Search results for 'scientific models' (try it on Scholar)

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  1. Gabriele Contessa (2010). Scientific Models and Fictional Objects. Synthese 172 (2):215 - 229.score: 90.0
    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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  2. S. Ducheyne (2008). Towards an Ontology of Scientific Models. Metaphysica 9 (1):119-127.score: 90.0
    Scientific models occupy centre stage in scientific practice. Correspondingly, in recent literature in the philosophy of science, scientific models have been a focus of research. However, little attention has been paid so far to the ontology of scientific models. In this essay, I attempt to clarify the issues involved in formulating an informatively rich ontology of scientific models. Although no full-blown theory—containing all ontological issues involved—is provided, I make several distinctions and (...)
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  3. Ronald Giere (2010). An Agent-Based Conception of Models and Scientific Representation. Synthese 172 (2):269–281.score: 75.0
    I argue for an intentional conception of representation in science that requires bringing scientific agents and their intentions into the picture. So the formula is: Agents (1) intend; (2) to use model, M; (3) to represent a part of the world, W; (4) for some purpose, P. This conception legitimates using similarity as the basic relationship between models and the world. Moreover, since just about anything can be used to represent anything else, there can be no unified ontology (...)
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  4. Alisa Bokulich (2011). How Scientific Models Can Explain. Synthese 180 (1):33 - 45.score: 66.0
    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 (...)
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  5. Rasmus Grønfeldt Winther (2006). On the Dangers of Making Scientific Models Ontologically Independent: Taking Richard Levins' Warnings Seriously. Biology and Philosophy 21 (5):703-724.score: 66.0
    Levins and Lewontin have contributed significantly to our philosophical understanding of the structures, processes, and purposes of biological mathematical theorizing and modeling. Here I explore their separate and joint pleas to avoid making abstract and ideal scientific models ontologically independent by confusing or conflating our scientific models and the world. I differentiate two views of theorizing and modeling, orthodox and dialectical, in order to examine Levins and Lewontin’s, among others, advocacy of the latter view. I compare (...)
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  6. Demetris P. Portides (2005). A Theory of Scientific Model Construction: The Conceptual Process of Abstraction and Concretisation. [REVIEW] Foundations of Science 10 (1):67-88.score: 66.0
    The process of abstraction and concretisation is a label used for an explicative theory of scientific model-construction. In scientific theorising this process enters at various levels. We could identify two principal levels of abstraction that are useful to our understanding of theory-application. The first level is that of selecting a small number of variables and parameters abstracted from the universe of discourse and used to characterise the general laws of a theory. In classical mechanics, for example, we select (...)
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  7. David Ludwig (forthcoming). Mediating Objects. Scientific and Public Functions of Models in Nineteenth-Century Biology. History and Philosophy of the Life Sciences.score: 66.0
  8. Axel Gelfert (2011). Mathematical Formalisms in Scientific Practice: From Denotation to Model-Based Representation. Studies in History and Philosophy of Science 42 (2):272-286.score: 63.0
    The present paper argues that ‘mature mathematical formalisms’ play a central role in achieving representation via scientific models. A close discussion of two contemporary accounts of how mathematical models apply—the DDI account (according to which representation depends on the successful interplay of denotation, demonstration and interpretation) and the ‘matching model’ account—reveals shortcomings of each, which, it is argued, suggests that scientific representation may be ineliminably heterogeneous in character. In order to achieve a degree of unification that (...)
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  9. Daniela M. Bailer-Jones (2003). When Scientific Models Represent. International Studies in the Philosophy of Science 17 (1):59 – 74.score: 60.0
    Scientific models represent aspects of the empirical world. I explore to what extent this representational relationship, given the specific properties of models, can be analysed in terms of propositions to which truth or falsity can be attributed. For example, models frequently entail false propositions despite the fact that they are intended to say something "truthful" about phenomena. I argue that the representational relationship is constituted by model users "agreeing" on the function of a model, on the (...)
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  10. Gabriele Contessa (2007). Representing Reality: The Ontology of Scientific Models and Their Representational Function. Dissertation, University of Londonscore: 60.0
    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 (...)
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  11. Ronald N. Giere, Why Scientific Models Are Not Works of Fiction.score: 60.0
    The usual question, “Are models fictions?” is replaced by the question, “Should scientific models be regarded as works of fiction?” This makes it clear that the issue is not one of definition but of interpretation. First one must distinguish between the ontology of scientific models and their function in the practice of science. Theoretical models and works of fiction are ontologically on a par, their both being creations of human imagination. It is their differing (...)
