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  1. The Kac Ring or the Art of Making Idealisations.Julie Jebeile - 2020 - Foundations of Physics 50 (10):1152-1170.
    In 1959, mathematician Mark Kac introduced a model, called the Kac ring, in order to elucidate the classical solution of Boltzmann to the problem of macroscopic irreversibility. However, the model is far from being a realistic representation of something. How can it be of any help here? In philosophy of science, it is often argued that models can provide explanations of the phenomenon they are said to approximate, in virtue of the truth they contain, and in spite of the idealisations (...)
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  • Scientific Discovery Through Fictionally Modelling Reality.Fiora Salis - 2020 - 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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  • Formal Semantics and Applied Mathematics: An Inferential Account.Ryan M. Nefdt - 2020 - Journal of Logic, Language and Information 29 (2):221-253.
    In this paper, I utilise the growing literature on scientific modelling to investigate the nature of formal semantics from the perspective of the philosophy of science. Specifically, I incorporate the inferential framework proposed by Bueno and Colyvan : 345–374, 2011) in the philosophy of applied mathematics to offer an account of how formal semantics explains and models its data. This view produces a picture of formal semantic models as involving an embedded process of inference and representation applying indirectly to linguistic (...)
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  • What Good Are Abstract and What-If Models? Lessons From the Gaïa Hypothesis.Sébastien Dutreuil - 2014 - History and Philosophy of the Life Sciences 36 (1):16-41.
    This article on the epistemology of computational models stems from an analysis of the Gaia hypothesis (GH). It begins with James Kirchner’s criticisms of the central computational model of GH: Daisyworld. Among other things, the model has been criticized for being too abstract, describing fictional entities (fictive daisies on an imaginary planet) and trying to answer counterfactual (what-if) questions (how would a planet look like if life had no influence on it?). For these reasons the model has been considered not (...)
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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 has the resources to answer these questions. I introduce (...)
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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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  • Pluralism in Economics: Its Critiques and Their Lessons.Claudius Gräbner & Birte Strunk - forthcoming - Journal of Economic Methodology:1-19.
    This paper starts with an evaluation of three common arguments against pluralism in economics: the claim that economics is already pluralist, the argument that if there was the need for gre...
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  • The Nature of Model-World Comparisons.Fiora Salis - 2016 - The Monist 99 (3):243-259.
    Upholders of fictionalism about scientific models have not yet successfully explained how scientists can learn about the real world by making comparisons between models and the real phenomena they stand for. In this paper I develop an account of model-world comparisons in terms of what I take to be the best antirealist analyses of comparative claims that emerge from the current debate on fiction.
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  • Explanation and Understanding Through Scientific Models.Richard David-Rus - 2012 - Institutul European.
  • From Models-as-Fictions to Models-as-Tools.Adrian Currie - 2017 - Ergo: An Open Access Journal of Philosophy 4.
    Many accounts of scientific modeling conceive of models as fictions: scientists interact with models in ways analogous to various aesthetic objects. Fictionalists follow most other accounts of modeling by taking them to be revelatory of the actual world in virtue of bearing some resemblance relation to a target system. While such fictionalist accounts capture crucial aspects of modelling practice, they are ill-suited to some design and engineering contexts. Here, models sometimes serve to underwrite design projects whereby real-world targets are constructed. (...)
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  • It’s Not a Game: Accurate Representation with Toy Models.James Nguyen - 2020 - British Journal for the Philosophy of Science 71 (3):1013-1041.
    Drawing on ‘interpretational’ accounts of scientific representation, I argue that the use of so-called ‘toy models’ provides no particular philosophical puzzle. More specifically; I argue that once one gives up the idea that models are accurate representations of their targets only if they are appropriately similar, then simple and highly idealized models can be accurate in the same way that more complex models can be. Their differences turn on trading precision for generality, but, if they are appropriately interpreted, toy models (...)
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  • Science in a New Mode: Good Old (Theoretical) Science Versus Brave New (Commodified) Knowledge Production?Tarja Knuuttila - 2013 - Science & Education 22 (10):2443-2461.
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  • Are We Sims? How Computer Simulations Represent and What This Means for the Simulation Argument.Claus Beisbart - 2014 - The Monist 97 (3):399-417.
    N. Bostrom’s simulation argument and two additional assumptions imply that we likely live in a computer simulation. The argument is based upon the following assumption about the workings of realistic brain simulations: The hardware of a computer on which a brain simulation is run bears a close analogy to the brain itself. To inquire whether this is so, I analyze how computer simulations trace processes in their targets. I describe simulations as fictional, mathematical, pictorial, and material models. Even though the (...)
