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

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  1.  6
    John K. Dagsvik & Stine Røine Hoff (2011). Justification of Functional Form Assumptions in Structural Models: Applications and Testing of Qualitative Measurement Axioms. [REVIEW] Theory and Decision 70 (2):215-254.
    In both theoretical and applied modeling in behavioral sciences, it is common to choose a mathematical specification of functional form and distribution of unobservables on grounds of analytic convenience without support from explicit theoretical postulates. This article discusses the issue of deriving particular qualitative hypotheses about functional form restrictions in structural models from intuitive theoretical axioms. In particular, we focus on a family of postulates known as dimensional invariance. Subsequently, we discuss how specific qualitative postulates can be reformulated so (...)
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  2.  50
    Alessandro Giordani & Luca Mari (2012). Measurement, Models, and Uncertainty. IEEE Transactions on Instrumentation and Measurement 61 (8):2144 - 2152.
    Against the tradition, which has considered measurement able to produce pure data on physical systems, the unavoidable role played by the modeling activity in measurement is increasingly acknowledged, particularly with respect to the evaluation of measurement uncertainty. This paper characterizes measurement as a knowledge-based process and proposes a framework to understand the function of models in measurement and to systematically analyze their influence in the production of measurement results and their interpretation. To this (...)
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  3. Nigel Guenole & Anna Brown (2014). The Consequences of Ignoring Measurement Invariance for Path Coefficients in Structural Equation Models. Frontiers in Psychology 5.
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  4. Christian Geiser, Jacob Bishop & Ginger Lockhart (2015). Collapsing Factors in Multitrait-Multimethod Models: Examining Consequences of a Mismatch Between Measurement Design and Model. Frontiers in Psychology 6.
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  5.  10
    Luca Mari, Valentina Lazzarotti & Raffaella Manzini (2009). Measurement in Soft Systems: Epistemological Framework and a Case Study. Measurement 42 (2):241-253.
    Measurement in soft systems generally cannot exploit physical sensors as data acquisition devices. The emphasis in this case is instead on how to choose the appropriate indicators and to combine their values so to obtain an overall result, interpreted as the value of a property, i.e., the measurand, for the system under analysis. This paper aims at discussing the epistemological conditions of the claim that such a process is a measurement, and performance evaluation is the case introduced to (...)
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  6.  5
    John Bickle (2008). Anna Alexandrova is an Assistant Professor in Philosophy at the University of Missouri St. Louis. Her Research Focuses on the Use of Formal Models for Explanation and Policy Making in Economics and Also on the Measurement of Happiness and Well-Being. Her Recent Papers Are Appearing in Philosophy of Science, Philosophical Psychology, and the Journal. [REVIEW] Economics and Philosophy 24:545-547.
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  7. Robert L. Causey (1970). Suppes Patrick. Measurement, Empirical Meaningfulness, and Three-Valued Logic. Measurement: Definitions and Theories, Edited by West Churchman C. And Ratoosh Philburn, Wiley John & Sons, Inc., New York, and Chapman & Hall, Limited, London 1959, Pp. 129–143.Suppes Patrick. Logics Appropriate to Empirical Theories. The Theory of Models, Proceedings of the 1963 International Symposium at Berkeley, Edited by Addison J. W., Henkin Leon, and Tarski Alfred, Studies in Logic and the Foundations of Mathematics, North-Holland Publishing Co., Amsterdam 1965, Pp. 364–375. [REVIEW] Journal of Symbolic Logic 35 (1):129-131.
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  8.  47
    E. Tal (2014). Making Time: A Study in the Epistemology of Measurement. British Journal for the Philosophy of Science (1):axu037.
    This article develops a model-based account of the standardization of physical measurement, taking the contemporary standardization of time as its central case-study. To standardize the measurement of a quantity, I argue, is to legislate the mode of application of a quantity-concept to a collection of exemplary artefacts. Legislation involves an iterative exchange between top-down adjustments to theoretical and statistical models regulating the application of a concept, and bottom-up adjustments to material artefacts in light of remaining gaps. The (...)
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  9. Paola Cantù (2010). The Role of Epistemological Models in Veronese's and Bettazzi's Theory of Magnitudes. In M. D'Agostino, G. Giorello, F. Laudisa, T. Pievani & C. Sinigaglia (eds.), New Essays in Logic and Philosophy of Science. College Publications
    The philosophy of mathematics has been accused of paying insufficient attention to mathematical practice: one way to cope with the problem, the one we will follow in this paper on extensive magnitudes, is to combine the `history of ideas' and the `philosophy of models' in a logical and epistemological perspective. The history of ideas allows the reconstruction of the theory of extensive magnitudes as a theory of ordered algebraic structures; the philosophy of models allows an investigation into the (...)