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  12. Demetris P. Portides (2005). Scientific Models and the Semantic View of Scientific Theories. Philosophy of Science 72 (5):1287-1298.score: 60.0
    I argue against the conception of scientific models advocated by the proponents of the Semantic View of scientific theories. Part of the paper is devoted to clarifying the important features of the scientific modeling view that the Semantic conception entails. The liquid drop model of nuclear structure is analyzed in conjunction with the particular auxiliary hypothesis that is the guiding force behind its construction and it is argued that it does not meet the necessary features to (...)
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  13. Daniela M. Bailer-Jones (2002). Scientists' Thoughts on Scientific Models. Perspectives on Science 10 (3):275-301.score: 60.0
    : 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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  14. Tarja Knuuttila (2011). Scientific Models in Philosophy of Science. International Studies in the Philosophy of Science 24 (4):437-440.score: 60.0
    Scientists have used models for hundreds of years as a means of describing phenomena and as a basis for further analogy. In Scientific Models in Philosophy of Science, Daniela Bailer-Jones assembles an original and comprehensive philosophical analysis of how models have been used and interpreted in both historical and contemporary contexts. Bailer-Jones delineates the many forms models can take (ranging from equations to animals; from physical objects to theoretical constructs), and how they are put to (...)
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  15. Christopher D. Green (2001). Scientific Models, Connectionist Networks, and Cognitive Science. .score: 60.0
    The employment of a particular class of computer programs known as "connectionist networks" to model mental processes is a widespread approach to research in cognitive science these days. Little has been written, however, on the precise connection that is thought to hold between such programs and actual in vivo cognitive processes such that the former can be said to "model" the latter in a scientific sense. What is more, this relation can be shown to be problematic. In this paper (...)
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  16. Nicola Angius & Guglielmo Tamburrini (2011). Scientific Theories of Computational Systems in Model Checking. Minds and Machines 21 (2):323-336.score: 60.0
    Model checking, a prominent formal method used to predict and explain the behaviour of software and hardware systems, is examined on the basis of reflective work in the philosophy of science concerning the ontology of scientific theories and model-based reasoning. The empirical theories of computational systems that model checking techniques enable one to build are identified, in the light of the semantic conception of scientific theories, with families of models that are interconnected by simulation relations. And the (...)
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  17. Elisabet Sahtouris (2006). Why True Globalization Depends on New Scientific Models. World Futures 62 (1 & 2):17 – 27.score: 60.0
    Ervin Laszlo's Science and the Akashic Field is vital to our transition from a long epoch of empire building - of the drive to control Earth's resources by fierce competition in a situation of perceived scarcity - to a future of truly cooperative global family. Laszlo's universe is a far cry from the one Western science has taught us and compatible with my own views as a "post-Darwinian" evolution biologist. In fact, no small number of Western scientists today have defected (...)
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  18. Pericle Salvini, Edoardo Datteri, Cecilia Laschi & Paolo Dario (2008). Scientific Models and Ethical Issues in Hybrid Bionic Systems Research. AI and Society 22 (3):431-448.score: 60.0
    Research on hybrid bionic systems (HBSs) is still in its infancy but promising results have already been achieved in laboratories. Experiments on humans and animals show that artificial devices can be controlled by neural signals. These results suggest that HBS technologies can be employed to restore sensorimotor functionalities in disabled and elderly people. At the same time, HBS research raises ethical concerns related to possible exogenous and endogenous limitations to human autonomy and freedom. The analysis of these concerns requires reflecting (...)
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  19. Roman Frigg & Stephan Hartmann (2005). Scientific Models. In Sahotra Sarkar et al (ed.), The Philosophy of Science: An Encyclopedia, Vol. 2. Routledge.score: 57.0
    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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  20. Axel Gelfert (2011). Scientific Models, Simulation, and the Experimenter's Regress. In Paul Humphreys & Cyrille Imbert (eds.), Models, Simulations, and Representations. Routledge.score: 57.0
    According to the "experimenter's regress", disputes about the validity of experimental results cannot be closed by objective facts because no conclusive criteria other than the outcome of the experiment itself exist for deciding whether the experimental apparatus was functioning properly or not. Given the frequent characterization of simulations as "computer experiments", one might worry that an analogous regress arises for computer simulations. The present paper analyzes the most likely scenarios where one might expect such a "simulationist's regress" to surface, and, (...)