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  • Are Computer Simulations Experiments? And If Not, How Are They Related to Each Other?Claus Beisbart - 2018 - European Journal for Philosophy of Science 8 (2):171-204.
    Computer simulations and experiments share many important features. One way of explaining the similarities is to say that computer simulations just are experiments. This claim is quite popular in the literature. The aim of this paper is to argue against the claim and to develop an alternative explanation of why computer simulations resemble experiments. To this purpose, experiment is characterized in terms of an intervention on a system and of the observation of the reaction. Thus, if computer simulations are experiments, (...)
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  • How Can Computer Simulations Produce New Knowledge?Claus Beisbart - 2012 - European Journal for Philosophy of Science 2 (3):395-434.
    It is often claimed that scientists can obtain new knowledge about nature by running computer simulations. How is this possible? I answer this question by arguing that computer simulations are arguments. This view parallels Norton’s argument view about thought experiments. I show that computer simulations can be reconstructed as arguments that fully capture the epistemic power of the simulations. Assuming the extended mind hypothesis, I furthermore argue that running the computer simulation is to execute the reconstructing argument. I discuss some (...)
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  • Virtual Realism: Really Realism or Only Virtually So? A Comment on D. J. Chalmers’s Petrus Hispanus Lectures.Claus Beisbart - 2019 - Disputatio 11 (55):297-331.
    What is the status of a cat in a virtual reality environment? Is it a real object? Or part of a fiction? Virtual realism, as defended by D. J. Chalmers, takes it to be a virtual object that really exists, that has properties and is involved in real events. His preferred specification of virtual realism identifies the cat with a digital object. The project of this paper is to use a comparison between virtual reality environments and scientific computer simulations to (...)
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  • Models, Sherlock Holmes and the Emperor Claudius.Adam Toon - manuscript
    Recently, a number of authors have suggested that we understand scientific models in the same way as fictional characters, like Sherlock Holmes. The biggest challenge for this approach concerns the ontology of fictional characters. I consider two responses to this challenge, given by Roman Frigg, Ronald Giere and Peter Godfrey-Smith, and argue that neither is successful. I then suggest an alternative approach. While parallels with fiction are useful, I argue that models of real systems are more aptly compared to works (...)
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  • Abstraction as an Autonomous Process in Scientific Modeling.Sim-Hui Tee - 2020 - Philosophia 48 (2):789-801.
    ion is one of the important processes in scientific modeling. It has always been implied that abstraction is an agent-centric activity that involves the cognitive processes of scientists in model building. I contend that there is an autonomous aspect of abstraction in many modeling activities. I argue that the autonomous process of abstraction is continuous with the agent-centric abstraction but capable of evolving independently from the modeler’s abstraction activity.
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  • How Do Models Give Us Knowledge? The Case of Carnot’s Ideal Heat Engine.Tarja Knuuttila & Mieke Boon - 2011 - European Journal for Philosophy of Science 1 (3):309-334.
    Our concern is in explaining how and why models give us useful knowledge. We argue that if we are to understand how models function in the actual scientific practice the representational approach to models proves either misleading or too minimal. We propose turning from the representational approach to the artefactual, which implies also a new unit of analysis: the activity of modelling. Modelling, we suggest, could be approached as a specific practice in which concrete artefacts, i.e., models, are constructed with (...)
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  • Reports From the High Table: Sepkoski and Ruse : The Paleobiological Revolution: Essays on the Growth of Modern Paleontology, University of Chicago Press, 2009.Adrian Currie - 2012 - Biology and Philosophy 27 (1):149-158.
    David Sepkoski and Michael Ruse’s edited collection The Peolobiological Revolution covers the changes in paleontological science in the last half-century. The collection should be of interest to philosophers of science (particularly those interested in non-reductive unity) as well as historians. I give an overview of the content and major themes of the volume and draw some lessons for the philosophy of science along the way. In particular, I argue that the history of paleontology demands a new approach to philosophical delineation (...)
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  • Cutting a Bone to Heal a Ligament: Idealized Animals and Orthopaedics. [REVIEW]Chris Degeling - 2010 - Medicine Studies 2 (2):101-119.
    Developments in biomedical science continue to transform our understanding of concepts such as health and disease. The creation of this expertise has also had a substantive role in changing the veterinary approach to animal diseases. Traditionally, companion animal veterinarians modelled their practices on developments in the diagnosis and treatment of human patients. As science and technology have realigned the boundaries between normalcy, intra-species variation and pathology in particular domains of expertise such as orthopaedic surgery, these patterns of knowledge translation have (...)
