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  1. The Oxford Handbook of Philosophical Methodology.Herman Cappelen, Tamar Gendler & John Hawthorne (eds.) - 2016 - Oxford, United Kingdom: Oxford University Press.
    This is the most comprehensive book ever published on philosophical methodology. A team of thirty-eight of the world's leading philosophers present original essays on various aspects of how philosophy should be and is done. The first part is devoted to broad traditions and approaches to philosophical methodology. The entries in the second part address topics in philosophical methodology, such as intuitions, conceptual analysis, and transcendental arguments. The third part of the book is devoted to essays about the interconnections between philosophy (...)
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  • Models, Fiction and the Imagination.Arnon Levy - 2024 - In Tarja Knuuttila, Natalia Carrillo & Rami Koskinen (eds.), The Routledge Handbook of Philosophy of Scientific Modeling. Routledge.
    Science and fiction seem to lie at opposite ends of the cognitive-epistemic spectrum. The former is typically seen as the study of hard, real-world facts in a rigorous manner. The latter is treated as an instrument of play and recreation, dealing in figments of the imagination. Initial appearances notwithstanding, several central features of scientific modeling in fact suggest a close connection with the imagination and recent philosophers have developed detailed accounts of models that treat them, in one way or another, (...)
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  • Arnon Levy, Peter Godfrey-Smith (Eds.): The Scientific Imagination: Philosophical and Psychological Perspectives. [REVIEW]Michael T. Stuart - 2021 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 52 (3):493-499.
    This is a review of Arnon Levy and Peter Godfrey-Smith's book, The Scientific Imagination.
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  • Arnon Levy, Peter Godfrey-Smith (Eds.): The Scientific Imagination: Philosophical and Psychological Perspectives: Oxford University Press: Oxford 2020, 344 pp., £55.00 (hardcover), ISBN 9780190212308. [REVIEW]Michael T. Stuart - 2021 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 52 (3):493-499.
  • Image/Images: A Debate Between Philosophy and Visual Studies.Alessandro Cavazzana & Francesco Ragazzi (eds.) - 2021 - Venice: Edizioni Ca' Foscari.
    The third issue of the Journal for the Philosophy of Language, Mind and the Arts is centered on a series of questions related to the nature of images. What properties characterize them? Do they exist also in our minds? What relationship do they have with phenomena such as perception, memory, language and interpretation? The authors participating in this issue have been asked to answer these and other questions starting from and in dialogue with the two philosophical perspectives that have most (...)
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  • Typology and Natural Kinds in Evo-Devo.Ingo Brigandt - 2021 - In Nuño De La Rosa Laura & Müller Gerd (eds.), Evolutionary Developmental Biology: A Reference Guide. Springer. pp. 483-493.
    The traditional practice of establishing morphological types and investigating morphological organization has found new support from evolutionary developmental biology (evo-devo), especially with respect to the notion of body plans. Despite recurring claims that typology is at odds with evolutionary thinking, evo-devo offers mechanistic explanations of the evolutionary origin, transformation, and evolvability of morphological organization. In parallel, philosophers have developed non-essentialist conceptions of natural kinds that permit kinds to exhibit variation and undergo change. This not only facilitates a construal of species (...)
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  • Models and representation.Roman Frigg & James Nguyen - 2017 - In Magnani Lorenzo & Bertolotti Tommaso Wayne (eds.), Springer Handbook of Model-Based Science. Springer. pp. 49-102.
    Scientific discourse is rife with passages that appear to be ordinary descriptions of systems of interest in a particular discipline. Equally, the pages of textbooks and journals are filled with discussions of the properties and the behavior of those systems. Students of mechanics investigate at length the dynamical properties of a system consisting of two or three spinning spheres with homogenous mass distributions gravitationally interacting only with each other. Population biologists study the evolution of one species procreating at a constant (...)
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  • The Evolution of Complexity.Mark Bedau - 2009 - In Barberousse Anouk, Morange M. & Pradeau T. (eds.), Mapping the Future of Biology. Boston Studies in the Philosophy of Science, vol 266. Springer.
