cover justification of many scientifically interesting possibility claims. Drawing on recent work on modelling in the philosophy of science, I suggest that scientific modelling is usefully seen as the creation and investigation of relevantly similar epistemic counterparts of real target systems. (...)
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  40. Unrealistic Models in Mathematics.William D'Alessandro - 2023 - Philosophers' Imprint 23 (#27).
    Models are indispensable tools of scientific inquiry, and one of their main uses is to improve our understanding of the phenomena they represent. How do models accomplish this? And what does this tell us about the nature of understanding? While much recent work has aimed at answering these questions, philosophers' focus has been squarely on models in empirical science. I aim to show that pure mathematics also deserves a seat at the table. I begin by presenting (...)
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  41. (1 other version)Models, Idealisations, and Realism.Juha Saatsi - 1st ed. 2016 - In Emiliano Ippoliti, Fabio Sterpetti & Thomas Nickles (eds.), Models and Inferences in Science. Cham: Springer.
    I explore a challenge that idealisations pose to scientific realism and argue that the realist can best accommodate idealisations by capitalising on certain modal features of idealised models that are underwritten by laws of nature.
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  42. Models of Introspection vs. Introspective Devices Testing the Research Programme for Possible Forms of Introspection.Krzysztof Dołęga - 2023 - Journal of Consciousness Studies 30 (9):86-101.
    The introspective devices framework proposed by Kammerer and Frankish (this issue) offers an attractive conceptual tool for evaluating and developing accounts of introspection. However, the framework assumes that different views about the nature of introspection can be easily evaluated against a set of common criteria. In this paper, I set out to test this assumption by analysing two formal models of introspection using the introspective device framework. The question I aim to answer is not only whether models developed (...)
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  43. Toward a New Model of Scientific Rationality.Howard Sankey - 1998 - In Dimitri Ginev (ed.), Meaningfulness, Meaning, Mediation: Essays in Honor of Prof. Dr. Dimitri Ginev. Sofia: Critique and Humanism Publishing House. pp. 69-81.
    The paper presents some thoughts about how an account of rationality might be recovered from what might have first appeared as anti-rationalistic ideas in the work of Kuhn and Feyerabend. The paper draws inspiration from some suggestions of Bernstein and Rorty, as well well as Brown's theory of rationality.
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  44. Understanding from Machine Learning Models.Emily Sullivan - 2022 - British Journal for the Philosophy of Science 73 (1):109-133.
    Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal (...) provide understanding misguided? In this paper, using the case of deep neural networks, I argue that it is not the complexity or black box nature of a model that limits how much understanding the model provides. Instead, it is a lack of scientific and empirical evidence supporting the link that connects a model to the target phenomenon that primarily prohibits understanding. (shrink)
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  45. Scientific Collaboration: Do Two Heads Need to Be More than Twice Better than One?Thomas Boyer-Kassem & Cyrille Imbert - 2015 - Philosophy of Science 82 (4):667-688.
    Epistemic accounts of scientific collaboration usually assume that, one way or another, two heads really are more than twice better than one. We show that this hypothesis is unduly strong. We present a deliberately crude model with unfavorable hypotheses. We show that, even then, when the priority rule is applied, large differences in successfulness can emerge from small differences in efficiency, with sometimes increasing marginal returns. We emphasize that success is sensitive to the structure of competing communities. Our results (...)
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  46. Imagination in scientific modeling.Adam Toon - 2016 - In Amy Kind (ed.), The Routledge Handbook of the Philosophy of Imagination. New York: Routledge. pp. 451-462.
    Modeling is central to scientific inquiry. It also depends heavily upon the imagination. In modeling, scientists seem to turn their attention away from the complexity of the real world to imagine a realm of perfect spheres, frictionless planes and perfect rational agents. Modeling poses many questions. What are models? How do they relate to the real world? Recently, a number of philosophers have addressed these questions by focusing on the role of the imagination in modeling. Some have also (...)
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  47. Model robustness as a confirmatory virtue: The case of climate science.Elisabeth A. Lloyd - 2015 - Studies in History and Philosophy of Science Part A 49:58-68.
    I propose a distinct type of robustness, which I suggest can support a confirmatory role in scientific reasoning, contrary to the usual philosophical claims. In model robustness, repeated production of the empirically successful model prediction or retrodiction against a background of independentlysupported and varying model constructions, within a group of models containing a shared causal factor, may suggest how confident we can be in the causal factor and predictions/retrodictions, especially once supported by a variety of evidence framework. I (...)
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  48. 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 (...)
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  49. Standards and the distribution of cognitive labour: A model of the dynamics of scientific activity.Langhe Rogieder & Greiff Matthias - 2010 - Logic Journal of the IGPL 18 (2):278-294.
    We present a model of the distribution of labour in science. Such models tend to rely on the mechanism of the invisible hand . Our analysis starts from the necessity of standards in distributed processes and the possibility of multiple standards in science. Invisible hand models turn out to have only limited scope because they are restricted to describing the atypical single-standard case. Our model is a generalisation of these models to J standards; single-standard models such (...)
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  50. On structural accounts of model-explanations.Martin King - 2016 - Synthese 193 (9):2761-2778.
    The focus in the literature on scientific explanation has shifted in recent years towards model-based approaches. In recent work, Alisa Bokulich has argued that idealization has a central role to play in explanation. Bokulich claims that certain highly-idealized, structural models can be explanatory, even though they are not considered explanatory by causal, mechanistic, or covering law accounts of explanation. This paper focuses on Bokulich’s account in order to make the more general claim that there are problems with maintaining (...)
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