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  1. Peter Achinstein (1972). Models and Analogies: A Reply to Girill. Philosophy of Science 39 (2):235-240.
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  2. Peter Achinstein (1965). Theoretical Models. British Journal for the Philosophy of Science 16 (62):102-120.
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  3. Peter Achinstein (1964). Models, Analogies, and Theories. Philosophy of Science 31 (4):328-350.
    Recent accounts of scientific method suggest that a model, or analogy, for an axiomatized theory is another theory, or postulate set, with an identical calculus. The present paper examines five central theses underlying this position. In the light of examples from physical science it seems necessary to distinguish between models and analogies and to recognize the need for important revisions in the position under study, especially in claims involving an emphasis on logical structure and similarity in form between theory and (...)
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  4. Anna Alexandrova (2008). Making Models Count. Philosophy of Science 75 (3):383-404.
    What sort of claims do scientific models make and how do these claims then underwrite empirical successes such as explanations and reliable policy interventions? In this paper I propose answers to these questions for the class of models used throughout the social and biological sciences, namely idealized deductive ones with a causal interpretation. I argue that the two main existing accounts misrepresent how these models are actually used, and propose a new account. *Received July 2006; revised August 2008. †To contact (...)
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  5. Eugen Altschul & Erwin Biser (1948). The Validity of Unique Mathematical Models in Science. Philosophy of Science 15 (1):11-24.
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  6. D. A. Anapolitanos (1989). Theories and Their Models. Journal for General Philosophy of Science 20 (2):201-211.
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  7. Rachel A. Ankeny (2000). Fashioning Descriptive Models in Biology: Of Worms and Wiring Diagrams. Philosophy of Science 67 (3):272.
    The biological sciences have become increasingly reliant on so-called 'model organisms'. I argue that in this domain, the concept of a descriptive model is essential for understanding scientific practice. Using a case study, I show how such a model was formulated in a preexplanatory context for subsequent use as a prototype from which explanations ultimately may be generated both within the immediate domain of the original model and in additional, related domains. To develop this concept of a descriptive model, I (...)
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  8. Rachel A. Ankeny & Sabina Leonelli (2011). What's so Special About Model Organisms? Studies in History and Philosophy of Science Part A 42 (2):313-323.
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  9. Jan Baedke (2013). The Epigenetic Landscape in the Course of Time: Conrad Hal Waddington's Methodological Impact on the Life Sciences. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 44 (4):756-773.
    It seems that the reception of Conrad Hal Waddington’s work never really gathered speed in mainstream biology. This paper, offering a transdisciplinary survey of approaches using his epigenetic landscape images, argues that (i) Waddington’s legacy is much broader than is usually recognized—it is widespread across the life sciences (e.g. stem cell biology, developmental psychology and cultural anthropology). In addition, I will show that (ii) there exist as yet unrecognized heuristic roles, especially in model building and theory formation, which Waddington’s images (...)
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  10. Daniela M. Bailer-Jones (2002). Scientists' Thoughts on Scientific Models. Perspectives on Science 10 (3):275-301.
    : This paper contains the analysis of nine interviews with UK scientists on the topic of scientific models. Scientific models are an important, very controversially discussed topic in philosophy of science. A reasonable expectation is that philosophical conceptions of models ought to be in agreement with scientific practice. Questioning practicing scientists on their use of and views on models provides material against which philosophical positions can be measured.
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  11. Anouk Barberousse, Sara Franceschelli & Cyrille Imbert, Cellular Automata, Modeling, and Computation.
    Cellular Automata (CA) based simulations are widely used in a great variety of domains, fromstatistical physics to social science. They allow for spectacular displays and numerical predictions. Are they forall that a revolutionary modeling tool, allowing for “direct simulation”, or for the simulation of “the phenomenon itself”? Or are they merely models "of a phenomenological nature rather than of a fundamental one”? How do they compareto other modeling techniques? In order to answer these questions, we present a systematic exploration of (...)
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  12. Robert Batterman (2010). On the Explanatory Role of Mathematics in Empirical Science. British Journal for the Philosophy of Science 61 (1):1-25.