  • Models, Fictions and Artifacts.Tarja Knuuttila - 2021 - In Wenceslao J. Gonzalez (ed.), Language and Scientific Research. Springer Verlag. pp. 199-22.
    This paper discusses modeling from the artifactual perspective. The artifactual approach conceives models as erotetic devices. They are purpose-built systems of dependencies that are constrained in view of answering a pending scientific question, motivated by theoretical or empirical considerations. In treating models as artifacts, the artifactual approach is able to address the various languages of sciences that are overlooked by the traditional accounts that concentrate on the relationship of representation in an abstract and general manner. In contrast, the artifactual approach (...)
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  • Modeling Morality.Walter Veit - 2019 - In Matthieu Fontaine, Cristina Barés-Gómez, Francisco Salguero-Lamillar, Lorenzo Magnani & Ángel Nepomuceno-Fernández (eds.), Model-Based Reasoning in Science and Technology: Inferential Models for Logic, Language, Cognition and Computation. Springer Verlag. pp. 83–102.
    Unlike any other field, the science of morality has drawn attention from an extraordinarily diverse set of disciplines. An interdisciplinary research program has formed in which economists, biologists, neuroscientists, psychologists, and even philosophers have been eager to provide answers to puzzling questions raised by the existence of human morality. Models and simulations, for a variety of reasons, have played various important roles in this endeavor. Their use, however, has sometimes been deemed as useless, trivial and inadequate. The role of models (...)
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  • Scientific representation is representation-as.Frigg Roman & Nguyen James - 2016 - In Hsiang-Ke Chao & Julian Reiss (eds.), Philosophy of Science in Practice: Nancy Cartwright and the nature of scientific reasoning. Cham: Springer International Publishing. pp. 149-179.
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  • Imagination in scientific modeling.Adam Toon - 2016 - In Amy Kind (ed.), The Routledge Handbook of the Philosophy of Imagination. New York: Routledge. pp. 451-462.
    Modeling is central to scientific inquiry. It also depends heavily upon the imagination. In modeling, scientists seem to turn their attention away from the complexity of the real world to imagine a realm of perfect spheres, frictionless planes and perfect rational agents. Modeling poses many questions. What are models? How do they relate to the real world? Recently, a number of philosophers have addressed these questions by focusing on the role of the imagination in modeling. Some have also drawn parallels (...)
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  • Scientific Discovery Through Fictionally Modelling Reality.Fiora Salis - 2018 - Topoi 39 (4):927-937.
    How do scientific models represent in a way that enables us to discover new truths about reality and draw inferences about it? Contemporary accounts of scientific discovery answer this question by focusing on the cognitive mechanisms involved in the generation of new ideas and concepts in terms of a special sort of reasoning—or model-based reasoning—involving imagery. Alternatively, I argue that answering this question requires that we recognise the crucial role of the propositional imagination in the construction and development of models (...)
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  • Models and fiction.Roman Frigg - 2010 - Synthese 172 (2):251-268.
    Most scientific models are not physical objects, and this raises important questions. What sort of entity are models, what is truth in a model, and how do we learn about models? In this paper I argue that models share important aspects in common with literary fiction, and that therefore theories of fiction can be brought to bear on these questions. In particular, I argue that the pretence theory as developed by Walton (1990, Mimesis as make-believe: on the foundations of the (...)
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  • Models in Economics Are Not (Always) Nomological Machines.Cyril Hédoin - 2013 - Philosophy of the Social Sciences 44 (4):424-459.
    This paper evaluates Nancy Cartwright’s critique of economic models. Cartwright argues that economics fails to build relevant “nomological machines” able to isolate capacities. In this paper, I contend that many economic models are not used as nomological machines. I give some evidence for this claim and build on an inferential and pragmatic approach to economic modeling. Modeling in economics responds to peculiar inferential norms where a “good” model is essentially a model that enhances our knowledge about possible worlds. As a (...)
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  • A Complementary Account of Scientific Modelling: Modelling Mechanisms in Cancer Immunology.Martin Zach - forthcoming - British Journal for the Philosophy of Science.
