Results for 'Models*'

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  1. The Explanatory Force of Dynamical and Mathematical Models in Neuroscience: A Mechanistic Perspective.David Michael Kaplan & Carl F. Craver - 2011 - 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 fields are (...)
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  2. How Scientific Models Can Explain.Alisa Bokulich - 2011 - 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 applying it to the (...)
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  3.  67
    Scientific Models, Simulation, and the Experimenter's Regress.Axel Gelfert - 2011 - 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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  4.  62
    Models and Explanation.Alisa Bokulich - 2017 - In Lorenzo Magnani & Tommaso Wayne Bertolotti (eds.), Springer Handbook of Model-Based Science. Springer. pp. 103-118.
    Detailed examinations of scientific practice have revealed that the use of idealized models in the sciences is pervasive. These models play a central role in not only the investigation and prediction of phenomena, but in their received scientific explanations as well. This has led philosophers of science to begin revising the traditional philosophical accounts of scientific explanation in order to make sense of this practice. These new model-based accounts of scientific explanation, however, raise a number of key questions: Can the (...)
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  5. Model Organisms Are Not 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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  6. Climate Models, Calibration, and Confirmation.K. Steele & C. Werndl - 2013 - British Journal for the Philosophy of Science 64 (3):609-635.
    We argue that concerns about double-counting—using the same evidence both to calibrate or tune climate models and also to confirm or verify that the models are adequate—deserve more careful scrutiny in climate modelling circles. It is widely held that double-counting is bad and that separate data must be used for calibration and confirmation. We show that this is far from obviously true, and that climate scientists may be confusing their targets. Our analysis turns on a Bayesian/relative-likelihood approach to incremental confirmation. (...)
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  7. Dynamical Models: An Alternative or Complement to Mechanistic Explanations?David M. Kaplan & William Bechtel - 2011 - 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 examples involving the generation (...)
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  8. An Agent-Based Conception of Models and Scientific Representation.Ronald Giere - 2010 - 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 models. (...)
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  9. Models and Mechanisms in Psychological Explanation.Daniel A. Weiskopf - 2011 - 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 techniques for abstracting (...)
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  10. 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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  11. Confirmation and Robustness of Climate Models.Elisabeth A. Lloyd - 2010 - 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, I conclude that his (...)
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  12.  46
    Models as Signs: Extending Kralemann and Lattman’s Proposal on Modeling Models Within Peirce’s Theory of Signs.Sergio A. Gallegos - forthcoming - Synthese.
    In recent decades, philosophers of science have devoted considerable efforts to understand what models represent. One popular position is that models represent fictional situations. Another position states that, though models often involve fictional elements, they represent real objects or scenarios. Though these two positions may seem to be incompatible, I believe it is possible to reconcile them. Using a threefold distinction between different signs proposed by Peirce, I develop an argument based on a proposal recently made by Kralemann and Lattman (...)
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  13. Models as Make-Believe: Imagination, Fiction, and Scientific Representation.Adam Toon - 2012 - 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.  4
    Models of Ecological Rationality: The Recognition Heuristic.Daniel G. Goldstein & Gerd Gigerenzer - 2002 - Psychological Review 109 (1):75-90.
    [Correction Notice: An erratum for this article was reported in Vol 109 of Psychological Review. Due to circumstances that were beyond the control of the authors, the studies reported in "Models of Ecological Rationality: The Recognition Heuristic," by Daniel G. Goldstein and Gerd Gigerenzer overlap with studies reported in "The Recognition Heuristic: How Ignorance Makes Us Smart," by the same authors and with studies reported in "Inference From Ignorance: The Recognition Heuristic". In addition, Figure 3 in the Psychological Review article (...)
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  15.  31
    Intuition and Awareness of Abstract Models: A Challenge for Realists.Dimitris Kilakos - 2018 - Philosophies 3 (1):3-0.
    It is plausible to think that, in order to actively employ models in their inquiries, scientists should be aware of their existence. The question is especially puzzling for realists in the case of abstract models, since it is not obvious how this is possible. Interestingly, though, this question has drawn little attention in the relevant literature. Perhaps the most obvious choice for a realist is appealing to intuition. In this paper, I argue that if scientific models were abstract entities, one (...)
