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  1. Symbol Systems as Collective Representational Resources: Mary Hesse, Nelson Goodman, and the Problem of Scientific Representation.Axel Gelfert - 2015 - Social Epistemology Review and Reply Collective 4 (6):52-61.
    This short paper grew out of an observation—made in the course of a larger research project—of a surprising convergence between, on the one hand, certain themes in the work of Mary Hesse and Nelson Goodman in the 1950/60s and, on the other hand, recent work on the representational resources of science, in particular regarding model-based representation. The convergence between these more recent accounts of representation in science and the earlier proposals by Hesse and Goodman consists in the recognition that, in (...)
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  • Pluralists about Pluralism? Versions of Explanatory Pluralism in Psychiatry.Jeroen Van Bouwel - 2014 - In Thomas Uebel (ed.), New Directions in the Philosophy of Science. Cham: Springer. pp. 105-119.
    In this contribution, I comment on Raffaella Campaner’s defense of explanatory pluralism in psychiatry (in this volume). In her paper, Campaner focuses primarily on explanatory pluralism in contrast to explanatory reductionism. Furthermore, she distinguishes between pluralists who consider pluralism to be a temporary state on the one hand and pluralists who consider it to be a persisting state on the other hand. I suggest that it would be helpful to distinguish more than those two versions of pluralism – different understandings (...)
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  • Scientific Consensus and Expert Testimony in Courts: Lessons from the Bendectin Litigation.Boaz Miller - 2016 - Foundations of Science 21 (1):15-33.
    A consensus in a scientific community is often used as a resource for making informed public-policy decisions and deciding between rival expert testimonies in legal trials. This paper contains a social-epistemic analysis of the high-profile Bendectin drug controversy, which was decided in the courtroom inter alia by deference to a scientific consensus about the safety of Bendectin. Drawing on my previously developed account of knowledge-based consensus, I argue that the consensus in this case was not knowledge based, hence courts’ deference (...)
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  • When is consensus knowledge based? Distinguishing shared knowledge from mere agreement.Boaz Miller - 2013 - Synthese 190 (7):1293-1316.
    Scientific consensus is widely deferred to in public debates as a social indicator of the existence of knowledge. However, it is far from clear that such deference to consensus is always justified. The existence of agreement in a community of researchers is a contingent fact, and researchers may reach a consensus for all kinds of reasons, such as fighting a common foe or sharing a common bias. Scientific consensus, by itself, does not necessarily indicate the existence of shared knowledge among (...)
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  • Scientific representation and dissimilarity.Brandon Boesch - 2019 - Synthese 198 (6):5495-5513.
    In this essay, I examine the role of dissimilarity in scientific representation. After briefly reviewing some of the philosophical literature which places a strong emphasis on the role of similarity, I turn to examine some work from Carroll and Borges which demonstrates that perfect similarity is not valuable in the representational use of maps. Expanding on this insight, I go on to argue that this shows that dissimilarity is an important part of the representational use of maps—a point I then (...)
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  • Scientific modelling with diagrams.Ulrich E. Stegmann - 2019 - Synthese 198 (3):2675-2694.
    Diagrams can serve as representational models in scientific research, yet important questions remain about how they do so. I address some of these questions with a historical case study, in which diagrams were modified extensively in order to elaborate an early hypothesis of protein synthesis. The diagrams’ modelling role relied mainly on two features: diagrams were modified according to syntactic rules, which temporarily replaced physico-chemical reasoning, and diagram-to-target inferences were based on semantic interpretations. I then explore the lessons for the (...)
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  • (1 other version)What do numerical (climate) models really represent?Gabriele Gramelsberger - 2011 - Studies in History and Philosophy of Science Part A 42 (2):296-302.
    The translation of a mathematical model into a numerical one employs various modifications in order to make the model accessible for computation. Such modifications include discretizations, approximations, heuristic assumptions, and other methods. The paper investigates the divergent styles of mathematical and numerical models in the case of a specific piece of code in a current atmospheric model. Cognizance of these modifications means that the question of the role and function of scientific models has to be reworked. Neither are numerical models (...)
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  • How do different interpretations work together in a single scientific explanatory project? A case study of the Olami-Feder-Christensen model of earthquakes.Hernán Bobadilla - 2024 - European Journal for Philosophy of Science 14 (3):1-29.