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  21. Adam Toon (2012). Models as Make-Believe: Imagination, Fiction, and Scientific Representation. Palgrave Macmillan.score: 55.0
    Models as Make-Believe offers a new approach to scientific modelling by looking to an unlikely source of inspiration: the dolls and toy trucks of children's games of make-believe.
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  22. Décio Krause, Jonas Arenhart & Fernando Moraes (2011). Axiomatization and Models of Scientific Theories. Foundations of Science 16 (4):363-382.score: 54.0
    In this paper we discuss two approaches to the axiomatization of scientific theories in the context of the so called semantic approach, according to which (roughly) a theory can be seen as a class of models. The two approaches are associated respectively to Suppes’ and to da Costa and Chuaqui’s works. We argue that theories can be developed both in a way more akin to the usual mathematical practice (Suppes), in an informal set theoretical environment, writing the set (...)
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  23. Jeroen Van Bouwel (forthcoming). Towards Democratic Models of Science. Exploring the Case of Scientific Pluralism. Perspectives on Science.score: 54.0
    Scientific pluralism, a normative endorsement of the plurality or multiplicity of research approaches in science, has recently been advocated by several philosophers (e.g., Kellert et al. 2006, Kitcher 2001, Longino 2013, Mitchell 2009, and Chang 2010). Comparing these accounts of scientific pluralism, one will encounter quite some variation. We want to clarify the different interpretations of scientific pluralism by showing how they incarnate different models of democracy, stipulating the desired interaction among the plurality of research approaches (...)
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  24. Anguel Stefanov (2012). Theoretical Models as Representations. Journal for General Philosophy of Science 43 (1):67-76.score: 54.0
    My aims here are, firstly, to suggest a minor amendment to R. I. G. Hughes’ DDI account of modeling, so that it could be viewed as a plausible epistemological “model” of how scientific models represent and secondly, to distinguish between two epistemological kinds of models that I call “descriptive” and “constitutive”. This aim is achieved by criticizing Michael Weisberg’s distinction between models and abstract direct representations and by following, at the same time, his own methodological approach (...)
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  25. Isabelle Peschard (2007). The Value(s) of a Story: Theories, Models and Cognitive Values. Principia 11 (2):151-169.score: 51.0
    This paper aims 1) to introduce the notion of theoretical story as a resource and source of constraint for the construction and assessment of models of phenomena; 2) to show the relevance of this notion for a better understanding of the role and nature of values in scientific activity. The reflection on the role of values and value judgments in scientific activity should be attentive, I will argue, to the distinction between models and the theoretical story (...)
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  26. Sebastian Lutz (2014). What's Right with a Syntactic Approach to Theories and Models? Erkenntnis:1-18.score: 51.0
    Syntactic approaches in the philosophy of science, which are based on formalizations in predicate logic, are often considered in principle inferior to semantic approaches, which are based on formalizations with the help of structures. To compare the two kinds of approach, I identify some ambiguities in common semantic accounts and explicate the concept of a structure in a way that avoids hidden references to a specific vocabulary. From there, I argue that contrary to common opinion (i) unintended models do (...)
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  27. Henk W. Regt (2005). Scientific Realism in Action: Molecular Models and Boltzmann's Bildtheorie. Erkenntnis 63 (2):205 - 230.score: 51.0
    This paper approaches the scientific realism question from a naturalistic perspective. On the basis of a historical case study of the work of James Clerk Maxwell and Ludwig Boltzmann on the kinetic theory of gases, it shows that scientists’ views about the epistemological status of theories and models typically interact with their scientific results. Subsequently, the implications of this result for the current realism debate are analysed. The case study supports Giere’s moderately realist view of scientific (...)
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  28. Henk W. De Regt (2005). Scientific Realism in Action: Molecular Models and Boltzmann's Bildtheorie. [REVIEW] Erkenntnis 63 (2):205-230.score: 51.0
    This paper approaches the scientific realism question from a naturalistic perspective. On the basis of a historical case study of the work of James Clerk Maxwell and Ludwig Boltzmann on the kinetic theory of gases, it shows that scientists’ views about the epistemological status of theories and models typically interact with their scientific results. Subsequently, the implications of this result for the current realism debate are analysed. The case study supports Giere’s moderately realist view of scientific (...)