    According to a widely held view, scientific modelling consists in entertaining a set of model descriptions that specify a model. Rather than studying the phenomenon of interest directly, scientists investigate the phenomenon indirectly via a model in the hope of learning about some of the phenomenon’s features. I call this view the description-driven modelling (DDM) account. I argue that although an accurate description of much of scientific research, the DDM account is found wanting as regards the mechanistic modelling found in (...)
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  • Understanding with theoretical models.Petri Ylikoski & N. Emrah Aydinonat - 2014 - Journal of Economic Methodology 21 (1):19-36.
    This paper discusses the epistemic import of highly abstract and simplified theoretical models using Thomas Schelling’s checkerboard model as an example. We argue that the epistemic contribution of theoretical models can be better understood in the context of a cluster of models relevant to the explanatory task at hand. The central claim of the paper is that theoretical models make better sense in the context of a menu of possible explanations. In order to justify this claim, we introduce a distinction (...)
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  • Modeling Mystery.William Wood - 2016 - Scientia et Fides 4 (1):39-59.
    The practice of model-building is very common in analytic philosophical theology. Yet many other theologians worry that any attempt to model God must be hubristic and idolatrous. A better understanding of scientific modeling can set the stage for a more fruitful engagement between analytic theologians and their critics. I first present an account of scientific modeling that draws on recent work in the philosophy of science. I then apply that account to a prominent analytic model of the trinity, Michael Rea (...)
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  • Predictive minds and small-scale models: Kenneth Craik’s contribution to cognitive science.Daniel Williams - 2018 - Philosophical Explorations 21 (2):245-263.
    I identify three lessons from Kenneth Craik’s landmark book “The Nature of Explanation” for contemporary debates surrounding the existence, extent, and nature of mental representation: first, an account of mental representations as neural structures that function analogously to public models; second, an appreciation of prediction as the central component of intelligence in demand of such models; and third, a metaphor for understanding the brain as an engineer, not a scientist. I then relate these insights to discussions surrounding the representational status (...)
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  • Who is a Modeler?Michael Weisberg - 2007 - British Journal for the Philosophy of Science 58 (2):207-233.
    Many standard philosophical accounts of scientific practice fail to distinguish between modeling and other types of theory construction. This failure is unfortunate because there are important contrasts among the goals, procedures, and representations employed by modelers and other kinds of theorists. We can see some of these differences intuitively when we reflect on the methods of theorists such as Vito Volterra and Linus Pauling on the one hand, and Charles Darwin and Dimitri Mendeleev on the other. Much of Volterra's and (...)
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  • The Structure of Tradeoffs in Model Building.John Matthewson & Michael Weisberg - 2009 - Synthese 170 (1):169 - 190.
    Despite their best efforts, scientists may be unable to construct models that simultaneously exemplify every theoretical virtue. One explanation for this is the existence of tradeoffs: relationships of attenuation that constrain the extent to which models can have such desirable qualities. In this paper, we characterize three types of tradeoffs theorists may confront. These characterizations are then used to examine the relationships between parameter precision and two types of generality. We show that several of these relationships exhibit tradeoffs and discuss (...)
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  • Three Kinds of Idealization.Michael Weisberg - 2007 - Journal of Philosophy 104 (12):639-659.
    Philosophers of science increasingly recognize the importance of idealization: the intentional introduction of distortion into scientific theories. Yet this recognition has not yielded consensus about the nature of idealization. e literature of the past thirty years contains disparate characterizations and justifications, but little evidence of convergence towards a common position.
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  • How Fictional Worlds Are Created.Deena Skolnick Weisberg - 2016 - Philosophy Compass 11 (8):462-470.
    Both adults and children have the ability to not only think about reality but also use their imaginations and create fictional worlds. This article describes the process by which world creation happens, drawing from philosophical and psychological treatments of this issue. First, world creators recognize the need to create a fictional world, as when starting a pretend game or opening a novel. Then, creators merge some real-world knowledge with the premises of the fictional world to construct a fuller representation, though (...)
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  • Hypothetical, not Fictional Worlds.Friedel Weinert - 2016 - Kairos 17 (1):110-136.