    Interpretation plays a central role in using scientific models to explain natural phenomena: Meaning must be bestowed upon a model in terms of what it is and what it represents to be used for model explanations. However, it remains unclear how capacious and complex interpretation in models can be, particularly when conducted by the same group of scientists in the context of one explanatory project. This paper sheds light upon this question by examining modelling and explanatory practices related to the (...)
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  • Representation-supporting model elements.Sim-Hui Tee - 2020 - Biology and Philosophy 35 (1):1-24.
    It is assumed that scientific models contain no superfluous model elements in scientific representation. A representational model is constructed with all the model elements serving the representational purpose. The received view has it that there are no redundant model elements which are non-representational. Contrary to this received view, I argue that there exist some non-representational model elements which are essential in scientific representation. I call them representation-supporting model elements in virtue of the fact that they play the role to support (...)
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  • Applicability, Indispensability, and Underdetermination: Puzzling Over Wigner’s ‘Unreasonable Effectiveness of Mathematics’.Axel Gelfert - 2014 - Science & Education 23 (5):997-1009.
    In his influential 1960 paper ‘The Unreasonable Effectiveness of Mathematics in the Natural Sciences’, Eugene P. Wigner raises the question of why something that was developed without concern for empirical facts—mathematics—should turn out to be so powerful in explaining facts about the natural world. Recent philosophy of science has developed ‘Wigner’s puzzle’ in two different directions: First, in relation to the supposed indispensability of mathematical facts to particular scientific explanations and, secondly, in connection with the idea that aesthetic criteria track (...)
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  • Conceptual Modelling, Combinatorial Heuristics and Ars Inveniendi: An Epistemological History (Ch 1 & 2).Tom Ritchey - manuscript
    (1) An introduction to the principles of conceptual modelling, combinatorial heuristics and epistemological history; (2) the examination of a number of perennial epistemological-methodological schemata: conceptual spaces and blending theory; ars inveniendi and ars demonstrandi; the two modes of analysis and synthesis and their relationship to ars inveniendi; taxonomies and typologies as two fundamental epistemic structures; extended cognition, cognitio symbolica and model-based reasoning; (3) Plato’s notions of conceptual spaces, conceptual blending and hypothetical-analogical models (paradeigmata); (4) Ramon Llull’s concept analysis and combinatoric (...)
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  • Modelling and representing: An artefactual approach to model-based representation.Tarja Knuuttila - 2011 - Studies in History and Philosophy of Science Part A 42 (2):262-271.
    The recent discussion on scientific representation has focused on models and their relationship to the real world. It has been assumed that models give us knowledge because they represent their supposed real target systems. However, here agreement among philosophers of science has tended to end as they have presented widely different views on how representation should be understood. I will argue that the traditional representational approach is too limiting as regards the epistemic value of modelling given the focus on the (...)
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  • Strategies of model-building in condensed matter physics: trade-offs as a demarcation criterion between physics and biology?Axel Gelfert - 2013 - Synthese 190 (2):253-272.
    This paper contrasts and compares strategies of model-building in condensed matter physics and biology, with respect to their alleged unequal susceptibility to trade-offs between different theoretical desiderata. It challenges the view, often expressed in the philosophical literature on trade-offs in population biology, that the existence of systematic trade-offs is a feature that is specific to biological models, since unlike physics, biology studies evolved systems that exhibit considerable natural variability. By contrast, I argue that the development of ever more sophisticated experimental, (...)
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  • A strategy to what end? “The strategy of model building in population biology” in its programmatic context.Zvi Hasnes-Beninson - 2024 - History and Philosophy of the Life Sciences 46 (4):1-33.
    “The Strategy of Model Building in Population Biology” published by Richard Levins in 1966 has been cited over 2500 times. For a paper concerned with modeling approaches in population biology a surprisingly large part of the attention. The Strategy received comes from history and philosophy of biology, and specifically from accounts on model and model formulation. The Strategy is an unusual paper; it presents neither new data nor a new formal model; at times it reads like a manifesto for some (...)
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  • (1 other version)What do numerical models really represent?Gabriele Gramelsberger - 2011 - Studies in History and Philosophy of Science Part A 42 (2):296-302.
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