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  29. Mark L. Taper, David F. Staples & Bradley B. Shepard (2008). Model Structure Adequacy Analysis: Selecting Models on the Basis of Their Ability to Answer Scientific Questions. Synthese 163 (3):357 - 370.score: 50.0
    Models carry the meaning of science. This puts a tremendous burden on the process of model selection. In general practice, models are selected on the basis of their relative goodness of fit to data penalized by model complexity. However, this may not be the most effective approach for selecting models to answer a specific scientific question because model fit is sensitive to all aspects of a model, not just those relevant to the question. Model Structural Adequacy (...)
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  30. Décio Krause & Otávio Bueno (2007). Scientific Theories, Models, and the Semantic Approach. Principia 11 (2):187-201.score: 48.0
    According to the semantic view, a theory is characterized by a class of mod- els. 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 consider- ation, 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 (...)
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  31. Jack C. Carloye (1971). An Interpretation of Scientific Models Involving Analogies. Philosophy of Science 38 (4):562-569.score: 48.0
    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 (...)
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  32. Chuang LIu, Re-Inflating the Conception of Scientific Representation.score: 48.0
    This paper argues for an anti-deflationist view of scientific representation. Our discussion begins with an analysis of the recent Callender-Cohen deflationary view on scientific representation. We then argue that there are at least two radically different ways in which a thing can be used to represent: one is purely symbolic and therefore conventional, and the other is epistemic. The failure to recognize that scientific models are epistemic vehicles rather than symbolic ones has led to the mistaken (...)
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  33. L. Taper Mark, F. Staples David & B. Shepard Bradley (2008). Model Structure Adequacy Analysis: Selecting Models on the Basis of Their Ability to Answer Scientific Questions. Synthese 163 (3).score: 48.0
    Models carry the meaning of science. This puts a tremendous burden on the process of model selection. In general practice, models are selected on the basis of their relative goodness of fit to data penalized by model complexity. However, this may not be the most effective approach for selecting models to answer a specific scientific question because model fit is sensitive to all aspects of a model, not just those relevant to the question. Model Structural Adequacy (...)
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  34. Thomas Nickles (1980). Scientific Problems: Three Empiricist Models. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1980:3 - 19.score: 48.0
    One component of a viable account of scientific inquiry is a defensible conception of scientific problems. This paper specifies some logical and conceptual requirements that an acceptable account of scientific problems must meet as well as indicating some features that a study of scientific inquiry indicates scientific problems have. On the basis of these requirements and features, three standard empiricist models of problems are examined and found wanting. Finally a constraint inclusion-model of scientific (...)
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  35. Collin Rice (2013). Moving Beyond Causes: Optimality Models and Scientific Explanation. Noûs 48 (2).score: 48.0
    A prominent approach to scientific explanation and modeling claims that for a model to provide an explanation it must accurately represent at least some of the actual causes in the event's causal history. In this paper, I argue that many optimality explanations present a serious challenge to this causal approach. I contend that many optimality models provide highly idealized equilibrium explanations that do not accurately represent the causes of their target system(s). Furthermore, in many contexts, it is in (...)
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  36. Robert Klee (2000). Problems with Formal Models of Epistemic Entrenchment as Applied to Scientific Theories. Synthese 122 (3):313 - 320.score: 48.0
    Formal models of theory contraction entered the philosophicalliterature with the prototype model by Alchourrón, Gärdenfors,and Makinson (Alchourrón et al. 1985). One influential modelinvolves theory contraction with respect to a relation calledepistemic entrenchment which orders the propositions of a theoryaccording to their relative degrees of theoretical importance.Various postulates have been suggested for characterizingepistemic entrenchment formally. I argue here that threesuggested postulates produce inappropriately bizarre results whenapplied to scientific theories. I argue that the postulates callednoncovering, continuing up, and continuing down, (...)
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  37. Robert McCauley, Enriching Philosophical Models of Cross-Scientific Relations: Incorporating Diachronic Theories.score: 48.0
    Simple Reduction and Beyond Traditional and New Wave models of reduction in science have not lacked for ambition. Philosophers have presented single models to account for the full range of interesting intertheoretic relations, for scientific progress, and for the unity of science (Nagel, 1961; Oppenheim and Putnam, 1958). Early critics attacked the logical empiricists' proposals about the character of intertheoretic connections (Feyerabend, 1962; Kuhn, 1970). New Wave reductionists have similarly argued that various intertheoretic relations fall at different (...)
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  38. Otávio Bueno & Décio Krause (2010). Scientific Theories, Models, and the Semantic Approach. Principia 11 (2):187-201.score: 48.0
    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 (...)