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  10. Alessandro Giordani & Luca Mari (2014). Modeling Measurement: Error and Uncertainty. In Marcel Boumans, Giora Hon & Arthur Petersen (eds.), Error and Uncertainty in Scientific Practice. Pickering & Chatto 79-96.
    In the last few decades the role played by models and modeling activities has become a central topic in the scientific enterprise. In particular, it has been highlighted both that the development of models constitutes a crucial step for understanding the world and that the developed models operate as mediators between theories and the world. Such perspective is exploited here to cope with the issue as to whether error-based and uncertainty-based modeling of measurement are incompatible, and (...)
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  11. Eran Tal (2013). Old and New Problems in Philosophy of Measurement. Philosophy Compass 8 (12):1159-1173.
    The philosophy of measurement studies the conceptual, ontological, epistemic, and technological conditions that make measurement possible and reliable. A new wave of philosophical scholarship has emerged in the last decade that emphasizes the material and historical dimensions of measurement and the relationships between measurement and theoretical modeling. This essay surveys these developments and contrasts them with earlier work on the semantics of quantity terms and the representational character of measurement. The conclusions highlight four characteristics of (...)
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  12.  35
    Michael Heidelberger, Models in Fluid Dynamics.
    In this paper, I would like to show that considering technological models as they arise in engineering disciplines can greatly enrich the philosophical perspective on models. In fluid mechanics, (at least) three types of models are distinguished: mathematical, computer and physical models. Very often, the choice of a particular mathematical, computer or physical model highly affects the type of solutions and the computational time needed for it. Technological models not only aim at a correct description (...)
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  13. Robyn M. Dawes & Bernard Corrigan (1974). Linear Models in Decision Making. Psychological Bulletin 81 (2):95-106.
    A review of the literature indicates that linear models are frequently used in situations in which decisions are made on the basis of multiple codable inputs. These models are sometimes used normatively to aid the decision maker, as a contrast with the decision maker in the clinical vs statistical controversy, to represent the decision maker "paramorphically" and to "bootstrap" the decision maker by replacing him with his representation. Examination of the contexts in which linear models have been (...)
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  14.  19
    Elisabeth A. Lloyd (2012). The Role of 'Complex' Empiricism in the Debates About Satellite Data and Climate Models. Studies in History and Philosophy of Science Part A 43 (2):390-401.
    climate scientists have been engaged in a decades-long debate over the standing of satellite measurements of the temperature trends of the atmosphere above the surface of the earth. This is especially significant because skeptics of global warming and the greenhouse effect have utilized this debate to spread doubt about global climate models used to predict future states of climate. I use this case from an under-studied science to illustrate two distinct philosophical approaches to the relation among data, scientists, (...), models, and theory. I argue that distinguishing between 'direct' empiricist and 'complex' empiricist approaches helps us understand and analyze this important scientific episode. I also introduce a complex empiricist account of testing and evaluation, and contrast it with the basic Hypothetico-Deductive approach to the climate models used by the direct empiricists. This more developed complex empiricist apporach will serve philosophy of science well, as computational models become more wide-spread in the sciences. (shrink)
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  15. Robyn M. Dawes (1979). The Robust Beauty of Improper Linear Models in Decision Making. American Psychologist 34 (7):571-582.
    Proper linear models are those in which predictor variables are given weights such that the resulting linear composite optimally predicts some criterion of interest; examples of proper linear models are standard regression analysis, discriminant function analysis, and ridge regression analysis. Research summarized in P. Meehl's book on clinical vs statistical prediction and research stimulated in part by that book indicate that when a numerical criterion variable is to be predicted from numerical predictor variables, proper linear models outperform (...)
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  16.  17
    Ron A. Shapira (1999). Fuzzy Measurement in the Mishnah and the Talmud. Artificial Intelligence and Law 7 (2-3):273-288.
    I discuss the attitude of Jewish law sources from the 2nd–:5th centuries to the imprecision of measurement. I review a problem that the Talmud refers to, somewhat obscurely, as impossible reduction. This problem arises when a legal rule specifies an object by referring to a maximized measurement function, e.g., when a rule applies to the largest part of a divided whole, or to the first incidence that occurs, etc. A problem that is often mentioned is whether there might (...)
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  17. Margaret Morrison (2009). Models, Measurement and Computer Simulation: The Changing Face of Experimentation. Philosophical Studies 143 (1):33 - 57.
    The paper presents an argument for treating certain types of computer simulation as having the same epistemic status as experimental measurement. While this may seem a rather counterintuitive view it becomes less so when one looks carefully at the role that models play in experimental activity, particularly measurement. I begin by discussing how models function as “measuring instruments” and go on to examine the ways in which simulation can be said to constitute an experimental activity. By (...)
