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  1. Robert J. Ackermann (1966). Confirmatory Models of Theories. British Journal for the Philosophy of Science 16 (64):312-326.
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  2. Matthias Adam (2004). Why Worry About Theory-Dependence? Circularity, Minimal Empiricality and Reliability. International Studies in the Philosophy of Science 18 (2 & 3):117 – 132.
    It is a widely shared view among philosophers of science that the theory-dependence (or theory-ladenness) of observations is worrying, because it can bias empirical tests in favour of the tested theories. These doubts are taken to be dispelled if an observation is influenced by a theory independent of the tested theory and thus circularity is avoided, while (partially) circular tests are taken to require special attention. Contrary to this consensus, it is argued that the epistemic value of theory-dependent tests has (...)
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  3. Matthias Adam, Martin Carrier & Torsten Wilholt (2006). How to Serve the Customer and Still Be Truthful: Methodological Characteristics of Applied Research. Science and Public Policy 33 (6):435-444.
    Transdisciplinarity includes the assumption that within new institutional settings, scientific research becomes more closely responsive to practical problems and user needs and is therefore often subject to considerable application pressure. This raises the question whether transdisciplinarity affects the epistemic standards and the fruitfulness of research. Case studies show how user-orientation and epistemic innovativeness can be combined. While the modeling involved in all cases under consideration was local and focused primarily on features of immediate practical relevance, it was informed by theoretical (...)
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  4. Michael D. Alder (1973). On Theories. Philosophy of Science 40 (2):213-226.
    An axiom set is given which purports to formalize the notion of a "theory involving measurement." The abstract objects satisfying these axioms are examined, and some candidates for measures of complexity are considered. This framework allows us to discuss some forms of a degree of confirmation. Both "complexity" and "degree of confirmation" appear to be intimately bound up with geometrical aspects of these "theories" which derive from measurement considerations, suggesting that the concepts may be inapplicable to more "general theories." The (...)
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  5. Peter Alexander (1958). Theory-Construction and Theory-Testing. British Journal for the Philosophy of Science 9 (33):29-38.
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  6. Wolfgang Balzer & Joseph D. Sneed (1978). Generalized Net Structures of Empirical Theories. II. Studia Logica 37 (2):167 - 194.
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  7. Wolfgang Balzer & Joseph D. Sneed (1977). Generalized Net Structures of Empirical Theories. I. Studia Logica 36 (3):195 - 211.
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  8. Trevor J. M. Bench-Capon & Giovanni Sartor (2003). A Model of Legal Reasoning with Cases Incorporating Theories and Values. Artificial Intelligence 150 (1-2):97-143.
    Reasoning with cases has been a primary focus of those working in AI and law who have attempted to model legal reasoning. In this paper we put forward a formal model of reasoning with cases which captures many of the insights from that previous work. We begin by stating our view of reasoning with cases as a process of constructing, evaluating and applying a theory. Central to our model is a view of the relationship between cases, rules based on cases, (...)
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  9. Otávio Bueno, Steven French & James Ladyman (2012). Models and Structures: Phenomenological and Partial. Studies in History and Philosophy of Science Part B 43 (1):43-46.
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  10. Otávio Bueno & Décio Krause (2010). Scientific Theories, Models, and the Semantic Approach. Principia 11 (2):187-201.
    According to the semantic view, a theory is characterized by a class of models. In this paper, we examine critically some of the assumptions that underlie this approach. First, we recall that models are models of something. Thus we cannot leave completely aside the axiomatization of the theories under consideration, nor can we ignore the metamathematics used to elaborate these models, for changes in the metamathematics often impose restrictions on the resulting models. Second, based on a parallel between van Fraassen’s (...)
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  11. 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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  12. Luiz Henrique de A. Dutra (2008). Models and the Semantic and Pragmatic Views of Theories. Principia 12 (1):73-86.
    http://dx.doi.org/10.5007/1808-1711.2008v12n1p73 This paper aims at discussing from the point of view of a pragmatic stance the concept of model as an abstract replica. According to this view, scientific models are abstract structures different from set-theoretic models. The view of models argued for here stems from the conceptions of some important philosophers of science who elaborated on the notion of model, such as Suppe, Cartwright, Hempel, and Nagel. Differently from all those authors, however, the conception of model argued for here is (...)
