Search results for 'Models' (try it on Scholar)

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  1.  49
    Axel Gelfert (2011). Scientific Models, Simulation, and the Experimenter's Regress. In Paul Humphreys & Cyrille Imbert (eds.), Models, Simulations, and Representations. Routledge
    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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  2. Eric J. Hall (2002). A Characterization of Permutation Models in Terms of Forcing. Notre Dame Journal of Formal Logic 43 (3):157-168.
    We show that if N and M are transitive models of ZFA such that N M, N and M have the same kernel and same set of atoms, and M AC, then N is a Fraenkel-Mostowski-Specker (FMS) submodel of M if and only if M is a generic extension of N by some almost homogeneous notion of forcing. We also develop a slightly modified notion of FMS submodels to characterize the case where M is a generic extension of N (...)
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  3.  48
    H. G. Callaway (forthcoming). Fundamental Physics, Partial Models and Time’s Arrow. In L. Magnani (ed.), Proceedings of MBR2015. Springer
    This paper explores the scientific viability of the concept of causality—by questioning a central element of the distinction between “fundamental” and non-fundamental physics. It will be argued that the prevalent emphasis on fundamental physics involves formalistic and idealized partial models of physical regularities abstracting from and idealizing the causal evolution of physical systems. The accepted roles of partial models and of the special sciences in the growth of knowledge help demonstrate proper limitations of the concept of fundamental physics. (...)
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  4. David Michael Kaplan & Carl F. Craver (2011). The Explanatory Force of Dynamical and Mathematical Models in Neuroscience: A Mechanistic Perspective. Philosophy of Science 78 (4):601-627.
    We argue that dynamical and mathematical models in systems and cognitive neuro- science explain (rather than redescribe) a phenomenon only if there is a plausible mapping between elements in the model and elements in the mechanism for the phe- nomenon. We demonstrate how this model-to-mechanism-mapping constraint, when satisfied, endows a model with explanatory force with respect to the phenomenon to be explained. Several paradigmatic models including the Haken-Kelso-Bunz model of bimanual coordination and the difference-of-Gaussians model of visual receptive (...)
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  5. Alisa Bokulich (2011). How Scientific Models Can Explain. Synthese 180 (1):33 - 45.
    Scientific models invariably involve some degree of idealization, abstraction, or nationalization of their target system. Nonetheless, I argue that there are circumstances under which such false models can offer genuine scientific explanations. After reviewing three different proposals in the literature for how models can explain, I shall introduce a more general account of what I call model explanations, which specify the conditions under which models can be counted as explanatory. I shall illustrate this new framework by (...)
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  6. Piotr Kulicki (2013). On Minimal Models for Pure Calculi of Names. Logic and Logical Philosophy 22 (4):429–443.
    By pure calculus of names we mean a quantifier-free theory, based on the classical propositional calculus, which defines predicates known from Aristotle’s syllogistic and Leśniewski’s Ontology. For a large fragment of the theory decision procedures, defined by a combination of simple syntactic operations and models in two-membered domains, can be used. We compare the system which employs `ε’ as the only specific term with the system enriched with functors of Syllogistic. In the former, we do not need an empty (...)
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  7. Arnon Levy & Adrian Currie (2015). Model Organisms Are Not Models. 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 (...)
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  8. Roman Frigg (2010). Models and Fiction. 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 (...)
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  9. David M. Kaplan & William Bechtel (2011). Dynamical Models: An Alternative or Complement to Mechanistic Explanations? Topics in Cognitive Science 3 (2):438-444.
    Abstract While agreeing that dynamical models play a major role in cognitive science, we reject Stepp, Chemero, and Turvey's contention that they constitute an alternative to mechanistic explanations. We review several problems dynamical models face as putative explanations when they are not grounded in mechanisms. Further, we argue that the opposition of dynamical models and mechanisms is a false one and that those dynamical models that characterize the operations of mechanisms overcome these problems. By briefly considering (...)
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  10.  93
    Susan G. Sterrett, Experimentation on Analogue Models.
    Summary Analogue models are actual physical setups used to model something else. They are especially useful when what we wish to investigate is difficult to observe or experiment upon due to size or distance in space or time: for example, if the thing we wish to investigate is too large, too far away, takes place on a time scale that is too long, does not yet exist or has ceased to exist. The range and variety of analogue models (...)
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  11. Daniel A. Weiskopf (2011). Models and Mechanisms in Psychological Explanation. Synthese 183 (3):313-338.