    This paper critically analyzes the fiction-view of scientific modeling, which exploits presumed analogies between literary fiction and model building in science. The basic idea is that in both fiction and scientific modeling fictional worlds are created. The paper argues that the fiction-view comes closest to certain scientific thought experiments, especially those involving demons in science and to literary movements like naturalism. But the paper concludes that the dissimilarities prevail over the similarities. The fiction-view fails to do justice to the plurality (...)
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  • Forty years of 'the strategy': Levins on model building and idealization.Michael Weisberg - 2006 - Biology and Philosophy 21 (5):623-645.
    This paper is an interpretation and defense of Richard Levins’ “The Strategy of Model Building in Population Biology,” which has been extremely influential among biologists since its publication 40 years ago. In this article, Levins confronted some of the deepest philosophical issues surrounding modeling and theory construction. By way of interpretation, I discuss each of Levins’ major philosophical themes: the problem of complexity, the brute-force approach, the existence and consequence of tradeoffs, and robustness analysis. I argue that Levins’ article is (...)
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  • Biology and Philosophy symposium on Simulation and Similarity: Using Models to Understand the World: Response to critics.Michael Weisberg - 2015 - Biology and Philosophy 30 (2):299-310.
    Simulation and Similarity: Using Models to Understand the World is an account of modeling in contemporary science. Modeling is a form of surrogate reasoning where target systems in the natural world are studied using models, which are similar to these targets. My book develops an account of the nature of models, the practice of modeling, and the similarity relation that holds between models and their targets. I also analyze the conceptual tools that allow theorists to identify the trustworthy aspects of (...)
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  • The strategy of model building in climate science.Lachlan Douglas Walmsley - 2020 - Synthese 199 (1-2):745-765.
    In the 1960s, theoretical biologist Richard Levins criticised modellers in his own discipline of population biology for pursuing the “brute force” strategy of building hyper-realistic models. Instead of exclusively chasing complexity, Levins advocated for the use of multiple different kinds of complementary models, including much simpler ones. In this paper, I argue that the epistemic challenges Levins attributed to the brute force strategy still apply to state-of-the-art climate models today: they have big appetites for unattainable data, they are limited by (...)
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  • Representing with imaginary models: Formats matter.Marion Vorms - 2011 - Studies in History and Philosophy of Science Part A 42 (2):287-295.
    Models such as the simple pendulum, isolated populations, and perfectly rational agents, play a central role in theorising. It is now widely acknowledged that a study of scientific representation should focus on the role of such imaginary entities in scientists’ reasoning. However, the question is most of the time cast as follows: How can fictional or abstract entities represent the phenomena? In this paper, I show that this question is not well posed. First, I clarify the notion of representation, and (...)
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  • Metaphors in arts and science.Walter Veit & Ney Milan - 2021 - European Journal for Philosophy of Science 11 (2):1-24.
    Metaphors abound in both the arts and in science. Due to the traditional division between these enterprises as one concerned with aesthetic values and the other with epistemic values there has unfortunately been very little work on the relation between metaphors in the arts and sciences. In this paper, we aim to remedy this omission by defending a continuity thesis regarding the function of metaphor across both domains, that is, metaphors fulfill any of the same functions in science as they (...)
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  • Model Pluralism.Walter Veit - 2019 - Philosophy of the Social Sciences 50 (2):91-114.
    This paper introduces and defends an account of model-based science that I dub model pluralism. I argue that despite a growing awareness in the philosophy of science literature of the multiplicity, diversity, and richness of models and modeling practices, more radical conclusions follow from this recognition than have previously been inferred. Going against the tendency within the literature to generalize from single models, I explicate and defend the following two core theses: any successful analysis of models must target sets of (...)
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  • What is the Problem of Explanation and Modeling?Raphael van Riel - 2017 - Acta Analytica 32 (3):263-275.
  • The content of model-based information.Raphael van Riel - 2015 - Synthese 192 (12):3839-3858.
    The paper offers an account of the structure of information provided by models that relevantly deviate from reality. It is argued that accounts of scientific modeling according to which a model’s epistemic and pragmatic relevance stems from the alleged fact that models give access to possibilities fail. First, it seems that there are models that do not give access to possibilities, for what they describe is impossible. Secondly, it appears that having access to a possibility is epistemically and pragmatically idle. (...)