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  13. Bas C. Van Fraassen & Pérez Ransanz (1985). On the Question of Identification of a Scientific Theory (A Reply to "Van Fraassen's Concept of Empirical Theory" by Pérez Ransanz). Crítica 17 (51):21 - 29.
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  14. Roman Frigg & Stephan Hartmann (2005). Scientific Models. In Sahotra Sarkar et al (ed.), The Philosophy of Science: An Encyclopedia, Vol. 2. Routledge.
    Models are of central importance in many scientific contexts. The roles the MIT bag model of the nucleon, the billiard ball model of a gas, the Bohr model of the atom, the Gaussian-chain model of a polymer, the Lorenz model of the atmosphere, the Lotka- Volterra model of predator-prey interaction, agent-based and evolutionary models of social interaction, or general equilibrium models of markets play in their respective domains are cases in point.
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  15. Roman Frigg, Stephan Hartmann & Cyrille Imbert (2009). Models and Simluations. Synthese 169 (3).
    Special issue. With contributions by Anouk Barberouse, Sarah Francescelli and Cyrille Imbert, Robert Batterman, Roman Frigg and Julian Reiss, Axel Gelfert, Till Grüne-Yanoff, Paul Humphreys, James Mattingly and Walter Warwick, Matthew Parker, Wendy Parker, Dirk Schlimm, and Eric Winsberg.
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  16. Axel Gelfert (2013). Strategies of Model-Building in Condensed Matter Physics: Trade-Offs as a Demarcation Criterion Between Physics and Biology? 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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  17. Axel Gelfert (2011). Model-Based Representation in Scientific Practice: New Perspectives. Studies in History and Philosophy of Science 42 (2):251-252.
    Editorial introduction to special issue on 'Model-based representation in scientific practice'.
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  18. Michel Ghins (2012). Scientific Representation and Realism. Principia 15 (3):461-474.
    After a brief presentation of what I take to be the representational démarche in science, I stress the fundamental role of true judgements in model construction. The success and correctness of a representation rests on the truth of judgements which attribute properties to real targeted entities, called “ontic judgements”. I then present what van Fraassen calls “the Loss of Reality objection”. After criticizing his dissolution of the objection, I offer an alternative way of answering the Loss of Reality objection by (...)
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  19. Ronald N. Giere (1984). Causal Models with Frequency Dependence. Journal of Philosophy 81 (7):384-391.
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  20. Patrick Grim & Nicholas Rescher (2013). How Modeling Can Go Wrong. Philosophy and Technology 26 (1):75-80.
    Modeling and simulation clearly have an upside. My discussion here will deal with the inevitable downside of modeling — the sort of things that can go wrong. It will set out a taxonomy for the pathology of models — a catalogue of the various ways in which model contrivance can go awry. In the course of that discussion, I also call on some of my past experience with models and their vulnerabilities.
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  21. Till Grüne-Yanoff, Appraising Non-Representational Models.
    Many scientific models are non-representational in that they refer to merely possible processes, background conditions and results. The paper shows how such non-representational models can be appraised, beyond the weak role that they might play as heuristic tools. Using conceptual distinctions from the discussion of how-possibly explanations, six types of models are distinguished by their modal qualities of their background conditions, model processes and model results. For each of these types, an actual model example – drawn from economics, biology, psychology (...)
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  22. Joseph Y. Halpern & Christopher Hitchcock (2013). Compact Representations of Extended Causal Models. Cognitive Science 37 (6):986-1010.
    Judea Pearl (2000) was the first to propose a definition of actual causation using causal models. A number of authors have suggested that an adequate account of actual causation must appeal not only to causal structure but also to considerations of normality. In Halpern and Hitchcock (2011), we offer a definition of actual causation using extended causal models, which include information about both causal structure and normality. Extended causal models are potentially very complex. In this study, we show how it (...)
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  23. Stephan Hartmann (1996). The World as a Process: Simulations in the Natural and Social Sciences. In Rainer Hegselmann (ed.), Modelling and Simulation in the Social Sciences from the Philosophy of Science Point of View.
    Simulation techniques, especially those implemented on a computer, are frequently employed in natural as well as in social sciences with considerable success. There is mounting evidence that the "model-building era" (J. Niehans) that dominated the theoretical activities of the sciences for a long time is about to be succeeded or at least lastingly supplemented by the "simulation era". But what exactly are models? What is a simulation and what is the difference and the relation between a model and a simulation? (...)