    Mechanistic explanation has an impressive track record of advancing our understanding of complex, hierarchically organized physical systems, particularly biological and neural systems. But not every complex system can be understood mechanistically. Psychological capacities are often understood by providing cognitive models of the systems that underlie them. I argue that these models, while superficially similar to mechanistic models, in fact have a substantially more complex relation to the real underlying system. They are typically constructed using a range of (...)
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  12. Elisabeth A. Lloyd (2010). Confirmation and Robustness of Climate Models. Philosophy of Science 77 (5):971–984.
    Recent philosophical attention to climate models has highlighted their weaknesses and uncertainties. Here I address the ways that models gain support through observational data. I review examples of model fit, variety of evidence, and independent support for aspects of the models, contrasting my analysis with that of other philosophers. I also investigate model robustness, which often emerges when comparing climate models simulating the same time period or set of conditions. Starting from Michael Weisberg’s analysis of robustness, (...)
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  13.  20
    Adam Toon (2012). Models as Make-Believe: Imagination, Fiction, and Scientific Representation. Palgrave Macmillan.
    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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  14.  74
    Adam Morton & Mauricio Suarez (2001). Kinds of Models. In Model Validation: perspectives in hydrological science. 11-22.
    We separate metaphysical from epistemic questions in the evaluation of models, taking into account the distinctive functions of models as opposed to theories. The examples a\are very varied.
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  15. Ronald Giere (2010). An Agent-Based Conception of Models and Scientific Representation. Synthese 172 (2):269–281.
    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 of (...)
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  16.  35
    Eric Hochstein (2016). One Mechanism, Many Models: A Distributed Theory of Mechanistic Explanation. Synthese 193 (5):1387-1407.
    There have been recent disagreements in the philosophy of neuroscience regarding which sorts of scientific models provide mechanistic explanations, and which do not. These disagreements often hinge on two commonly adopted, but conflicting, ways of understanding mechanistic explanations: what I call the “representation-as” account, and the “representation-of” account. In this paper, I argue that neither account does justice to neuroscientific practice. In their place, I offer a new alternative that can defuse some of these disagreements. I argue that individual (...)
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  17.  29
    Sergio A. Gallegos (2016). The Explanatory Role of Abstraction Processes in Models: The Case of Aggregations. Studies in History and Philosophy of Science Part A 56:161-167.
    Though it is held that some models in science have explanatory value, there is no conclusive agreement on what provides them with this value. One common view is that models have explanatory value vis-à-vis some target systems because they are developed using an abstraction process. Though I think this is correct, I believe it is not the whole picture. In this paper, I argue that, in addition to the well-known process of abstraction understood as an omission of features (...)
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  18. Peter Godfrey-Smith (2009). Models and Fictions in Science. 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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  19.  62
    Marcin Miłkowski (2015). Evaluating Artificial Models of Cognition. Studies in Logic, Grammar and Rhetoric 40 (1):43-62.
    Artificial models of cognition serve different purposes, and their use determines the way they should be evaluated. There are also models that do not represent any particular biological agents, and there is controversy as to how they should be assessed. At the same time, modelers do evaluate such models as better or worse. There is also a widespread tendency to call for publicly available standards of replicability and benchmarking for such models. In this paper, I argue (...)
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  20.  43
    Erik D. Reichle, Keith Rayner & Alexander Pollatsek (2003). The E-Z Reader Model of Eye-Movement Control in Reading: Comparisons to Other Models. Behavioral and Brain Sciences 26 (4):445-476.
    The E-Z Reader model (Reichle et al. 1998; 1999) provides a theoretical framework for understanding how word identification, visual processing, attention, and oculomotor control jointly determine when and where the eyes move during reading. In this article, we first review what is known about eye movements during reading. Then we provide an updated version of the model (E-Z Reader 7) and describe how it accounts for basic findings about eye movement control in reading. We then review several alternative models (...)
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  21. Patricia H. Werhane (2008). Mental Models, Moral Imagination and System Thinking in the Age of Globalization. Journal of Business Ethics 78 (3):463 - 474.
    After experiments with various economic systems, we appear to have conceded, to misquote Winston Churchill that "free enterprise is the worst economic system, except all the others that have been tried." Affirming that conclusion, I shall argue that in today's expanding global economy, we need to revisit our mind-sets about corporate governance and leadership to fit what will be new kinds of free enterprise. The aim is to develop a values-based model for corporate governance in this age of globalization that (...)