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  16. Models, Conjectures and Exploration: An Analysis of Schelling's Checkerboard Model of Residential Segregation.N. Emrah Aydinonat - 2007 - Journal of Economic Methodology 14 (4):429-454.
    This paper analyses and explicates the explanatory characteristics of Schelling's checkerboard model of segregation. It argues that the explanation of emergence of segregation which is based on the checkerboard model is a partial potential (theoretical) explanation. Yet it is also argued that despite its partiality, the checkerboard model is valuable because it improves our chances to provide better explanations of particular exemplifications of residential segregation. The paper establishes this argument by way of examining the several ways in which the checkerboard (...)
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  17. Mental Models, Moral Imagination and System Thinking in the Age of Globalization.Patricia H. Werhane - 2008 - 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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  18.  60
    The E-Z Reader Model of Eye-Movement Control in Reading: Comparisons to Other Models.Erik D. Reichle, Keith Rayner & Alexander Pollatsek - 2003 - 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 of (...)
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  19.  30
    Models in the Geosciences.Alisa Bokulich & Naomi Oreskes - 2017 - In Lorenzo Magnani & Tommaso Wayne Bertolotti (eds.), Springer Handbook of Model-Based Science. Springer. pp. 891-911.
    The geosciences include a wide spectrum of disciplines ranging from paleontology to climate science, and involve studies of a vast range of spatial and temporal scales, from the deep-time history of microbial life to the future of a system no less immense and complex than the entire Earth. Modeling is thus a central and indispensable tool across the geosciences. Here, we review both the history and current state of model-based inquiry in the geosciences. Research in these fields makes use of (...)
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  20.  36
    Mental Models: An Alternative Evaluation of a Sensemaking Approach to Ethics Instruction.E. Brock Meagan, Vert Andrew, Kligyte Vykinta, P. Waples Ethan, T. Sevier Sydney & D. Mumford Michael - 2008 - 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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  21.  45
    MISSing the World: Models as Isolations, Representations, and Credible Worlds.Uskali Mäki - 2009 - Erkenntnis 70 (1):29-43.
    This article shows how the MISS account of models—as isolations and surrogate systems—accommodates and elaborates Sugden’s account of models as credible worlds and Hausman’s account of models as explorations. Theoretical models typically isolate by means of idealization, and they are representatives of some target system, which prompts issues of resemblance between the two to arise. Models as representations are constrained both ontologically (by their targets) and pragmatically (by the purposes and audiences of the modeller), and these relations are coordinated by (...)
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  22. Living with the Abstract: Realism and Models.Stathis Psillos - 2011 - 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 abandoned? Is (...)
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  23.  73
    Redundancy in Perceptual and Linguistic Experience: Comparing Feature-Based and Distributional Models of Semantic Representation.Brian Riordan & Michael N. Jones - 2011 - 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 compare the representations (...)
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  24.  32
    Minimal Models and the Generalized Ontic Conception of Scientific Explanation.Mark Povich - 2018 - British Journal for the Philosophy of Science 69 (1):117-137.
    Batterman and Rice ([2014]) argue that minimal models possess explanatory power that cannot be captured by what they call ‘common features’ approaches to explanation. Minimal models are explanatory, according to Batterman and Rice, not in virtue of accurately representing relevant features, but in virtue of answering three questions that provide a ‘story about why large classes of features are irrelevant to the explanandum phenomenon’ ([2014], p. 356). In this article, I argue, first, that a method (the renormalization group) they propose (...)
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  25.  23
    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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  26.  59
    Laboratory Models, Causal Explanation and Group Selection.James R. Griesemer & Michael J. Wade - 1988 - 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. But to (...)
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  27.  40
    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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  28. A Characterization of Permutation Models in Terms of Forcing.Eric J. Hall - 2002 - 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 not (...)
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  29.  70
    Do Role Models Matter? An Investigation of Role Modeling as an Antecedent of Perceived Ethical Leadership.Michael E. Brown & Linda K. Treviño - 2014 - Journal of Business Ethics 122 (4):1-12.