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  18.  1
    Eran Tal (2016). Making Time: A Study in the Epistemology of Measurement. British Journal for the Philosophy of Science 67 (1):297-335.
    This article develops a model-based account of the standardization of physical measurement, taking the contemporary standardization of time as its central case study. To standardize the measurement of a quantity, I argue, is to legislate the mode of application of a quantity concept to a collection of exemplary artefacts. Legislation involves an iterative exchange between top-down adjustments to theoretical and statistical models regulating the application of a concept, and bottom-up adjustments to material artefacts in light of remaining (...)
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  19.  10
    Peter Rodenburg (2005). Models as Measuring Instruments: Measurement of Duration Dependence of Unemployment. Journal of Economic Methodology 12 (3):407-431.
    Nancy Cartwright views models as blueprints for nomological machines? machines that, if properly shielded, generate law?like behaviour or regularities. Marcel Boumans has argued that we can look for devices inside models, which enable us to measure aspects of these regularities. Therefore, if models do produce regular behaviour, they might perhaps generate numbers about phenomena in the world, provided we can locate a good measuring device in the model. How do they do this? Models are often understood (...)
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  20.  2
    Axel Buchner & Edgar Erdfelder (1996). On Assumptions of, Relations Between, and Evaluations of Some Process Dissociation Measurement Models. Consciousness and Cognition 5 (4):581-594.
    In this article, we analyze both M. J. Wainwright and E. M. Reingold's view of the process dissociation measurement models presented by A. Buchner, E. Erdfelder, and B. Vaterrodt-Plunnecke and their suggestions on that topic. This analysis reveals a number of problems in Wainwright and Reingold's approach. Some of these problems are more subtle than others, but they are nevertheless consequential. Thus, researchers working with the process dissociation procedure should be aware of these problems.
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  21.  17
    Hubert M. Blalock (1986). Multiple Causation, Indirect Measurement and Generalizability in the Social Sciences. Synthese 68 (1):13-36.
    The fact that causal laws in the social sciences are most realistically expressed as both multivariate and stochastic has a number of very important implications for indirect measurement and generalizability. It becomes difficult to link theoretical definitions of general constructs in a one-to-one relationship to research operations, with the result that there is conceptual slippage in both experimental and nonexperimental research. It is argued that problems of this nature can be approached by developing specific multivariate causal models that (...)
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  22.  7
    Hubert M. Blalock Jr (1986). Multiple Causation, Indirect Measurement and Generalizability in the Social Sciences. Synthese 68 (1):13 - 36.
    The fact that causal laws in the social sciences are most realistically expressed as both multivariate and stochastic has a number of very important implications for indirect measurement and generalizability. It becomes difficult to link theoretical definitions of general constructs in a one-to-one relationship to research operations, with the result that there is conceptual slippage in both experimental and nonexperimental research. It is argued that problems of this nature can be approached by developing specific multivariate causal models that (...)
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  23. Michael B. Mensky (1997). Decoherence in Continuous Measurements: From Models to Phenomenology. [REVIEW] Foundations of Physics 27 (12):1637-1654.
    Decoherence is the name for the complex of phenomena leading to appearance of classical features of quantum systems. In the present paper decoherence in continuous measurements is analyzed with the help of restricted path integrals (RPI) and (equivalently in simple cases) complex Hamiltonians. A continuous measurement results in a readout giving information in the classical form on the evolution of the measured quantum system. The quantum features of the system reveal themselves in the variation of possible measurement readouts. (...)
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  24. Axel Buchner & Werner Wippich (1996). Investigating Fame Judgments: On the Generality of Hypotheses, Conclusions, and Measurement Models. Consciousness and Cognition 5 (1-2):226-231.
    In this article, we try to clarify some of the issues raised by S. C. Draine, A. G. Greenwald, and M. R. Banaji concerning our investigation into the gender bias in fame judgments . First, we did not test the general hypothesis and did not draw the general conclusion that Drain et al. suggest we did. Second, we did not reject M. R. Banaji and A. G. Greenwald's assumptions about the familiarity of male and female names in the fame judgment (...)
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  25.  10
    Emiliano Ippoliti, Fabio Sterpetti & Thomas Nickles (eds.) (2016). Models and Inferences in Science. Springer.
    The book answers long-standing questions on scientific modeling and inference across multiple perspectives and disciplines, including logic, mathematics, physics and medicine. The different chapters cover a variety of issues, such as the role models play in scientific practice; the way science shapes our concept of models; ways of modeling the pursuit of scientific knowledge; the relationship between our concept of models and our concept of science. The book also discusses models and scientific explanations; models in (...)
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  26.  31
    GianCarlo Ghirardi (2008). Reconsidering Mermin's “In Praise of Measurement”. Foundations of Physics 38 (11):1011-1019.