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  39. Miriam Solomon (1994). Multivariate Models of Scientific Change. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1994:287 - 297.score: 48.0
    Social scientists regularly make use of multivariate models to describe complex social phenomena. It is argued that this approach is useful for modelling the variety of cognitive and social factors contributing to scientific change, and superior to the integrated models of scientific change currently available. It is also argued that care needs to be taken in drawing normative conclusions: cognitive factors are not instrinsically more "rational" than social factors, nor is it likely that social factors, by (...)
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  40. Peter de Clercq (2006). Science at Court: The Eighteenth-Century Cabinet of Scientific Instruments and Models of the Dutch Stadholders. Annals of Science 45 (2):113-152.score: 48.0
    (1988). Science at court: the eighteenth-century cabinet of scientific instruments and models of the Dutch stadholders. Annals of Science: Vol. 45, No. 2, pp. 113-152.
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  41. L. P. Steffe (2014). Constructing Models of Ethical Knowledge: A Scientific Enterprise. Constructivist Foundations 9 (2):262-264.score: 48.0
    Open peer commentary on the article “Ethics: A Radical-constructivist Approach” by Andreas Quale. Upshot: The first of my two main goals in this commentary is to establish thinking of ethics as concepts rather than as non-cognitive knowledge. The second is to argue that establishing models of individuals’ ethical concepts is a scientific enterprise that is quite similar to establishing models of individuals’ mathematical concepts. To accomplish these two primary goals, I draw from my experience of working scientifically (...)
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  42. 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.score: 45.0
    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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  43. Stathis Psillos (2011). Living with the Abstract: Realism and Models. Synthese 180 (1):3 - 17.score: 45.0
    A natural way to think of models is as abstract entities. If theories employ models to represent the world, theories traffic in abstract entities much more widely than is often assumed. This kind of thought seems to create a problem for a scientific realist approach to theories. Scientific realists claim theories should be understood literally. Do they then imply (and are they committed to) the reality of abstract entities? Or are theories simply—and incurably—false (if there are (...)
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  44. 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.score: 45.0
  45. Peter Godfrey-Smith (2009). Models and Fictions in Science. Philosophical Studies 143 (1):101 - 116.score: 45.0
    Non-actual model systems discussed in scientific theories are compared to fictions in literature. This comparison may help with the understanding of similarity relations between models and real-world target systems. The ontological problems surrounding fictions in science may be particularly difficult, however. A comparison is also made to ontological problems that arise in the philosophy of mathematics.
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  46. Erich Schienke, Seth Baum, Nancy Tuana, Kenneth Davis & Klaus Keller (2011). Intrinsic Ethics Regarding Integrated Assessment Models for Climate Management. Science and Engineering Ethics 17 (3):503-523.score: 45.0
    In this essay we develop and argue for the adoption of a more comprehensive model of research ethics than is included within current conceptions of responsible conduct of research (RCR). We argue that our model, which we label the ethical dimensions of scientific research (EDSR), is a more comprehensive approach to encouraging ethically responsible scientific research compared to the currently typically adopted approach in RCR training. This essay focuses on developing a pedagogical approach that enables scientists to better (...)
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  47. Amihud Gilead (forthcoming). Pure Possibilities and Some Striking Scientific Discoveries. Foundations of Chemistry:1-15.score: 45.0
    Regardless or independent of any actuality or actualization and exempt from spatiotemporal and causal conditions, each individual possibility is pure. Actualism excludes the existence of individual pure possibilities, altogether or at least as existing independently of actual reality. In this paper, I demonstrate, on the grounds of my possibilist metaphysics—panenmentalism—how some of the most fascinating scientific discoveries in chemistry could not have been accomplished without relying on pure possibilities and the ways in which they relate to each other (for (...)
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  48. Michael Ruse (1973). The Nature of Scientific Models : Formal V Material Analogy. Philosophy of the Social Sciences 3 (1):63-80.score: 45.0
  49. Paul Humphreys (1995). Computational Science and Scientific Method. Minds and Machines 5 (4):499-512.score: 45.0
    The process of constructing mathematical models is examined and a case made that the construction process is an integral part of the justification for the model. The role of heuristics in testing and modifying models is described and some consequences for scientific methodology are drawn out. Three different ways of constructing the same model are detailed to demonstrate the claims made here.
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  50. Stephen M. Downes (2011). Scientific Models. Philosophy Compass 6 (11):757-764.score: 45.0
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