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  • Philosophy of Chemistry.Michael Weisberg, Paul Needham & Robin Hendry - 2011 - Stanford Encyclopedia of Philosophy.
    Chemistry is the study of the structure and transformation of matter. When Aristotle founded the field in the 4th century BCE, his conceptual grasp of the nature of matter was tailored to accommodate a relatively simple range of observable phenomena. In the 21st century, chemistry has become the largest scientific discipline, producing over half a million publications a year ranging from direct empirical investigations to substantial theoretical work. However, the specialized interest in the conceptual issues arising in chemistry, hereafter Philosophy (...)
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  • Philosophy of Psychiatry.Dominic Murphy - 2010 - Stanford Encyclopedia of Philosophy.
  • The Periodic System: The (Multiple) Values of an Icon.Annette Lykknes & Brigitte Van Tiggelen - 2019 - Centaurus 61 (4):287-298.
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  • Concrete Scale Models, Essential Idealization, and Causal Explanation.Christopher Pincock - forthcoming - British Journal for the Philosophy of Science.
    This paper defends three claims about concrete or physical models: these models remain important in science and engineering, they are often essentially idealized, in a sense to be made precise, and despite these essential idealizations, some of these models may be reliably used for the purpose of causal explanation. This discussion of concrete models is pursued using a detailed case study of some recent models of landslide generated impulse waves. Practitioners show a clear awareness of the idealized character of these (...)
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  • Model-Based Theorizing in Cognitive Neuroscience.Elizabeth Irvine - 2016 - British Journal for the Philosophy of Science 67 (1):143-168.
    Weisberg and Godfrey-Smith distinguish between two forms of theorising: data-driven ‘abstract direct representation’ and modeling. The key difference is that when using a data-driven approach, theories are intended to represent specific phenomena, so directly represent them, while models may not be intended to represent anything, so represent targets indirectly, if at all. The aim here is to compare and analyse these practices, in order to outline an account of model-based theorising that involves direct representational relationships. This is based on the (...)
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  • Philosophy of Science and the Curse of the Case Study.Adrian Currie - 2015 - In Christopher Daly (ed.), The Palgrave Handbook of Philosophical Methods. Palgrave Macmillan. pp. 553-572.
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  • Optimality Explanations: A Plea for an Alternative Approach.Collin C. Rice - 2012 - Biology and Philosophy 27 (5):685-703.
    Recently philosophers of science have begun to pay more attention to the use of highly idealized mathematical models in scientific theorizing. An important example of this kind of highly idealized modeling is the widespread use of optimality models within evolutionary biology. One way to understand the explanations provided by these models is as a censored causal explanation: an explanation that omits certain causal factors in order to focus on a modular subset of the causal processes that led to the explanandum. (...)
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  • Scientific Models.Stephen M. Downes - 2011 - Philosophy Compass 6 (11):757-764.
    This contribution provides an assessment of the epistemological role of scientific models. The prevalent view that all scientific models are representations of the world is rejected. This view points to a unified way of resolving epistemic issues for scientific models. The emerging consensus in philosophy of science that models have many different epistemic roles in science is presented and defended.
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  • Models and Fictions in Science.Peter Godfrey-Smith - 2009 - Philosophical Studies 143 (1):101 - 116.
    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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  • Factive Scientific Understanding Without Accurate Representation.Collin C. Rice - 2016 - Biology and Philosophy 31 (1):81-102.
    This paper analyzes two ways idealized biological models produce factive scientific understanding. I then argue that models can provide factive scientific understanding of a phenomenon without providing an accurate representation of the features of their real-world target system. My analysis of these cases also suggests that the debate over scientific realism needs to investigate the factive scientific understanding produced by scientists’ use of idealized models rather than the accuracy of scientific models themselves.
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  • Cognition in Practice: Conceptual Development and Disagreement in Cognitive Science.Mikio Akagi - 2016 - Dissertation, University of Pittsburgh
    Cognitive science has been beset for thirty years by foundational disputes about the nature and extension of cognition—e.g. whether cognition is necessarily representational, whether cognitive processes extend outside the brain or body, and whether plants or microbes have them. Whereas previous philosophical work aimed to settle these disputes, I aim to understand what conception of cognition scientists could share given that they disagree so fundamentally. To this end, I develop a number of variations on traditional conceptual explication, and defend a (...)
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  • Imagination in Scientific Modeling.Adam Toon - 2016 - In Amy Kind (ed.), The Routledge Handbook of Philosophy of Imagination. 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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  • The New Fiction View of Models.Fiora Salis - forthcoming - British Journal for the Philosophy of Science:axz015.