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  22. Adam Toon (2010). Models as Make-Believe. In Roman Frigg & Matthew Hunter (eds.), Beyond Mimesis and Convention: Representation in Art and Science. Boston Studies in Philosophy of Science
    In this paper I propose an account of representation for scientific models based on Kendall Walton’s ‘make-believe’ theory of representation in art. I first set out the problem of scientific representation and respond to a recent argument due to Craig Callender and Jonathan Cohen, which aims to show that the problem may be easily dismissed. I then introduce my account of models as props in games of make-believe and show how it offers a solution to the problem. Finally, (...)
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  23.  13
    Petri Ylikoski & N. Emrah Aydinonat (2014). Understanding with Theoretical Models. 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, (...)
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  24. 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.
    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 the positions (...)
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  25. Gabriele Contessa (2010). Scientific Models and Fictional Objects. Synthese 172 (2):215 - 229.
    In this paper, I distinguish scientific models in three kinds on the basis of their ontological status—material models, mathematical models and fictional models, and develop and defend an account of fictional models as fictional objects—i.e. abstract objects that stand for possible concrete objects.
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  26.  20
    Meagan E. Brock, Andrew Vert, Vykinta Kligyte, Ethan P. Waples, Sydney T. Sevier & Michael D. Mumford (2008). Mental Models: An Alternative Evaluation of a Sensemaking Approach to Ethics Instruction. Science and Engineering Ethics 14 (3):449-472.
    In spite of the wide variety of approaches to ethics training it is still debatable which approach has the highest potential to enhance professionals’ integrity. The current effort assesses a novel curriculum that focuses on metacognitive reasoning strategies researchers use when making sense of day-to-day professional practices that have ethical implications. The evaluated trainings effectiveness was assessed by examining five key sensemaking processes, such as framing, emotion regulation, forecasting, self-reflection, and information integration that experts and novices apply in ethical decision-making. (...)
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  27. Stathis Psillos (2011). Living with the Abstract: Realism and Models. Synthese 180 (1):3-17.
    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 the reality of abstract entities? Or are theories simply—and incurably—false? Or has the very idea of literal understanding to be (...)
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  28.  54
    Bryce Huebner (2015). What is a Philosophical Effect? Models of Data in Experimental Philosophy. Philosophical Studies 172 (12):3273-3292.
    Papers in experimental philosophy rarely offer an account of what it would take to reveal a philosophically significant effect. In part, this is because experimental philosophers tend to pay insufficient attention to the hierarchy of models that would be required to justify interpretations of their data; as a result, some of their most exciting claims fail as explanations. But this does not impugn experimental philosophy. My aim is to show that experimental philosophy could be made more successful by developing, (...)
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  29.  23
    Raoul Gervais & Erik Weber (2013). Plausibility Versus Richness in Mechanistic Models. Philosophical Psychology 26 (1):139-152.
    In this paper we argue that in recent literature on mechanistic explanations, authors tend to conflate two distinct features that mechanistic models can have or fail to have: plausibility and richness. By plausibility, we mean the probability that a model is correct in the assertions it makes regarding the parts and operations of the mechanism, i.e., that the model is correct as a description of the actual mechanism. By richness, we mean the amount of detail the model gives about (...)
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  30. Isabelle Peschard (2007). The Value(s) of a Story: Theories, Models and Cognitive Values. Principia 11 (2):151-169.
    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 that guides (...)
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  31. Thomas Mormann, McKinsey Algebras and Topological Models of S4.1.
    The aim of this paper is to show that every topological space gives rise to a wealth of topological models of the modal logic S4.1. The construction of these models is based on the fact that every space defines a Boolean closure algebra (to be called a McKinsey algebra) that neatly reflects the structure of the modal system S4.1. It is shown that the class of topological models based on McKinsey algebras contains a canonical model that can (...)
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  32.  43
    James R. Griesemer & Michael J. Wade (1988). Laboratory Models, Causal Explanation and Group Selection. Biology and Philosophy 3 (1):67-96.
    We develop an account of laboratory models, which have been central to the group selection controversy. We compare arguments for group selection in nature with Darwin's arguments for natural selection to argue that laboratory models provide important grounds for causal claims about selection. Biologists get information about causes and cause-effect relationships in the laboratory because of the special role their own causal agency plays there. They can also get information about patterns of effects and antecedent conditions in nature. (...)
