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  1. Explanatory Idealizations.Andrew Wayne - manuscript
    A signal development in contemporary physics is the widespread use, in explanatory contexts, of highly idealized models. This paper argues that some highly idealized models in physics have genuine explanatory power, and it extends the explanatory role for such idealizations beyond the scope of previous philosophical work. It focuses on idealizations of nonlinear oscillator systems.
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  2. The Diversity of Models as a Means to Better Explanations in Economics.Emrah Aydinonat - forthcoming - Journal of Economic Methodology 25 (3):237-251.
    In Economics Rules, Dani Rodrik (2015) argues that what makes economics powerful despite the limitations of each and every model is its diversity of models. Rodrik suggests that the diversity of models in economics improves its explanatory capacities, but he does not fully explain how. I offer a clearer picture of how models relate to explanations of particular economic facts or events, and suggest that the diversity of models is a means to better economic explanations.
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  3. Deflationary Realism: Representation and Idealisation in Cognitive Science.Dimitri Coelho Mollo - forthcoming - Mind and Language:1-19.
    Debate on the nature of representation in cognitive systems tends to oscillate between robustly realist views and various anti-realist options. I defend an alternative view, deflationary realism, which sees cognitive representation as an offshoot of the extended application to cognitive systems of an explanatory model whose primary domain is public representation use. This extended application, justified by a common explanatory target, embodies idealisations, partial mismatches between model and reality. By seeing representation as part of an idealised model, deflationary realism avoids (...)
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  4. *G* as Bridge Model.Devin Sanchez Curry - forthcoming - Philosophy of Science.
    g—a statistical factor capturing intercorrelations between scores on different IQ tests—is of theoretical interest despite being a low-fidelity model of both folk psychological intelligence and its cognitive/neural underpinnings. g idealizes away from those aspects of cognitive/neural mechanisms that are not explanatory of the relevant variety of folk psychological intelligence, and idealizes away from those varieties of folk psychological intelligence that are not generated by the relevant cognitive/neural substrate. In this manner, g constitutes a high-fidelity bridge model of the relationship between (...)
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  5. A New Role for Mathematics in Empirical Sciences.Atoosa Kasirzadeh - forthcoming - Philosophy of Science.
    Mathematics is often taken to play one of two roles in the empirical sciences: either it represents empirical phenomena, or it explains these phenomena by imposing constraints on them. This paper identifies a third and distinct role which has not been fully appreciated in the literature, and may be pervasive in scientific practice. I call this the “bridging” role of mathematics, according to which mathematics acts as a connecting scheme in our explanatory reasoning about why and how two different descriptions (...)
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  6. Unifying the Essential Concepts of Biological Networks: Biological Insights and Philosophical Foundations.Daniel Kostic, Claus Hilgetag & Marc Tittgemeyer - forthcoming - Philosophical Transactions of the Royal Society B: Biological Sciences.
    Over the last decades, network-based approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While these approaches continue to grow very rapidly, some of their conceptual and methodological aspects still require a programmatic foundation. This challenge particularly concerns the question of whether a generalized account of explanatory, organisational and descriptive levels of networks can be applied universally across biological sciences. To this end, this highly interdisciplinary theme issue focuses on the definition, motivation (...)
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  7. Scientific Understanding and Felicitous Legitimate Falsehoods.Insa Lawler - forthcoming - Synthese:1-29.
    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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  8. Model Explanation Versus Model-Induced Explanation.Insa Lawler & Emily Sullivan - forthcoming - Foundations of Science:1-26.
    Scientists appeal to models when explaining phenomena. Such explanations are often dubbed model explanations or model-based explanations. But what are the precise conditions for ME? Are ME special explanations? In our paper, we first rebut two definitions of ME and specify a more promising one. Based on this analysis, we single out a related conception that is concerned with explanations that are induced from working with a model. We call them ‘model-induced explanations’. Second, we study three paradigmatic cases of alleged (...)
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  9. On the Relationship Between Modelling Practices and Interpretive Stances in Quantum Mechanics.Quentin Ruyant - forthcoming - Foundations of Science:1-19.