    This paper examines contemporary attempts to explicate the explanatory role of mathematics in the physical sciences. Most such approaches involve developing so-called mapping accounts of the relationships between the physical world and mathematical structures. The paper argues that the use of idealizations in physical theorizing poses serious difficulties for such mapping accounts. A new approach to the applicability of mathematics is proposed.
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  13. Robert W. Batterman (2009). Idealization and Modeling. Synthese 169 (3):427 - 446.
    This paper examines the role of mathematical idealization in describing and explaining various features of the world. It examines two cases: first, briefly, the modeling of shock formation using the idealization of the continuum. Second, and in more detail, the breaking of droplets from the points of view of both analytic fluid mechanics and molecular dynamical simulations at the nano-level. It argues that the continuum idealizations are explanatorily ineliminable and that a full understanding of certain physical phenomena cannot be obtained (...)
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  14. Robert W. Batterman (2002). Asymptotics and the Role of Minimal Models. British Journal for the Philosophy of Science 53 (1):21-38.
    A traditional view of mathematical modeling holds, roughly, that the more details of the phenomenon being modeled that are represented in the model, the better the model is. This paper argues that often times this ‘details is better’ approach is misguided. One ought, in certain circumstances, to search for an exactly solvable minimal model—one which is, essentially, a caricature of the physics of the phenomenon in question.
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  15. William Bechtel, Some Virtues of Modeling with Both Hands.
    Webb distinguishes two endeavors she calls animal modeling and animat modeling and advocates for the former. I share her preference and point to additional virtues of modeling actual biological mechanisms (animal modeling). As Webb argues, animat modeling should be regarded as modeling of specific, but madeup, biological mechanisms. I contend that modeling made-up mechanisms in situations in which we have some knowledge of the actual mechanisms involved is modeling with one hand—the good one—tied behind one’s back.1 The hand that is (...)
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  16. David Berlinski (1975). Mathematical Models of the World. Synthese 31 (2):211 - 227.
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  17. Gregor Betz (2013). Chaos, Plurality and Model Metrics in Climate Science. In Ulrich V. Gähde & Stephan Hartmann (eds.), Models, Simulation, and the Reduction of Complexity. de Gruyter. 255-264.
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  18. Alisa Bokulich (2011). How Scientific Models Can Explain. Synthese 180 (1):33 - 45.
    Scientific models invariably involve some degree of idealization, abstraction, or nationalization of their target system. Nonetheless, I argue that there are circumstances under which such false models can offer genuine scientific explanations. After reviewing three different proposals in the literature for how models can explain, I shall introduce a more general account of what I call model explanations, which specify the conditions under which models can be counted as explanatory. I shall illustrate this new framework by applying it to the (...)
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  19. Agnes Bolinska (2013). Epistemic Representation, Informativeness and the Aim of Faithful Representation. Synthese 190 (2):219-234.
    In this paper, I take scientific models to be epistemic representations of their target systems. I define an epistemic representation to be a tool for gaining information about its target system and argue that a vehicle’s capacity to provide specific information about its target system—its informativeness—is an essential feature of this kind of representation. I draw an analogy to our ordinary notion of interpretation to show that a user’s aim of faithfully representing the target system is necessary for securing this (...)
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  20. Giovanni Boniolo, Theories and Models: Really Old Hat?
    In this paper the topic of the relations between scientific theories and scientific models is tackled by considering the former as hypothetical scientific representations and the latter as fictive scientific representations. A classification of the models is also proposed.
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  21. Giovanni Boniolo (1997). On a Unified Theory of Models and Thought Experiments in Natural Sciences. International Studies in the Philosophy of Science 11 (2):121 – 142.
    In this paper a unified theory of models and thought experiments is proposed by considering them as fictions, la Vaihinger. In order to reach this aim, the Hertzian and Botzmannian interpretation of theories as Bilder is reconsidered.
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  22. Katherine Brading & Elaine Landry (2006). Scientific Structuralism: Presentation and Representation. Philosophy of Science 73 (5):571-581.
    This paper explores varieties of scientific structuralism. Central to our investigation is the notion of `shared structure'. We begin with a description of mathematical structuralism and use this to point out analogies and disanalogies with scientific structuralism. Our particular focus is the semantic structuralist's attempt to use the notion of shared structure to account for the theory-world connection, this use being crucially important to both the contemporary structural empiricist and realist. We show why minimal scientific structuralism is, at the very (...)