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  33.  42
    Axel Gelfert (2015). Between Rigor and Reality: Many-Body Models in Condensed Matter Physics. In Brigitte Falkenburg & Margaret Morrison (eds.), Why More Is Different: Philosophical Issues in Condensed Matter Physics and Complex Systems. Springer 201-226.
    The present paper focuses on a particular class of models intended to describe and explain the physical behaviour of systems that consist of a large number of interacting particles. Such many-body models are characterized by a specific Hamiltonian (energy operator) and are frequently employed in condensed matter physics in order to account for such phenomena as magnetism, superconductivity, and other phase transitions. Because of the dual role of many-body models as models of physical sys-tems (with specific (...)
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  34. Toby Handfield, Charles R. Twardy, Kevin B. Korb & Graham Oppy (2008). The Metaphysics of Causal Models: Where's the Biff? Erkenntnis 68 (2):149-68.
    This paper presents an attempt to integrate theories of causal processes—of the kind developed by Wesley Salmon and Phil Dowe—into a theory of causal models using Bayesian networks. We suggest that arcs in causal models must correspond to possible causal processes. Moreover, we suggest that when processes are rendered physically impossible by what occurs on distinct paths, the original model must be restricted by removing the relevant arc. These two techniques suffice to explain cases of late preëmption and (...)
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  35. Hans Schoutens (1999). Existentially Closed Models of the Theory of Artinian Local Rings. Journal of Symbolic Logic 64 (2):825-845.
    The class of all Artinian local rings of length at most l is ∀ 2 -elementary, axiomatised by a finite set of axioms Art l . We show that its existentially closed models are Gorenstein, of length exactly l and their residue fields are algebraically closed, and, conversely, every existentially closed model is of this form. The theory Got l of all Artinian local Gorenstein rings of length l with algebraically closed residue field is model complete and the theory (...)
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  36.  34
    Charles Kemp, Noah D. Goodman & Joshua B. Tenenbaum (2010). Learning to Learn Causal Models. Cognitive Science 34 (7):1185-1243.
    Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these causal models. The schema organizes (...)
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  37.  57
    Giovanni De Grandis & Yrsa Neuman (2014). Measuring Openness and Evaluating Digital Academic Publishing Models: Not Quite the Same Business. The Journal of Electronic Publishing 17 (3).
    In this article we raise a problem, and we offer two practical contributions to its solution. The problem is that academic communities interested in digital publishing do not have adequate tools to help them in choosing a publishing model that suits their needs. We believe that excessive focus on Open Access (OA) has obscured some important issues; moreover exclusive emphasis on increasing openness has contributed to an agenda and to policies that show clear practical shortcomings. We believe that academic communities (...)
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  38.  71
    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.
    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 understand and (...)
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  39.  21
    José Díez & Pablo Lorenzano (2015). Are Natural Selection Explanatory Models a Priori? Biology and Philosophy 30 (6):787-809.
    The epistemic status of Natural Selection has seemed intriguing to biologists and philosophers since the very beginning of the theory to our present times. One prominent contemporary example is Elliott Sober, who claims that NS, and some other theories in biology, and maybe in economics, are peculiar in including explanatory models/conditionals that are a priori in a sense in which explanatory models/conditionals in Classical Mechanics and most other standard theories are not. Sober’s argument focuses on some “would promote” (...)
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  40. Domenico Zambella & Antonella Mancini (2001). A Note on Recursive Models of Set Theories. Notre Dame Journal of Formal Logic 42 (2):109-115.
    We construct two recursive models of fragments of set theory. We also show that the fragments of Kripke-Platek set theory that prove -induction for -formulas have no recursive models but the standard model of the hereditarily finite sets.
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  41.  35
    Brian Riordan & Michael N. Jones (2011). Redundancy in Perceptual and Linguistic Experience: Comparing Feature-Based and Distributional Models of Semantic Representation. Topics in Cognitive Science 3 (2):303-345.
    Abstract Since their inception, distributional models of semantics have been criticized as inadequate cognitive theories of human semantic learning and representation. A principal challenge is that the representations derived by distributional models are purely symbolic and are not grounded in perception and action; this challenge has led many to favor feature-based models of semantic representation. We argue that the amount of perceptual and other semantic information that can be learned from purely distributional statistics has been underappreciated. We (...)