    The purpose of this article is to establish a connection between modelling practices and interpretive approaches in quantum mechanics, taking as a starting point the literature on scientific representation. Different types of modalities play different roles in scientific representation. I postulate that the way theoretical structures are interpreted in this respect affects the way models are constructed. In quantum mechanics, this would be the case in particular of initial conditions and observables. I examine two formulations of quantum mechanics, the standard (...)
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  10. Understanding From Machine Learning Models.Emily Sullivan - forthcoming - British Journal for the Philosophy of Science:axz035.
    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 models provide understanding misguided? In (...)
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  11. Mechanistic Idealization in Systems Biology.Dingmar van Eck & Cory Wright - forthcoming - Synthese.
    This paper adds to the philosophical literature on mechanistic explanation by elaborating two related explanatory functions of idealisation in mechanistic models. The first function involves explaining the presence of structural/organizational features of mechanisms by reference to their role as difference-makers for performance requirements. The second involves tracking counterfactual dependency relations between features of mechanisms and features of mechanistic explanandum phenomena. To make these functions salient, we relate our discussion to an exemplar from systems biological research on the mechanism for countering (...)
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  12. Computational Cognitive Neuroscience.Carlos Zednik - forthcoming - In Mark Sprevak & Matteo Colombo (eds.), The Routledge Handbook of the Computational Mind. Routledge.
    This chapter provides an overview of the basic research strategies and analytic techniques deployed in computational cognitive neuroscience. On the one hand, “top-down” strategies are used to infer, from formal characterizations of behavior and cognition, the computational properties of underlying neural mechanisms. On the other hand, “bottom-up” research strategies are used to identify neural mechanisms and to reconstruct their computational capacities. Both of these strategies rely on experimental techniques familiar from other branches of neuroscience, including functional magnetic resonance imaging, single-cell (...)
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  13. How-Possibly Explanations in Economics: Anything Goes?Till Grüne-Yanoff & Philippe Verreault-Julien - 2021 - Journal of Economic Methodology 28 (1):114-123.
    The recent literature on economic models has rejected the traditional requirement that their epistemic value necessary depended on them offering actual explanations of phenomena. Contributors to that literature have argued that many models do not aim at providing how-actually explanations, but instead how-possibly explanations. However, how to assess the epistemic value of HPEs remains an open question. We present a programmatic approach to answering it. We first introduce a conceptual framework that distinguishes how-actually explanations from how-possibly explanations and that further (...)
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  14. The Historical Distinctiveness of Central Europe: A Study in the Philosophy of History.Krzysztof Brzechczyn - 2020 - Bern: Peter Lang.
    The aim of this book is to explain economic dualism in the history of modern Europe. The emergence of the manorial-serf economy in the Bohemia, Poland, and Hungary in the 16th and the 17th centuries was the result of a cumulative impact of various circumstantial factors. The weakness of cities in Central Europe disturbed the social balance – so characteristic for Western-European societies – between burghers and the nobility. The political dominance of the nobility hampered the development of cities and (...)
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  15. Extrapolating From Laboratory Behavioral Research on Nonhuman Primates Is Unjustified.Parker Crutchfield - 2020 - Journal of Applied Philosophy 37 (4):628-645.
    Conducting research on animals is supposed to be valuable because it provides information on how human mechanisms work. But for the use of animal models to be ethically justified, it must be epistemically justified. The inference from an observation about an animal model to a conclusion about humans must be warranted for the use of animals to be moral. When researchers infer from animals to humans, it’s an extrapolation. Often non-human primates are used as animal models in laboratory behavioral research. (...)
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  16. Viewing-as Explanations and Ontic Dependence.William D’Alessandro - 2020 - Philosophical Studies 177 (3):769-792.
    According to a widespread view in metaphysics and philosophy of science, all explanations involve relations of ontic dependence between the items appearing in the explanandum and the items appearing in the explanans. I argue that a family of mathematical cases, which I call “viewing-as explanations”, are incompatible with the Dependence Thesis. These cases, I claim, feature genuine explanations that aren’t supported by ontic dependence relations. Hence the thesis isn’t true in general. The first part of the paper defends this claim (...)
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  17. Explanations and candidate explanations in physics.Martin King - 2020 - European Journal for Philosophy of Science 10 (1):1-17.