    Thus far, we know much more about the significant outcomes of perceived ethical leadership than we do about its antecedents. In this study, we focus on multiple types of ethical role models as antecedents of perceived ethical leadership. According to social learning theory, role models facilitate the acquisition of moral and other types of behavior. Yet, we do not know whether having had ethical role models influences follower perceptions of one’s ethical leadership and, if so, what kinds of role models (...)
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  30. On Minimal Models for Pure Calculi of Names.Piotr Kulicki - 2013 - 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 name (...)
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  31. Models as Make-Believe.Adam Toon - 2010 - 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, I demonstrate (...)
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  32.  42
    Learning to Learn Causal Models.Charles Kemp, Noah D. Goodman & Joshua B. Tenenbaum - 2010 - 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 the (...)
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  33.  10
    Tiger Stripes and Embodied Systems: Hegel on Markets and Models.David Kolb - 2018 - In Michael J. Thompson (ed.), Hegel's Metaphysics and the Philosophy of Politics. New York, USA: Routledge. pp. 286-300.
    From Hegel's philosophy of nature, this essay develops a critique of economic models and market society, based on Hegel's notion of what it takes for a formally described system to be embodied and real.
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  34. The Tool Box of Science: Tools for the Building of Models with a Superconductivity Example.Nancy Cartwright, Towfic Shomar & Mauricio Suárez - 1995 - Poznan Studies in the Philosophy of the Sciences and the Humanities 44:137-149.
    We call for a new philosophical conception of models in physics. Some standard conceptions take models to be useful approximations to theorems, that are the chief means to test theories. Hence the heuristics of model building is dictated by the requirements and practice of theory-testing. In this paper we argue that a theory-driven view of models can not account for common procedures used by scientists to model phenomena. We illustrate this thesis with a case study: the construction of one of (...)
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  35.  77
    Intrinsic Ethics Regarding Integrated Assessment Models for Climate Management.Erich Schienke, Seth Baum, Nancy Tuana, Kenneth Davis & Klaus Keller - 2011 - 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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  36. The Metaphysics of Causal Models: Where's the Biff?Toby Handfield, Charles R. Twardy, Kevin B. Korb & Graham Oppy - 2008 - 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 other cases (...)
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  37. On the Dangers of Making Scientific Models Ontologically Independent: Taking Richard Levins' Warnings Seriously.Rasmus Grønfeldt Winther - 2006 - 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 of these (...)
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  38.  92
    One Mechanism, Many Models: A Distributed Theory of Mechanistic Explanation.Eric Hochstein - 2016 - 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 models (...)
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  39.  28
    Realistic Realism About Unrealistic Models.Uskali Mäki - 2009 - In Harold Kincaid & Don Ross (eds.), The Oxford handbook of philosophy of economics. Oxford University Press.
    My philosophical intuitions are those of a scientific realist. In addition to being realist in its philosophical outlook, my philosophy of economics also aspires to be realistic in the sense of being descriptively adequate, or at least normatively non-utopian, about economics as a scientific discipline. The special challenge my philosophy of economics must meet is to provide a scientific realist account that is realistic of a discipline that deals with a complex subject matter and operates with highly unrealistic models. Unrealisticness (...)
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  40.  27
    Plausibility Versus Richness in Mechanistic Models.Raoul Gervais & Erik Weber - 2013 - 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 the (...)
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  41.  22
    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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  42. One Phenomenon, Many Models: Inconsistency and Complementarity.Margaret Morrison - 2011 - Studies in History and Philosophy of Science Part A 42 (2):342-351.
    The paper examines philosophical issues that arise in contexts where one has many different models for treating the same system. I show why in some cases this appears relatively unproblematic (models of turbulence) while others represent genuine difficulties when attempting to interpret the information that models provide (nuclear models). What the examples show is that while complementary models needn’t be a hindrance to knowledge acquisition, the kind of inconsistency present in nuclear cases is, since it is indicative of a lack (...)
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  43.  47
    Robustness and Sensitivity of Biological Models.Jani Raerinne - 2013 - Philosophical Studies 166 (2):285-303.
    The aim of this paper is to develop ideas about robustness analyses. I introduce a form of robustness analysis that I call sufficient parameter robustness, which has been neglected in the literature. I claim that sufficient parameter robustness is different from derivational robustness, the focus of previous research. My purpose is not only to suggest a new taxonomy of robustness, but also to argue that previous authors have concentrated on a narrow sense of robustness analysis, which they have inadequately distinguished (...)