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  24. Stephan Hartmann (1995). Modelle Und Forschungsdynamik: Strategien der Zeitgenössischen Physik. Praxis der Naturwissenschaften - Physik 1:33-41.
    An Beispielen aus der Entwicklung der Elementarteilchenphysik wird aufgezeigt, welche Rolle Modelle im Entstehungsprozess einer physikalischen Theorie spielen.
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  25. Stephan Hartmann (1995). Simulation. In Jürgen Mittelstrass (ed.), Enzyklopädie Philosophie und Wissenschaftstheorie, Vol. 3. Metzler.
    Simulation (von lat. simulare, engl. simulation, franz. simulation, ital. simulazione), Bezeichnung für die Nachahmung eines Prozesses durch einen anderen Prozeß. Beide Prozesse laufen auf einem bestimmten System ab. Simuliertes u. simulierendes System (der Simulator in der Kybernetik) können dabei auf gleichen oder unterschiedlichen Substraten realisiert sein.
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  26. Stephan Hartmann & Roman Frigg (2006). Models in Science. In Ed Zalta (ed.), The Stanford Encyclopedia of Philosophy. Stanford.
    Models are of central importance in many scientific contexts. The centrality of models such as the billiard ball model of a gas, the Bohr model of the atom, the MIT bag model of the nucleon, the Gaussian-chain model of a polymer, the Lorenz model of the atmosphere, the Lotka-Volterra model of predator-prey interaction, the double helix model of DNA, agent-based and evolutionary models in the social sciences, or general equilibrium models of markets in their respective domains are cases in point. (...)
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  27. Stephan Hartmann & Roman Frigg (2005). Scientific Models. In SarkarSahotra (ed.), The Philosophy of Science: An Encyclopedia, Vol. 2. Routledge.
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  28. Richard Healey (2010). Science Without Representation. Analysis 70 (3):536-547.
    I think van Fraassen is right to see the development of quantum mechanics as a turning point for physical science with a profound moral for philosophy, and not just for the philosophy of science. But the moral is not that even a completely successful physical theory may fail to account for the appearances by showing how they arise within the reality it represents. The moral is more radical: it is that a physical theory – even a fundamental theory – may (...)
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  29. Patrick A. Heelan (2003). Husserl, Lonergan, and Paradoxes of Measurement. Journal of Macrodynamic Analysis 3.
    My scientific field is theoretical physics. My philosophical orientation is phenomenology, especially hermeneutical phenomenology, as modified and extended under the influence of Bernard Lonergan's cognitional theory. In fact, I was already deeply under the influence of Bernard Lonergan's workbefore I went to Louvain/Leuven to study phenomenology as a propaedeutic to my preparation in the philosophy of science. The specific topic of this paper is one close to the center of Philip's interest, namely, to articulate the right balance among theory, experiment, (...)
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  30. Andoni Ibarra & Thomas Mormann (1997). Theories as Representations. Poznan Studies in the Philosophy of the Sciences and the Humanities 61:39 - 87.
    In this paper we argue for the thesis that theories are to be considered as representations. The term "representation" is used in a sense inspired by its mathematical meaning. Our main thesis asserts that theories of empirical theories can be conceived as geometrical representations. This idea may be traced back to Galileo. The geometric format of empirical theories should not be simply considered as a clever device for displaying a theory. Rather, the geometrical character deeply influences the theory s ontology. (...)
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  31. Nicholaos Jones (2013). Don't Blame the Idealizations. Journal for General Philosophy of Science 44 (1):85-100.
    Idealizing conditions are scapegoats for scientific hypotheses, too often blamed for falsehood better attributed to less obvious sources. But while the tendency to blame idealizations is common among both philosophers of science and scientists themselves, the blame is misplaced. Attention to the nature of idealizing conditions, the content of idealized hypotheses, and scientists’ attitudes toward those hypotheses shows that idealizing conditions are blameless when hypotheses misrepresent. These conditions help to determine the content of idealized hypotheses, and they do so in (...)
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  32. John R. Josephson (1998). Abduction-Prediction Model of Scientific Inference Reflected in a Prototype System for Model-Based Diagnosis. Philosophica 61.