    There has been a growing trend to include non-causal models in accounts of scientific explanation. A worry addressed in this paper is that without a higher threshold for explanation there are no tools for distinguishing between models that provide genuine explanations and those that provide merely potential explanations. To remedy this, a condition is introduced that extends a veridicality requirement to models that are empirically underdetermined, highly-idealised, or otherwise non-causal. This condition is applied to models of electroweak symmetry breaking beyond (...)
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  18. The Exploratory Status of Postconnectionist Models.Miljana Milojevic & Vanja Subotić - 2020 - Theoria: Beograd 2 (63):135-164.
    This paper aims to offer a new view of the role of connectionist models in the study of human cognition through the conceptualization of the history of connectionism – from the simplest perceptrons to convolutional neural nets based on deep learning techniques, as well as through the interpretation of criticism coming from symbolic cognitive science. Namely, the connectionist approach in cognitive science was the target of sharp criticism from the symbolists, which on several occasions caused its marginalization and almost complete (...)
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  19. Préface à "La diffusion de la Covid-19 - Que peuvent les modèles ?".Franck Varenne - 2020 - In Juliette Rouchier & Victorien Barbet (eds.), La diffusion de la Covid-19. Paris: pp. 3-10.
    Voilà un livre comme on pouvait l’espérer. Centré sur la Covid-19 et sur sa diffusion, il s’installe au cœur de questions brûlantes, encore urgentes pour tout un chacun, mais il garde aussi la tête froide, prend du recul, informe, enseigne et questionne, qui plus est de façon pédagogique. Davantage : au-delà du bilan critique, il propose des perspectives inédites, voire quelques suggestions solides. Il nous donne à réfléchir sur des chemins moins balisés. À le lire, on comprendra, par l’exemple, pourquoi (...)
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  20. Near-Decomposability and the Timescale Relativity of Causal Representations.Naftali Weinberger - 2020 - Philosophy of Science 87 (5):841-856.
    A common strategy for simplifying complex systems involves partitioning them into subsystems whose behaviors are roughly independent of one another at shorter timescales. Dynamic causal models clarify how doing so reveals a system’s nonequilibrium causal relationships. Here I use these models to elucidate the idealizations and abstractions involved in representing a system at a timescale. The models reveal that key features of causal representations—such as which variables are exogenous—may vary with the timescale at which a system is considered. This has (...)
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  21. Explicating Objectual Understanding: Taking Degrees Seriously.Christoph Baumberger - 2019 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 1:1-22.
    The paper argues that an account of understanding should take the form of a Carnapian explication and acknowledge that understanding comes in degrees. An explication of objectual understanding is defended, which helps to make sense of the cognitive achievements and goals of science. The explication combines a necessary condition with three evaluative dimensions: An epistemic agent understands a subject matter by means of a theory only if the agent commits herself sufficiently to the theory of the subject matter, and to (...)
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  22. What We Cannot Learn From Analogue Experiments.Karen Crowther, Niels S. Linnemann & Christian Wüthrich - 2019 - Synthese:1-26.
    Analogue experiments have attracted interest for their potential to shed light on inaccessible domains. For instance, ‘dumb holes’ in fluids and Bose–Einstein condensates, as analogues of black holes, have been promoted as means of confirming the existence of Hawking radiation in real black holes. We compare analogue experiments with other cases of experiment and simulation in physics. We argue—contra recent claims in the philosophical literature—that analogue experiments are not capable of confirming the existence of particular phenomena in inaccessible target systems. (...)
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  23. The Relation Between Scientific Models and Their Targets: Report on the “Representation in Science” Workshop.Aldo Filomeno - 2019 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 50 (2):307-310.
    Brief overview of the debates held in the workshop on scientific representation, in Prague, May 2018.
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  24. The Epistemic Virtue of Robustness in Climate Modeling (MA Dissertation).Parjanya Joshi - 2019 - Dissertation, Tata Institute of Social Sciences
    The aim of this dissertation is to comprehensively study various robustness arguments proposed in the literature from Levins to Lloyd as well as the opposition offered to them and pose enquiry into the degree of epistemic virtue that they provide to the model prediction results with respect to climate science and modeling. Another critical issue that this dissertation strives to examine is that of the actual epistemic notion that is operational when scientists and philosophers appeal to robustness. In attempting to (...)