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  • Mechanist idealisation in systems biology.Dingmar van Eck & Cory Wright - 2020 - Synthese 199 (1-2):1555-1575.
    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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  • The ontology of theoretical modelling: models as make-believe.Adam Toon - 2010 - Synthese 172 (2):301-315.
    The descriptions and theoretical laws scientists write down when they model a system are often false of any real system. And yet we commonly talk as if there were objects that satisfy the scientists’ assumptions and as if we may learn about their properties. Many attempt to make sense of this by taking the scientists’ descriptions and theoretical laws to define abstract or fictional entities. In this paper, I propose an alternative account of theoretical modelling that draws upon Kendall Walton’s (...)
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  • Playing with molecules.Adam Toon - 2011 - Studies in History and Philosophy of Science Part A 42 (4):580-589.
    Recent philosophy of science has seen a number of attempts to understand scientific models by looking to theories of fiction. In previous work, I have offered an account of models that draws on Kendall Walton’s ‘make-believe’ theory of art. According to this account, models function as ‘props’ in games of make-believe, like children’s dolls or toy trucks. In this paper, I assess the make-believe view through an empirical study of molecular models. I suggest that the view gains support when we (...)
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  • Similarity and Scientific Representation.Adam Toon - 2012 - International Studies in the Philosophy of Science 26 (3):241-257.
    The similarity view of scientific representation has recently been subjected to strong criticism. Much of this criticism has been directed against a ?naive? similarity account, which tries to explain representation solely in terms of similarity between scientific models and the world. This article examines the more sophisticated account offered by the similarity view's leading proponent, Ronald Giere. In contrast to the naive account, Giere's account appeals to the role played by the scientists using a scientific model. A similar move is (...)
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  • Missing systems and the face value practice.Martin Thomson-Jones - 2010 - Synthese 172 (2):283-299.
    Call a bit of scientific discourse a description of a missing system when (i) it has the surface appearance of an accurate description of an actual, concrete system (or kind of system) from the domain of inquiry, but (ii) there are no actual, concrete systems in the world around us fitting the description it contains, and (iii) that fact is recognised from the outset by competent practitioners of the scientific discipline in question. Scientific textbooks, classroom lectures, and journal articles abound (...)
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  • Chomsky in the playground: Idealization in generative linguistics.Giulia Terzian - 2021 - Studies in History and Philosophy of Science Part A 87 (C):1-12.
    For a long time, the accepted explanatory model of language acquisition was the so-called Principles and Parameters framework (P&P). P&P seemingly provides an elegant answer to the central puzzle of generative linguistics: How do children acquire their native language given the limited time and input resources available to them? Yet P&P tells a story that is evolutionarily implausible, and for this reason it has since been abandoned. I argue that this is an unwarranted move, and that it could and should (...)
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  • The missing self in scientific psychiatry.Şerife Tekin - 2019 - Synthese 196 (6):2197-2215.
    Various traditions in mental health care, such as phenomenological, and existential and cognitive-behavioral psychotherapy, implicitly or explicitly acknowledge that a disruption of the self, or the person, or the agent is among the common denominators of different mental disorders. They often emphasize the importance of understanding patients as reasonsresponsive, in their full mental health relevant complexity, if their mental disorder is to be treated successfully. The centrality of the concept of the self is not mirrored in the mainstream scientific approaches (...)
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  • A Credible-World Account of Biological Models.Sim-Hui Tee - 2018 - Axiomathes 28 (3):309-324.
    In a broad brush, biological models are often constructed in two general types: as a concrete model; as an abstract model. A concrete model is a material model such as model organisms, while an abstract model is a mathematical or computational model consists of equations or algorithms. Though there are types of biological models that cannot be strictly categorized as either concrete or abstract, they are falling somewhere in between this spectrum. In view of the fact that biological phenomena are (...)
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  • Exploring students’ epistemological knowledge of models and modelling in science:results from a teaching/learning experience on climate change.Giulia Tasquier, Olivia Levrini & Justin Dillon - 2016 - International Journal of Science Education 38 (4):539-563.