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  23. Matthew J. Brown (2009). Models and Perspectives on Stage: Remarks on Giere's Scientific Perspectivism. Studies in History and Philosophy of Science Part A 40 (2):213-220.
    Ron Giere's recent book Scientific Perspectivism sets out an account of science that attempts to forge a via media between two popular extremes: absolutist, objectivist realism on the one hand, and social constructivism or skeptical anti-realism on the other. The key for Giere is to treat both scientific observation and scientific theories as perspectives, which are limited, partial, contingent, context-, agent- and purpose-dependent, and pluralism-friendly, while nonetheless world-oriented and modestly realist. Giere's perspectivism bears significant similarly to early writings by Paul (...)
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  24. Krzysztof Brzechczyn (2008). Polish Discussions on the Nature of Communism and Mechanisms of its Collapse: A Review Article. East European Politics and Societies 22 (4):828-855.
    The author, against the background of Communist Studies developed in Poland since World War I, reconstructs theoretical orientations that explained the communist system in that country. In this paper, the division of theoretical approaches into political, economic, and cultural ones is proposed. Each of them seeks factors responsible for nature, evolution, and final decline of the communist system in a different sphere of social life. An approach of the political type was Leszek Nowak’s theory of communism as a system of (...)
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  25. A. V. Bushkovitch (1974). Models, Theories, and Kant. Philosophy of Science 41 (1):86-88.
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  26. Jeremy Butterfield, Between Laws and Models: Some Philosophical Morals of Lagrangian Mechanics.
    I extract some philosophical morals from some aspects of Lagrangian mechanics. (A companion paper will present similar morals from Hamiltonian mechanics and Hamilton-Jacobi theory.) One main moral concerns methodology: Lagrangian mechanics provides a level of description of phenomena which has been largely ignored by philosophers, since it falls between their accustomed levels---``laws of nature'' and ``models''. Another main moral concerns ontology: the ontology of Lagrangian mechanics is both more subtle and more problematic than philosophers often realize. The treatment of Lagrangian (...)
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  27. H. G. Callaway (2014). Abduction, Competing Models and the Virtues of Hypotheses. In Lorenzo Magnani (ed.), (2014) Model-Based Reasoning in Science and Technology. Springer. 263-280.
    This paper focuses on abduction as explicit or readily formulatable inference to possible explanatory hypotheses--as contrasted with inference to conceptual innovations or abductive logic as a cycle of hypotheses, deduction of consequences and inductive testing. Inference to an explanation is often a matter of projection or extrapolation of elements of accepted theory for the solution of outstanding problems in particular domains of inquiry. I say "projections or extrapolation" of accepted theory, but I mean to point to something broader and suggest (...)
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  28. Craig Callender & Jonathan Cohen (2006). There is No Special Problem About Scientific Representation. Theoria 21 (1):67-85.
    We propose that scientific representation is a special case of a more general notion of representation, and that the relatively well worked-out and plausible theories of the latter are directly applicable to thc scientific special case. Construing scientific representation in this way makes the so-called “problem of scientific representation” look much less interesting than it has seerned to many, and suggests that some of the (hotly contested) debates in the literature are concerned with non-issues.
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  29. Jack C. Carloye (1971). An Interpretation of Scientific Models Involving Analogies. Philosophy of Science 38 (4):562-569.
    In order to account for the actual function of analogue models in extending theories to new domains, we argue that it is necessary to analyze the inference involved into a complex two dimensional form. This form must go horizontally from descriptions of entities used as a model to redescriptions of entities in the new domain, and it must go vertically from an observation language to a theoretical language having a different and exclusive logical syntax. This complex inference can only be (...)
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  30. Nancy Cartwright (2009). If No Capacities Then No Credible Worlds. But Can Models Reveal Capacities? Erkenntnis 70 (1):45 - 58.
    This paper argues that even when simple analogue models picture parallel worlds, they generally still serve as isolating tools. But there are serious obstacles that often stop them isolating in just the right way. These are obstacles that face any model that functions as a thought-experiment but they are especially pressing for economic models because of the paucity of economic principles. Because of the paucity of basic principles, economic models are rich in structural assumptions. Without these no interesting conclusions can (...)