    This paper describes in some detail a pattern of justification which seems to be part of common sense logic and also part of the logic of scientific investigations. Calling this pattern “abduction,” the paper lays out an “abduction-prediction” model of scientific inference as an update to the traditional hypothetico-deductive model. According to this newer model, scientific theories receive their claims for acceptance and belief from the abductive arguments that support them, and the processes of scientific discovery aim to develop theories (...)
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  33. Tarja Knuuttila (2011). Modelling and Representing: An Artefactual Approach to Model-Based Representation. Studies in History and Philosophy of Science 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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  34. Władysław Krajewski (1997). Ideal Objects as Models in Science. International Studies in the Philosophy of Science 11 (2):185-190.
    Abstract Three main concepts of model in science are distinguished: (1) semantical model of a theory; (2) real model of another real thing; (3) mathematical model of a real thing. The last concept is the most important for the empirical sciences. The mathematical model is not identical with a theory: it is an ideal object which is directly described by the theory. We have here an intermediate level between reality and theory.
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  35. Leonardo Lana de Carvalho, Franck Varenne & Elayne de Moura Bragra (2014). Ontologias para a Modelagem Multiagente de Sistemas Complexos em Ciências Cognitivas. Ciências and Cognição 19 (1):58-75.
    Cognitive sciences as an interdisciplinary field, involving scientific disciplines (such as computer science, linguistics, psychology, neuroscience, economics, etc.), philosophical disciplines (philosophy of language, philosophy of mind, analytic philosophy, etc.) and engineering (notably knowledge engineering), have a vast theoretical and practical content, some even conflicting. In this interdisciplinary context and on computational modeling, ontologies play a crucial role in communication between disciplines and also in a process of innovation of theories, models and experiments in cognitive sciences. We propose a model for (...)
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  36. Axel Leijonhufvud (1997). Models and Theories. Journal of Economic Methodology 4 (2):193-198.
    Economic theories are systems of beliefs about the world. Models formalize parts or aspects of theories but leave much of their content out. An example of a component of theories not contained in models are the instructions for how to proceed when a model fails (in Lakatos? terms the ?positive heuristic?). Mathematization gives precision of statement but not of empirical reference. The emphasis on explicit models as the main vehicle for journal communication among economists is questioned. The tendency for top (...)
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  37. Sabina Leonelli & Rachel Ankeny (2011). What’s so Special About Model Organisms? Studies in History and Philosophy of Science 42 (2):313-323.
    This paper aims to identify the key characteristics of model organisms that make them a specific type of model within the contemporary life sciences: in particular, we argue that the term “model organism” does not apply to all organisms used for the purposes of experimental research. We explore the differences between experimental and model organisms in terms of their material and epistemic features, and argue that it is essential to distinguish between their representational scope and representational target. We also examine (...)
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  38. Caterina Marchionni (2013). Model-Based Explanation in the Social Sciences: Modeling Kinship Terminologies and Romantic Networks. Perspectives on Science 21 (2):175-180.
    Read argues that modeling cultural idea systems serves to make explicit the cultural rules through which "cultural idea systems" frame behaviors that are culturally meaningful. Because cultural rules are typically "invisible" to us, one of the anthropologists' tasks is to elicit these rules, make them explicit and then use them to build explanations for patterns in cultural phenomena. The main example of Read's approach to cultural idea systems is the formal modeling of kinship terminologies. I reconstruct Read's modeling strategy as (...)
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  39. Caterina Marchionni (2006). Contrastive Explanation and Unrealistic Models: The Case of the New Economic Geography. Journal of Economic Methodology 13 (4):425-446.
    The contrastive approach to explanation is employed to shed light on the issue of the unrealisticness of models and their assumptions in economics. If we take explanations to be answers to contrastive questions of the form, then unrealistic elements such as omissions and idealizations are (at least partly) dependent on the selected contrast. These contrast?dependent assumptions are shown to serve the function of fixing the shared causal background between the fact and the foil. It is argued that looking at the (...)
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  40. Mary S. Morgan (2005). Experiments Versus Models: New Phenomena, Inference and Surprise. Journal of Economic Methodology 12 (2):317-329.
    A comparison of models and experiments supports the argument that although both function as mediators and can be understood to work in an experimental mode, experiments offer greater epistemic power than models as a means to investigate the economic world. This outcome rests on the distinction that whereas experiments are versions of the real world captured within an artificial laboratory environment, models are artificial worlds built to represent the real world. This difference in ontology has epistemic consequences: experiments have greater (...)