    The scientific community has been debating climate change for over two decades. In the light of certain arguments put forward by the aforesaid community, the EU has recommended a set of innovative reforms to science teaching such as incorporating environmental issues into the scientific curriculum, thereby helping to make schools a place of civic education. However, despite these European recommendations, relatively little emphasis is still given to climate change within science curricula. Climate change, although potentially engaging for students, is a (...)
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  • Knowledge Building Expertise: Nanomodellers’ Education as an Example.Suvi Tala - 2013 - Science & Education 22 (6):1323-1346.
  • 分析形而上学と経験科学の連続主義に対する批判的検討.Masahiro Takatori - 2023 - Kagaku Tetsugaku 56 (1):59.
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  • The Strategies of Modeling in Biology Education.Julia Svoboda & Cynthia Passmore - 2013 - Science & Education 22 (1):119-142.
  • The Representational Semantic Conception.Mauricio Suárez & Francesca Pero - 2019 - Philosophy of Science 86 (2):344-365.
    This paper argues for a representational semantic conception of scientific theories, which respects the bare claim of any semantic view, namely that theories can be characterised as sets of models. RSC must be sharply distinguished from structural versions that assume a further identity of ‘models’ and ‘structures’, which we reject. The practice-turn in the recent philosophical literature suggests instead that modelling must be understood in a deflationary spirit, in terms of the diverse representational practices in the sciences. These insights are (...)
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  • From Implausible Artificial Neurons to Idealized Cognitive Models: Rebooting Philosophy of Artificial Intelligence.Catherine Stinson - 2020 - Philosophy of Science 87 (4):590-611.
    There is a vast literature within philosophy of mind that focuses on artificial intelligence, but hardly mentions methodological questions. There is also a growing body of work in philosophy of science about modeling methodology that hardly mentions examples from cognitive science. Here these discussions are connected. Insights developed in the philosophy of science literature about the importance of idealization provide a way of understanding the neural implausibility of connectionist networks. Insights from neurocognitive science illuminate how relevant similarities between models and (...)
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  • Sustainability and the Infinite Future: A Case Study of a False Modeling Assumption in Environmental Economics.Daniel Steel - 2017 - Erkenntnis 82 (5):1065-1084.
    This essay examines the issue of false assumptions in models via a case study of a prominent economic model of sustainable development, wherein the assumption of an infinite future plays a central role. Two proposals are found to be helpful for this case, one based on the concept of derivational robustness and the other on understanding. Both suggest that the assumption of an infinite future, while arguably legitimate in some applications of the model, is problematic with respect to what I (...)
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  • A glass half-full: Brian Skyrms's signals.Kim Sterelny - 2012 - Economics and Philosophy 28 (1):73-86.
    ExtractBrian Skyrms's Signals has the virtues familiar from his Evolution of the Social Contract and The Stag Hunt. He begins with a very simple model of agents in interaction, and in a series of brief and beautifully clear chapters, this model and its successors are explored, elaborated, connected and illustrated through biological theory and the social sciences. Signals borrows its core model from David Lewis: it is Lewis's signalling game. In this game, two agents interact. One agent can observe which (...)
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  • Science without (parametric) models: the case of bootstrap resampling.Jan Sprenger - 2011 - Synthese 180 (1):65-76.
    Scientific and statistical inferences build heavily on explicit, parametric models, and often with good reasons. However, the limited scope of parametric models and the increasing complexity of the studied systems in modern science raise the risk of model misspecification. Therefore, I examine alternative, data-based inference techniques, such as bootstrap resampling. I argue that their neglect in the philosophical literature is unjustified: they suit some contexts of inquiry much better and use a more direct approach to scientific inference. Moreover, they make (...)
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  • The Vernacular Architecture of Household Energy Models.David Shipworth - 2013 - Perspectives on Science 21 (2):250-266.
    There are many theories of the drivers of energy use in buildings and how this evolves over different timescales. Moezzi and Lutzenhiser (2010, p. 4) characterized these perspectives as technology—in which energy use is determined by the characteristics of buildings and technologies; economics—in which the consumer is conceived as an economically rational utility maximizer; psychology—in which individuals' mental processes give rise to consumption choices; and sociology, anthropology, and social studies of technology—in which patterns of consumption are socially-negotiated and larger social (...)
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