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  25. Two Tales of Epistemic Models.Yang Liu - 2019 - Thought: A Journal of Philosophy 8 (4):291-302.
    This short paper has two parts. First, we prove a generalisation of Aumann's surprising impossibility result in the context of rational decision making. We then move, in the second part, to discuss the interpretational meaning of some formal setups of epistemic models, and we do so by means of presenting an interesting puzzle in epistemic logic. The aim is to highlight certain problematic aspects of these epistemic systems concerning first/third-person asymmetry which underlies both parts of the story. This asymmetry, we (...)
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  26. Analytic Narratives: What They Are and How They Contribute to Historical Explanation.Philippe Mongin - 2019 - In Claude Diebolt & Michael Haupert (eds.), Handbook of Cliometrics. Berlin: Springer.
    The expression "analytic narratives" is used to refer to a range of quite recent studies that lie on the boundaries between history, political science, and economics. These studies purport to explain specific historical events by combining the usual narrative approach of historians with the analytic tools that economists and political scientists draw from formal rational choice theories. Game theory, especially of the extensive form version, is currently prominent among these tools, but there is nothing inevitable about such a technical choice. (...)
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  27. Integrative Pluralism for Biological Function.Beckett Sterner & Samuel Cusimano - 2019 - Biology and Philosophy 34 (6):1-21.
    We introduce a new type of pluralism about biological function that, in contrast to existing, demonstrates a practical integration among the term’s different meanings. In particular, we show how to generalize Sandra Mitchell’s notion of integrative pluralism to circumstances where multiple epistemic tools of the same type are jointly necessary to solve scientific problems. We argue that the multiple definitions of biological function operate jointly in this way based on how biologists explain the evolution of protein function. To clarify how (...)
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  28. Understanding Does Not Depend on (Causal) Explanation.Philippe Verreault-Julien - 2019 - European Journal for Philosophy of Science 9 (2):18.
    One can find in the literature two sets of views concerning the relationship between understanding and explanation: that one understands only if 1) one has knowledge of causes and 2) that knowledge is provided by an explanation. Taken together, these tenets characterize what I call the narrow knowledge account of understanding. While the first tenet has recently come under severe attack, the second has been more resistant to change. I argue that we have good reasons to reject it on the (...)
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  29. Understanding with Models.Philippe Verreault-Julien - 2019 - Erasmus Journal for Philosophy and Economics 12 (1):133-136.
  30. How Could Models Possibly Provide How-Possibly Explanations?Philippe Verreault-Julien - 2019 - Studies in History and Philosophy of Science Part A 73:1-12.
    One puzzle concerning highly idealized models is whether they explain. Some suggest they provide so-called ‘how-possibly explanations’. However, this raises an important question about the nature of how-possibly explanations, namely what distinguishes them from ‘normal’, or how-actually, explanations? I provide an account of how-possibly explanations that clarifies their nature in the context of solving the puzzle of model-based explanation. I argue that the modal notions of actuality and possibility provide the relevant dividing lines between how-possibly and how-actually explanations. Whereas how-possibly (...)
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  31. Revisiting Model-Based Learning. [REVIEW]Yann Benétreau-Dupin - 2018 - Science & Education 27 (9-10):1033-1037.
  32. Explanation of Molecular Processes Without Tracking Mechanism Operation.Ingo Brigandt - 2018 - Philosophy of Science 85 (5):984–997.
    Philosophical discussions of systems biology have enriched the notion of mechanistic explanation by pointing to the role of mathematical modeling. However, such accounts still focus on explanation in terms of tracking a mechanism's operation across time (by means of mental or computational simulation). My contention is that there are explanations of molecular systems where the explanatory understanding does not consist in tracking a mechanism's operation and productive continuity. I make this case by a discussion of bifurcation analysis in dynamical systems, (...)
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  33. Idealizations, Essential Self-Adjointness, and Minimal Model Explanation in the Aharonov–Bohm Effect.Shech Elay - 2018 - Synthese 195 (11):4839-4863.
    Two approaches to understanding the idealizations that arise in the Aharonov–Bohm effect are presented. It is argued that a common topological approach, which takes the non-simply connected electron configuration space to be an essential element in the explanation and understanding of the effect, is flawed. An alternative approach is outlined. Consequently, it is shown that the existence and uniqueness of self-adjoint extensions of symmetric operators in quantum mechanics have important implications for philosophical issues. Also, the alleged indispensable explanatory role of (...)