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  31. Nancy Cartwright (1997). Models: The Blueprints for Laws. Philosophy of Science 64 (4):303.
    In this paper the claim that laws of nature are to be understood as claims about what necessarily or reliably happens is disputed. Laws can characterize what happens in a reliable way, but they do not do this easily. We do not have laws for everything occurring in the world, but only for those situations where what happens in nature is represented by a model: models are blueprints for nomological machines, which in turn give rise to laws. An example from (...)
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  32. Nancy Cartwright, Stephan Hartmann, Carl Hoefer & Luc Bovens (eds.) (2008). Nancy Cartwright's Philosophy of Science. Routledge.
    Nancy Cartwright is one of the most distinguished and influential contemporary philosophers of science. Despite the profound impact of her work, until now there has not been a systematic exposition of Cartwright's philosophy of science nor a collection of articles that contains in-depth discussions of the major themes of her philosophy. This book is devoted to a critical assessment of Cartwright's philosophy of science and contains contributions from Cartwright's champions and critics. Broken into three parts, the book begins by addressing (...)
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  33. Jordi Cat (2005). Modeling Cracks and Cracking Models: Structures, Mechanisms, Boundary Conditions, Constraints, Inconsistencies and the Proper Domains of Natural Laws. Synthese 146 (3):447 - 487.
    The emphasis on models hasn’t completely eliminated laws from scientific discourse and philosophical discussion. Instead, I want to argue that much of physics lies beyond the strict domain of laws. I shall argue that in important cases the physics, or physical understanding, does not lie either in laws or in their properties, such as universality, consistency and symmetry. I shall argue that the domain of application commonly attributed to laws is too narrow. That is, laws can still play an important, (...)
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  34. A. Charles Catania (2000). Metaphors, Models, and Mathematics in the Science of Behavior. Behavioral and Brain Sciences 23 (1):94-95.
    Metaphors and models involve correspondences between events in separate domains. They differ in the form and precision of how the correspondences are expressed. Examples include correspondences between phylogenic and ontogenic selection, and wave and particle metaphors of the mathematics of quantum physics. An implication is that the target article's metaphors of resistance to change may have heuristic advantages over those of momentum.
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  35. C. Cazeaux (2012). Beyond Mimesis and Convention: Representation in Art and Science. British Journal of Aesthetics 52 (2):211-216.
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  36. Anjan Chakravartty (2001). The Semantic or Model-Theoretic View of Theories and Scientific Realism. Synthese 127 (3):325 - 345.
    The semantic view of theoriesis one according to which theoriesare construed as models of their linguisticformulations. The implications of thisview for scientific realism have been little discussed. Contraryto the suggestion of various champions of the semantic view,it is argued that this approach does not makesupport for a plausible scientific realism anyless problematic than it might otherwise be.Though a degree of independence of theory fromlanguage may ensure safety frompitfalls associated with logical empiricism, realism cannot be entertained unless models or (abstractedand/or idealized) (...)
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  37. M. Chirimuuta (2013). Extending, Changing, and Explaining the Brain. Biology and Philosophy 28 (4):613-638.
    This paper addresses concerns raised recently by Datteri (Biol Philos 24:301–324, 2009) and Craver (Philos Sci 77(5):840–851, 2010) about the use of brain-extending prosthetics in experimental neuroscience. Since the operation of the implant induces plastic changes in neural circuits, it is reasonable to worry that operational knowledge of the hybrid system will not be an accurate basis for generalisation when modelling the unextended brain. I argue, however, that Datteri’s no-plasticity constraint unwittingly rules out numerous experimental paradigms in behavioural and systems (...)
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  38. Gabriele Contessa, Scientific Representation, Smilarity and Prediction.
    In this paper, I consider how different versions of the similarity account of scientific representation might apply to a simple case of scientific representation, in which a model is used to predict the behaviour of a system. I will argue that the similarity account is potentially susceptible to the problem of accidental similarities between the model and the system and that, if it is to avoid this problem, one has to specify which similarities have to hold between a model and (...)
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  39. Gabriele Contessa, Disentangling Scientific Representation.
    The main aim of this paper is to disentangle three senses in which we can say that a model represents a system—denotation epistemic representation, and successful epistemic representation--and to individuate what questions arise from each sense of the notion of representation as used in this context. Also, I argue that a model is an epistemic representation of a system only if a user adopts a general interpretation of the model in terms of a system. In the process, I hope to (...)