    How do models represent reality? There are two conditions that scientific models must satisfy to be representations of real systems, the aboutness condition and the epistemic condition. In this article, I critically assess the two main fictionalist theories of models as representations, the indirect fiction view and the direct fiction view, with respect to these conditions. And I develop a novel proposal, what I call ‘the new fiction view of models’. On this view, models are akin to fictional stories; they (...)
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  • A Verisimilitudinarian Analysis of the Linda Paradox.Gustavo Cevolani, Vincenzo Crupi & Roberto Festa - 2012 - VII Conference of the Spanish Society for Logic, Methodology and Philosphy of Science.
    The Linda paradox is a key topic in current debates on the rationality of human reasoning and its limitations. We present a novel analysis of this paradox, based on the notion of verisimilitude as studied in the philosophy of science. The comparison with an alternative analysis based on probabilistic confirmation suggests how to overcome some problems of our account by introducing an adequately defined notion of verisimilitudinarian confirmation.
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  • Model Organisms Are Not (Theoretical) Models.Arnon Levy & Adrian Currie - 2015 - British Journal for the Philosophy of Science 66 (2):327-348.
    Many biological investigations are organized around a small group of species, often referred to as ‘model organisms’, such as the fruit fly Drosophila melanogaster. The terms ‘model’ and ‘modelling’ also occur in biology in association with mathematical and mechanistic theorizing, as in the Lotka–Volterra model of predator-prey dynamics. What is the relation between theoretical models and model organisms? Are these models in the same sense? We offer an account on which the two practices are shown to have different epistemic characters. (...)
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  • Why We Cannot Learn From Minimal Models.Roberto Fumagalli - 2016 - Erkenntnis 81 (3):433-455.
    Philosophers of science have developed several accounts of how consideration of scientific models can prompt learning about real-world targets. In recent years, various authors advocated the thesis that consideration of so-called minimal models can prompt learning about such targets. In this paper, I draw on the philosophical literature on scientific modelling and on widely cited illustrations from economics and biology to argue that this thesis fails to withstand scrutiny. More specifically, I criticize leading proponents of such thesis for failing to (...)
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  • Representing in the Student Laboratory.Brandon Boesch - 2018 - Transversal: International Journal for the Historiography of Science 5:34-48.
    In this essay, I will expand the philosophical discussion about the representational practice in science to examine its role in science education through four case studies. The cases are of what I call ‘educational laboratory experiments’, performative models used representationally by students to come to a better understanding of theoretical knowledge of a scientific discipline. The studies help to demonstrate some idiosyncratic features of representational practices in science education, most importantly a lack of novelty and discovery built into the ELEs (...)
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  • Modeling Without Representation.Alistair M. C. Isaac - 2013 - Synthese 190 (16):3611-3623.
    How can mathematical models which represent the causal structure of the world incompletely or incorrectly have any scientific value? I argue that this apparent puzzle is an artifact of a realist emphasis on representation in the philosophy of modeling. I offer an alternative, pragmatic methodology of modeling, inspired by classic papers by modelers themselves. The crux of the view is that models developed for purposes other than explanation may be justified without reference to their representational properties.
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  • How Much Work Do Scientific Images Do?Stephen Downes - 2012 - Spontaneous Generations 6 (1):115-130.
    In this paper, I defend the view that there are many scientific images that have a serious epistemic role in science but this role is not adequately accounted for by the going view of representation and its attendant theoretical commitments. The relevant view of representation is Laura Perini’s account of representation for scientific images. I draw on Adina Roskies’ work on scientific images as well as work on models in science to support my conclusion.
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  • Model, Theory, and Evidence in the Discovery of the DNA Structure.Samuel Schindler - 2008 - British Journal for the Philosophy of Science 59 (4):619-658.
    In this paper, I discuss the discovery of the DNA structure by Francis Crick and James Watson, which has provoked a large historical literature but has yet not found entry into philosophical debates. I want to redress this imbalance. In contrast to the available historical literature, a strong emphasis will be placed upon analysing the roles played by theory, model, and evidence and the relationship between them. In particular, I am going to discuss not only Crick and Watson's well-known model (...)
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  • The Practical Value of Biological Information for Research.Beckett Sterner - 2014 - Philosophy of Science 81 (2):175-194,.
    Many philosophers are skeptical about the scientific value of the concept of biological information. However, several have recently proposed a more positive view of ascribing information as an exercise in scientific modeling. I argue for an alternative role: guiding empirical data collection for the sake of theorizing about the evolution of semantics. I clarify and expand on Bergstrom and Rosvall’s suggestion of taking a “diagnostic” approach that defines biological information operationally as a procedure for collecting empirical cases. The more recent (...)