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  34. Learning About Reality Through Models and Computer Simulations.Melissa Jacquart - 2018 - Science & Education 27 (7-8):805-810.
    Margaret Morrison, (2015) Reconstructing Reality: Models, Mathematics, and Simulations. Oxford University Press, New York. -/- Scientific models, mathematical equations, and computer simulations are indispensable to scientific practice. Through the use of models, scientists are able to effectively learn about how the world works, and to discover new information. However, there is a challenge in understanding how scientists can generate knowledge from their use, stemming from the fact that models and computer simulations are necessarily incomplete representations, and partial descriptions, of their (...)
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  35. Understanding (with) Toy Models.Alexander Reutlinger, Dominik Hangleiter & Stephan Hartmann - 2018 - British Journal for the Philosophy of Science 69 (4):1069-1099.
    Toy models are highly idealized and extremely simple models. Although they are omnipresent across scientific disciplines, toy models are a surprisingly under-appreciated subject in the philosophy of science. The main philosophical puzzle regarding toy models concerns what the epistemic goal of toy modelling is. One promising proposal for answering this question is the claim that the epistemic goal of toy models is to provide individual scientists with understanding. The aim of this article is to precisely articulate and to defend this (...)
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  36. Rational Analysis, Intractability, and the Prospects of ‘as If’-Explanations.Iris van Rooij, Cory Wright, Johan Kwisthout & Todd Wareham - 2018 - Synthese 195 (2):491-510.
    Despite their success in describing and predicting cognitive behavior, the plausibility of so-called ‘rational explanations’ is often contested on the grounds of computational intractability. Several cognitive scientists have argued that such intractability is an orthogonal pseudoproblem, however, since rational explanations account for the ‘why’ of cognition but are agnostic about the ‘how’. Their central premise is that humans do not actually perform the rational calculations posited by their models, but only act as if they do. Whether or not the problem (...)
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  37. From Models to Simulations.Franck Varenne - 2018 - London, UK: Routledge.
    This book analyses the impact computerization has had on contemporary science and explains the origins, technical nature and epistemological consequences of the current decisive interplay between technology and science: an intertwining of formalism, computation, data acquisition, data and visualization and how these factors have led to the spread of simulation models since the 1950s. -/- Using historical, comparative and interpretative case studies from a range of disciplines, with a particular emphasis on the case of plant studies, the author shows how (...)
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  38. Models and Mechanisms in Network Neuroscience.Carlos Zednik - 2018 - Philosophical Psychology 32 (1):23-51.
    This paper considers the way mathematical and computational models are used in network neuroscience to deliver mechanistic explanations. Two case studies are considered: Recent work on klinotaxis by Caenorhabditis elegans, and a longstanding research effort on the network basis of schizophrenia in humans. These case studies illustrate the various ways in which network, simulation and dynamical models contribute to the aim of representing and understanding network mechanisms in the brain, and thus, of delivering mechanistic explanations. After outlining this mechanistic construal (...)
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  39. Understanding, Explanation, and Scientific Knowledge.Kareem Khalifa - 2017 - Cambridge, UK: Cambridge University Press.
    From antiquity to the end of the twentieth century, philosophical discussions of understanding remained undeveloped, guided by a 'received view' that takes understanding to be nothing more than knowledge of an explanation. More recently, however, this received view has been criticized, and bold new philosophical proposals about understanding have emerged in its place. In this book, Kareem Khalifa argues that the received view should be revised but not abandoned. In doing so, he clarifies and answers the most central questions in (...)
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  40. What is the Problem of Explanation and Modeling?Raphael van Riel - 2017 - Acta Analytica 32 (3):263-275.
  41. Théories et modèles en sciences humaines. Le cas de la géographie.Franck Varenne - 2017 - Paris, France: Editions Matériologiques.
    Face à la diversité et à la complexification des modes de formalisation, une épistémologie des méthodes scientifiques doit confronter directement ses analyses à une pluralité d’études de cas comparatives. C’est l’objectif de cet ouvrage. -/- Aussi, dans une première partie, propose-t-il d’abord une classification large et raisonnée des différentes fonctions de connaissance des théories, des modèles et des simulations (de fait, cette partie constitue un panorama d’épistémologie générale particulièrement poussé). C’est ensuite à la lumière de cette classification que les deux (...)