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  40. Gabriele Contessa (2011). Scientific Models and Representation. In Steven French & Juha Saatsi (eds.), The Continuum Companion to the Philosophy of Science. Continuum Press. 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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  41. Gabriele Contessa (2010). Introduction. Synthese 172 (2):193-195.
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  42. Gabriele Contessa (2010). Scientific Models and Fictional Objects. Synthese 172 (2):215 - 229.
    In this paper, I distinguish scientific models in three kinds on the basis of their ontological status—material models, mathematical models and fictional models, and develop and defend an account of fictional models as fictional objects—i.e. abstract objects that stand for possible concrete objects.
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  43. Gabriele Contessa (2007). Scientific Representation, Interpretation, and Surrogative Reasoning. Philosophy of Science 74 (1):48-68.
    In this paper, I develop Mauricio Suárez’s distinction between denotation, epistemic representation, and faithful epistemic representation. I then outline an interpretational account of epistemic representation, according to which a vehicle represents a target for a certain user if and only if the user adopts an interpretation of the vehicle in terms of the target, which would allow them to perform valid (but not necessarily sound) surrogative inferences from the model to the system. The main difference between the interpretational conception I (...)
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  44. Gabriele Contessa (2007). Representing Reality: The Ontology of Scientific Models and Their Representational Function. Dissertation, University of London
    Today most philosophers of science believe that models play a central role in science and that one of the main functions of scientific models is to represent systems in the world. Despite much talk of models and representation, however, it is not yet clear what representation in this context amounts to nor what conditions a certain model needs to meet in order to be a representation of a certain system. In this thesis, I address these two questions. First, I will (...)
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  45. Gabriele Contessa (2006). Scientific Models, Partial Structures and the New Received View of Theories. [REVIEW] Studies in History and Philosophy of Science Part A 37 (2):370-377.
  46. Gregory Cooper (1996). Theoretical Modeling and Biological Laws. Philosophy of Science 63 (3):35.
    Recent controversy over the existence of biological laws raises questions about the cognitive aims of theoretical modeling in that science. If there are no laws for successful theoretical models to approximate, then what is it that successful theories do? One response is to regard theoretical models as tools. But this instrumental reading cannot accommodate the explanatory role that theories are supposed to play. Yet accommodating the explanatory function, as articulated by Brandon and Sober for example, seems to involve us once (...)
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  47. Carl F. Craver (2006). When Mechanistic Models Explain. Synthese 153 (3):355-376.
    Not all models are explanatory. Some models are data summaries. Some models sketch explanations but leave crucial details unspecified or hidden behind filler terms. Some models are used to conjecture a how-possibly explanation without regard to whether it is a how-actually explanation. I use the Hodgkin and Huxley model of the action potential to illustrate these ways that models can be useful without explaining. I then use the subsequent development of the explanation of the action potential to show what is (...)
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  48. Newton da Costa & Steven French (2000). Models, Theories, and Structures: Thirty Years On. Philosophy of Science 67 (3):127.
    Thirty years after the conference that gave rise to The Structure of Scientific Theories, there is renewed interest in the nature of theories and models. However, certain crucial issues from thirty years ago are reprised in current discussions; specifically: whether the diversity of models in the science can be captured by some unitary account; and whether the temporal dimension of scientific practice can be represented by such an account. After reviewing recent developments we suggest that these issues can be accommodated (...)
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  49. Lindley Darden (2007). Mechanisms and Models. In David L. Hull & Michael Ruse (eds.), The Cambridge Companion to the Philosophy of Biology. Cambridge University Press.
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  50. Michael A. Day (1990). The No-Slip Condition of Fluid Dynamics. Erkenntnis 33 (3):285 - 296.
    In many applications of physics, boundary conditions have an essential role. The purpose of this paper is to examine from both a historical and philosophical perspective one such boundary condition, namely, the no-slip condition of fluid dynamics. The historical perspective is based on the works of George Stokes and serves as the foundation for the philosophical perspective. It is seen that historically the acceptance of the no-slip condition was problematic. Philosophically, the no-slip condition is interesting since the use of the (...)
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