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  41. Mary S. Morgan & Till Grüne-Yanoff (2013). Modeling Practices in the Social and Human Sciences. An Interdisciplinary Exchange. Perspectives on Science 21 (2):143-156.
    Philosophers of science studying scientific practice often consider it a methodological requirement that their conceptualization of "model" closely connects with the understanding and use of models by practicing scientists. Occasionally, this connection has been explicitly made (Hutten 1954, Suppes 1961, Morgan and Morrison 1999, Bailer-Jones 2002, Lehtinen and Kuorikoski 2007, Kuorikoski 2007, Morgan 2012a). These studies have been dominated by a focus on the—relatively similar forms of—mathematical models in physics and economics. Yet it has become increasingly evident that the way (...)
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  42. Margaret Morrison (2011). One Phenomenon, Many Models: Inconsistency and Complementarity. Studies in History and Philosophy of Science 42 (2):342-351.
    The paper examines philosophical issues that arise in contexts where one has many different models for treating the same system. I show why in some cases this appears relatively unproblematic (models of turbulence) while others represent genuine difficulties when attempting to interpret the information that models provide (nuclear models). What the examples show is that while complementary models needn’t be a hindrance to knowledge acquisition, the kind of inconsistency present in nuclear cases is, since it is indicative of a lack (...)
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  43. Alan Musgrave (1983). Theory and Observation: Nola Versus Popper. Philosophica 31.
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  44. Ernest Nagel (ed.) (1971). Observation and Theory in Science. Baltimore,Johns Hopkins Press.
  45. Leszek Nowak (2012). Models of Scientific Research (1976). Poznan Studies in the Philosophy of the Sciences and the Humanities 100 (1):67-74.
    According to the commonsensical model of educating researchers, young researchers must first acquire the knowledge achieved thus far and then solve new problems by developing applications of the accepted theory. This model, which presupposes a positivist theory of science, is incapable of explaining why the major breakthroughs in science have been carried out by young researchers. On the idealizational view of science, it becomes clear that commonsensical model must be rejected and replaced with an alternative, according to which the primary (...)
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  46. Wendy S. Parker (2013). Getting (Even More) Serious About Similarity. Biology and Philosophy:1-10.
    This paper critically examines Weisberg’s weighted feature matching account of model-world similarity. A number of concerns are raised, including that Weisberg provides an account of what underlies scientific judgments of relative similarity, when what is desired is an account of the sorts of model-target similarities that are necessary or sufficient for achieving particular types of modeling goal. Other concerns relate to the details of the account, in particular to the content of feature sets, the nature of shared features and the (...)
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  47. R. B. Patel & B. P. Singh (eds.) (2010). International Conference on Methods and Models in Science and Technology: Icm2st-10, 25-26 December 2010, Chandrigarh, India. [REVIEW] American Institute of Physics.
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  48. Miguel Patiri (2012). Fictions in Science: Philosophical Essays on Modeling and Idealization. Revista Latinoamericana de Filosofía 38 (2):277-280.
    En este trabajo me propongo desarrollar un estudio crítico de la concepción mecanicista de la explicación científica. En primer lugar, argumento que la caracterización mecanicista de los modelos fenoménicos (no explicativos) es inadecuada, pues no ofrece un análisis aceptable de los conceptos de modelo científico y similitud, que son fundamentales para la propuesta. En segundo lugar, sostengo que la caracterización de los modelos mecanicistas (explicativos) es igualmente inadecuada, pues los análisis disponibles de la relación explicativa de relevancia constitutiva implican una (...)
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  49. Isabelle Peschard (2011). Modeling and Experimenting. In Paul Humphreys & Cyrille Imbert (eds.), Models, Simulations, and Representations. Routledge.
    Experimental activity is traditionally identified with testing the empirical implications or numerical simulations of models against data. In critical reaction to the ‘tribunal view’ on experiments, this essay will show the constructive contribution of experimental activity to the processes of modeling and simulating. Based on the analysis of a case in fluid mechanics, it will focus specifically on two aspects. The first is the controversial specification of the conditions in which the data are to be obtained. The second is conceptual (...)
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  50. Christopher Pincock (2011). Fictions in Science: Philosophical Essays on Modeling and Idealization. International Studies in the Philosophy of Science 25 (2):196 - 199.
    International Studies in the Philosophy of Science, Volume 25, Issue 2, Page 196-199, June 2011.
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