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  42. Non-Causal Understanding with Economic Models: The Case of General Equilibrium.Philippe Verreault-Julien - 2017 - Journal of Economic Methodology 24 (3):297-317.
    How can we use models to understand real phenomena if models misrepresent the very phenomena we seek to understand? Some accounts suggest that models may afford understanding by providing causal knowledge about phenomena via how-possibly explanations. However, general equilibrium models, for example, pose a challenge to this solution since their contribution appears to be purely mathematical results. Despite this, practitioners widely acknowledge that it improves our understanding of the world. I argue that the Arrow–Debreu model provides a mathematical how-possibly explanation (...)
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  43. Point-Particle Explanations: The Case of Gravitational Waves.Andrew Wayne - 2017 - Synthese:1-21.
    This paper explores the role of physically impossible idealizations in model-based explanation. We do this by examining the explanation of gravitational waves from distant stellar objects using models that contain point-particle idealizations. Like infinite idealizations in thermodynamics, biology and economics, the point-particle idealization in general relativity is physically impossible. What makes this case interesting is that there are two very different kinds of models used for predicting the same gravitational wave phenomena, post-Newtonian models and effective field theory models. The paper (...)
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  44. One Mechanism, Many Models: A Distributed Theory of Mechanistic Explanation.Eric Hochstein - 2016 - Synthese 193 (5):1387-1407.
    There have been recent disagreements in the philosophy of neuroscience regarding which sorts of scientific models provide mechanistic explanations, and which do not. These disagreements often hinge on two commonly adopted, but conflicting, ways of understanding mechanistic explanations: what I call the “representation-as” account, and the “representation-of” account. In this paper, I argue that neither account does justice to neuroscientific practice. In their place, I offer a new alternative that can defuse some of these disagreements. I argue that individual models (...)
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  45. 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. 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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  46. Similarity, Adequacy, and Purpose: Understanding the Success of Scientific Models.Melissa Jacquart - 2016 - Dissertation, University of Western Ontario
    A central component to scientific practice is the construction and use of scientific models. Scientists believe that the success of a model justifies making claims that go beyond the model itself. However, philosophical analysis of models suggests that drawing inferences about the world from successful models is more complex. In this dissertation I develop a framework that can help disentangle the related strands of evaluation of model success, model extendibility, and the ability to draw ampliative inferences about the world from (...)
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  47. 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 that a (...)
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  48. Abstraction and its Limits: Finding Space For Novel Explanation.Eleanor Knox - 2016 - Noûs 50 (1):41-60.
    Several modern accounts of explanation acknowledge the importance of abstraction and idealization for our explanatory practice. However, once we allow a role for abstraction, questions remain. I ask whether the relation between explanations at different theoretical levels should be thought of wholly in terms of abstraction, and argue that changes of the quantities in terms of which we describe a system can lead to novel explanations that are not merely abstractions of some more detailed picture. I use the example of (...)
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  49. How Are Models and Explanations Related?Yasha Rohwer & Collin C. Rice - 2016 - Erkenntnis 81 (5):1127-1148.
    Within the modeling literature, there is often an implicit assumption about the relationship between a given model and a scientific explanation. The goal of this article is to provide a unified framework with which to analyze the myriad relationships between a model and an explanation. Our framework distinguishes two fundamental kinds of relationships. The first is metaphysical, where the model is identified as an explanation or as a partial explanation. The second is epistemological, where the model produces understanding that is (...)
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  50. Taming Theory with Thought Experiments: Understanding and Scientific Progress.Michael T. Stuart - 2016 - Studies in History and Philosophy of Science Part A 58:24-33.
    I claim that one way thought experiments contribute to scientific progress is by increasing scientific understanding. Understanding does not have a currently accepted characterization in the philosophical literature, but I argue that we already have ways to test for it. For instance, current pedagogical practice often requires that students demonstrate being in either or both of the following two states: 1) Having grasped the meaning of some relevant theory, concept, law or model, 2) Being able to apply that theory, concept, (...)
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