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  42. Margaret Morrison (2011). One Phenomenon, Many Models: Inconsistency and Complementarity. Studies in History and Philosophy of Science 42 (2):342-351.
    The paper examines philosophical issues that arise in contexts where one has many different models for treating the same system. I show why in some cases this appears relatively unproblematic (models of turbulence) while others represent genuine difficulties when attempting to interpret the information that models provide (nuclear models). What the examples show is that while complementary models needn’t be a hindrance to knowledge acquisition, the kind of inconsistency present in nuclear cases is, since it (...)
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  43.  25
    Marion Vorms (2011). Representing with Imaginary Models: Formats Matter. Studies in History and Philosophy of Science 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, (...)
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  44.  3
    Francoise Monnoyeur (2015). What is the Value of Geometric Models to Understand Matter? Epekeina 6 (2):1-13.
    This article analyzes the value of geometric models to understand matter with the examples of the Platonic model for the primary four elements (fire, air, water, and earth) and the models of carbon atomic structures in the new science of crystallography. How the geometry of these models is built in order to discover the properties of matter is explained: movement and stability for the primary elements, and hardness, softness and elasticity for the carbon atoms. These geometric (...) appear to have a double quality: firstly, they exhibit visually the scientific properties of matter, and secondly they give us the possibility to visualize its whole nature. Geometrical models appear to be the expression of the mind in the understanding of physical matter. (shrink)
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  45.  67
    Axel Gelfert (2009). Rigorous Results, Cross-Model Justification, and the Transfer of Empirical Warrant: The Case of Many-Body Models in Physics. Synthese 169 (3):497 - 519.
    This paper argues that a successful philosophical analysis of models and simulations must accommodate an account of mathematically rigorous results. Such rigorous results may be thought of as genuinely model-specific contributions, which can neither be deduced from fundamental theory nor inferred from empirical data. Rigorous results provide new indirect ways of assessing the success of models and simulations and are crucial to understanding the connections between different models. This is most obvious in cases where rigorous results map (...)
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  46.  23
    Marcin Miłkowski (2014). Computational Mechanisms and Models of Computation. Philosophia Scientiæ 18:215-228.
    In most accounts of realization of computational processes by physical mechanisms, it is presupposed that there is one-to-one correspondence between the causally active states of the physical process and the states of the computation. Yet such proposals either stipulate that only one model of computation is implemented, or they do not reflect upon the variety of models that could be implemented physically. -/- In this paper, I claim that mechanistic accounts of computation should allow for a broad variation of (...)
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  47.  20
    Mathias Frisch (2013). Modeling Climate Policies: A Critical Look at Integrated Assessment Models. Philosophy and Technology 26 (2):117-137.
    Climate change presents us with a problem of intergenerational justice. While any costs associated with climate change mitigation measures will have to be borne by the world’s present generation, the main beneficiaries of mitigation measures will be future generations. This raises the question to what extent present generations have a responsibility to shoulder these costs. One influential approach for addressing this question is to appeal to neo-classical economic cost–benefit analyses and so-called economy-climate “integrated assessment models” to determine what course (...)
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  48.  17
    Mark Pexton (2014). Can Asymptotic Models Be Explanatory? European Journal for Philosophy of Science 4 (2):233-252.
    Asymptotic models in which singular limits are taken are very common in physics. They are often used to investigate the general behaviour of systems undergoing rapid, discontinuous, changes. The singularities in the mathematics of these systems have no physical counterparts; these models operate by containing non-physically interpretable fictional elements. As such there is an intuition that states that asymptotics only offer descriptions of systems not explanations of them. By contrast, in different areas of science other models containing (...)
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  49. Sebastian Lutz (2014). What's Right with a Syntactic Approach to Theories and Models? Erkenntnis (S8):1-18.
    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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  50.  88
    H. G. Callaway (2014). Abduction, Competing Models and the Virtues of Hypotheses. In Lorenzo Magnani (ed.), (2014) Model-Based Reasoning in Science and Technology. Springer 263-280.
    This paper focuses on abduction as explicit or readily formulatable inference to possible explanatory hypotheses--as contrasted with inference to conceptual innovations or abductive logic as a cycle of hypotheses, deduction of consequences and inductive testing. Inference to an explanation is often a matter of projection or extrapolation of elements of accepted theory for the solution of outstanding problems in particular domains of inquiry. I say "projections or extrapolation" of accepted theory, but I mean to point to something broader and suggest (...)
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