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  44.  72
    Models, Robustness, and Non-Causal Explanation: A Foray Into Cognitive Science and Biology.Elizabeth Irvine - 2015 - Synthese 192 (12):3943-3959.
    This paper is aimed at identifying how a model’s explanatory power is constructed and identified, particularly in the practice of template-based modeling (Humphreys, Philos Sci 69:1–11, 2002; Extending ourselves: computational science, empiricism, and scientific method, 2004), and what kinds of explanations models constructed in this way can provide. In particular, this paper offers an account of non-causal structural explanation that forms an alternative to causal–mechanical accounts of model explanation that are currently popular in philosophy of biology and cognitive science. Clearly, (...)
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  45.  32
    Modeling Climate Policies: A Critical Look at Integrated Assessment Models.Mathias Frisch - 2013 - 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 of (...)
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  46.  14
    Models, Postulates, and Generalized Nomic Truth Approximation.Theo A. F. Kuipers - 2016 - Synthese 193 (10).
    The qualitative theory of nomic truth approximation, presented in Kuipers in his, in which ‘the truth’ concerns the distinction between nomic, e.g. physical, possibilities and impossibilities, rests on a very restrictive assumption, viz. that theories always claim to characterize the boundary between nomic possibilities and impossibilities. Fully recognizing two different functions of theories, viz. excluding and representing, this paper drops this assumption by conceiving theories in development as tuples of postulates and models, where the postulates claim to exclude nomic impossibilities (...)
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  47.  65
    Significance of Models of Computation, From Turing Model to Natural Computation.Gordana Dodig-Crnkovic - 2011 - Minds and Machines 21 (2):301-322.
    The increased interactivity and connectivity of computational devices along with the spreading of computational tools and computational thinking across the fields, has changed our understanding of the nature of computing. In the course of this development computing models have been extended from the initial abstract symbol manipulating mechanisms of stand-alone, discrete sequential machines, to the models of natural computing in the physical world, generally concurrent asynchronous processes capable of modelling living systems, their informational structures and dynamics on both symbolic and (...)
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  48.  44
    Understanding (With) Toy Models.Alexander Reutlinger, Dominik Hangleiter & Stephan Hartmann - 2016 - British Journal for the Philosophy of Science:axx005.
    Toy models are highly idealized and extremely simple models. Although they are omnipresent across scientific disciplines, toy models are a surprisingly under-appreciated subject in the philosophy of science. The main philosophical puzzle regarding toy models is that it is an unsettled question what the epistemic goal of toy modeling is. One promising proposal for answering this question is the claim that the epistemic goal of toy models is to provide individual scientists with understanding. The aim of this paper is to (...)
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  49.  38
    Mechanistic and Non-Mechanistic Varieties of Dynamical Models in Cognitive Science: Explanatory Power, Understanding, and the ‘Mere Description’ Worry.Raoul Gervais - 2015 - Synthese 192 (1):43-66.
    In the literature on dynamical models in cognitive science, two issues have recently caused controversy. First, what is the relation between dynamical and mechanistic models? I will argue that dynamical models can be upgraded to be mechanistic as well, and that there are mechanistic and non-mechanistic dynamical models. Second, there is the issue of explanatory power. Since it is uncontested the mechanistic models can explain, I will focus on the non-mechanistic variety of dynamical models. It is often claimed by proponents (...)
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  50.  20
    Social Constructivism, Mental Models, and Problems of Obedience.Patricia H. Werhane, Laura P. Hartman, Dennis Moberg, Elaine Englehardt, Michael Pritchard & Bidhan Parmar - 2011 - Journal of Business Ethics 100 (1):103 - 118.
    There are important synergies for the next generation of ethical leaders based on the alignment of modified or adjusted mental models. This entails a synergistic application of moral imagination through collaborative input and critique, rather than "me too" obedience. In this article, we will analyze the Milgram results using frameworks relating to mental models (Werhane et al., Profitable partnerships for poverty alleviation, 2009), as well as work by Moberg on "ethics blind spots'' (Organizational Studies 27(3): 413-428, 2006), and by Bazerman (...)
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