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  • Microbes, Mathematics, and Models.Maureen A. O'Malley & Emily C. Parke - 2018 - Studies in History and Philosophy of Science Part A 72:1-10.
    Microbial model systems have a long history of fruitful use in fields that include evolution and ecology. In order to develop further insight into modelling practice, we examine how the competitive exclusion and coexistence of competing species have been modelled mathematically and materially over the course of a long research history. In particular, we investigate how microbial models of these dynamics interact with mathematical or computational models of the same phenomena. Our cases illuminate the ways in which microbial systems and (...)
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  • Mass Extinctions as Major Transitions.Adrian Currie - 2019 - Biology and Philosophy 34 (2):29.
    Both paleobiology and investigations of ‘major evolutionary transitions’ are intimately concerned with the macroevolutionary shape of life. It is surprising, then, how little studies of major transitions are informed by paleontological perspectives and. I argue that this disconnect is partially justified because paleobiological investigation is typically ‘phenomena-led’, while investigations of major transitions are ‘theory-led’. The distinction turns on evidential relevance: in the former case, evidence is relevant in virtue of its relationship to some phenomena or hypotheses concerning those phenomena; in (...)
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  • To Save the Semantic View: An Argument for Returning to Suppes' Interpretation.Thomas Cunningham - manuscript
    Recent work on the semantic view of scientific theories is highly critical of the position. This paper identifies two common criticisms of the view, describes two popular alternatives for responding to them, and argues those responses do not suffice. Subsequently, it argues that retuning to Patrick Suppes’ interpretation of the position provides the conceptual resources for rehabilitating the semantic view.
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  • Information in Biology.Peter Godfrey-Smith - 2007 - In David L. Hull & Michael Ruse (eds.), The Cambridge Companion to the Philosophy of Biology. Cambridge University Press. pp. 103--119.
    The concept of information has acquired a strikingly prominent role in contemporary biology. This trend is especially marked within genetics, but it has also become important in other areas, such as evolutionary theory and developmental biology, particularly where these fields border on genetics. The most distinctive biological role for informational concepts, and the one that has generated the most discussion, is in the description of the relations between genes and the various structures and processes that genes play a role in (...)
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  • Entropy - A Guide for the Perplexed.Roman Frigg & Charlotte Werndl - 2011 - In Claus Beisbart & Stephan Hartmann (eds.), Probabilities in Physics. Oxford University Press. pp. 115-142.
    Entropy is ubiquitous in physics, and it plays important roles in numerous other disciplines ranging from logic and statistics to biology and economics. However, a closer look reveals a complicated picture: entropy is defined differently in different contexts, and even within the same domain different notions of entropy are at work. Some of these are defined in terms of probabilities, others are not. The aim of this chapter is to arrive at an understanding of some of the most important notions (...)
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  • Feminist Implications of Model-Based Science.Angela Potochnik - 2012 - Studies in History and Philosophy of Science Part A 43 (2):383-389.
    Recent philosophy of science has witnessed a shift in focus, in that significantly more consideration is given to how scientists employ models. Attending to the role of models in scientific practice leads to new questions about the representational roles of models, the purpose of idealizations, why multiple models are used for the same phenomenon, and many more besides. In this paper, I suggest that these themes resonate with central topics in feminist epistemology, in particular prominent versions of feminist empiricism, and (...)
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  • On a Straw Man in the Philosophy of Science - A Defense of the Received View.Sebastian Lutz - 2012 - Hopos: The Journal of the International Society for the History of Philosophy of Science 2 (1):77–120.
    I defend the Received View on scientific theories as developed by Carnap, Hempel, and Feigl against a number of criticisms based on misconceptions. First, I dispute the claim that the Received View demands axiomatizations in first order logic, and the further claim that these axiomatizations must include axioms for the mathematics used in the scientific theories. Next, I contend that models are important according to the Received View. Finally, I argue against the claim that the Received View is intended to (...)
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  • The Mental States of Persons and Their Brains.Tim Crane - 2015 - Royal Institute of Philosophy Supplement 76:253-270.
    Cognitive neuroscientists frequently talk about the brain representing the world. Some philosophers claim that this is a confusion. This paper argues that there is no confusion, and outlines one thing that might mean, using the notion of a model derived from the philosophy of science. This description is then extended to make apply to propositional attitude attributions. A number of problems about propositional attitude attributions can be solved or dissolved by treating propositional attitudes as models.
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