    We critically analyze a recent paper by D. Mermin and we compare his statements with Bell’s position on the problems he is discussing.
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  27.  11
    Anna Conte & M. Vittoria Levati (2014). Use of Data on Planned Contributions and Stated Beliefs in the Measurement of Social Preferences. Theory and Decision 76 (2):201-223.
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  28. 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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  29. Mathilde G. E. Verdam & Frans J. Oort (2014). Measurement Bias Detection with Kronecker Product Restricted Models for Multivariate Longitudinal Data: An Illustration with Health-Related Quality of Life Data From Thirteen Measurement Occasions. Frontiers in Psychology 5.
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  30.  43
    Yuh-Jia Chen & Thomas Li-Ping Tang (2006). Attitude Toward and Propensity to Engage in Unethical Behavior: Measurement Invariance Across Major Among University Students. [REVIEW] Journal of Business Ethics 69 (1):77 - 93.
    This research examines business and psychology students’ attitude toward unethical behavior (measured at Time 1) and their propensity to engage in unethical behavior (measured at Time 1 and at Time 2, 4 weeks later) using a 15-item Unethical Behavior measure with five Factors: Abuse Resources, Not Whistle Blowing, Theft, Corruption, and Deception. Results suggested that male students had stronger unethical attitudes and had higher propensity to engage in unethical behavior than female students. Attitude at Time 1 predicted Propensity at Time (...)
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  31.  94
    Gareth Ernest Boardman (2013). Addressing the Conflict Between Relativity and Quantum Theory: Models, Measurement and the Markov Property. Cosmos and History: The Journal of Natural and Social Philosophy 9 (2):86-115.
    Twenty-first century science faces a dilemma. Two of its well-verified foundation stones - relativity and quantum theory - have proven inconsistent. Resolution of the conflict has resisted improvements in experimental precision leaving some to believe that some fundamental understanding in our world-view may need modification or even radical reform. Employment of the wave-front model of electrodynamics, as a propagation process with a Markov property, may offer just such a clarification.
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  32.  23
    Kristin Shrader-Frechette, Measurement Problems and Florida Panther Models.
    Conservation planning is only as good as the science on which it relies. This paper evaluates the science underlying the least-cost-path model, developed by Meegan and Maehr (2002) , for the Florida panther, Puma concolor coryi. It also assesses the resulting claim that private lands in central Florida are desirable for panther colonization (Maehr et al. 2002a , p. 187; Maehr 2001 , pp. 3–4; Maehr and Deason 2002 , p. 400). The paper argues that panther conservation planning, as proposed (...)
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  33.  11
    Jean-François Bonnefon & Stéphane Vautier (2008). Defective Truth Tables and Falsifying Cards: Two Measurement Models Yield No Evidence of an Underlying Fleshing-Out Propensity. Thinking and Reasoning 14 (3):231 – 243.
    Using a latent variable modelling strategy we study individual differences in patterns of answers to the selection task and to the truth table task. Specifically we investigate the prediction of mental model theory according to which the individual tendency to select the false consequent card (in the selection task) is negatively correlated with the tendency to judge the false antecedent cases as irrelevant (in the truth table task). We fit a psychometric model to two large samples ( N = 486, (...)
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  34.  7
    Stéphane Vautier & Jean-François Bonnefon (2008). Defective Truth Tables and Falsifying Cards: Two Measurement Models Yield No Evidence of an Underlying Fleshing-Out Propensity. Thinking and Reasoning 14 (3):231-243.
    Using a latent variable modelling strategy we study individual differences in patterns of answers to the selection task and to the truth table task. Specifically we investigate the prediction of mental model theory according to which the individual tendency to select the false consequent card (in the selection task) is negatively correlated with the tendency to judge the false antecedent cases as irrelevant (in the truth table task). We fit a psychometric model to two large samples ( N = 486, (...)
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  35.  4
    Jean-Fran (2008). Defective Truth Tables and Falsifying Cards: Two Measurement Models Yield No Evidence of an Underlying Fleshing-Out Propensity. Thinking and Reasoning 14 (3):231 – 243.
    Using a latent variable modelling strategy we study individual differences in patterns of answers to the selection task and to the truth table task. Specifically we investigate the prediction of mental model theory according to which the individual tendency to select the false consequent card (in the selection task) is negatively correlated with the tendency to judge the false antecedent cases as irrelevant (in the truth table task). We fit a psychometric model to two large samples ( N = 486, (...)
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  36.  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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  37.  1
    Ekaterina Svetlova & Vanessa Dirksen (2014). Models at Work—Models in Decision Making. Science in Context 27 (4):561-577.
    In this topical section, we highlight the next step of research on modeling aiming to contribute to the emerging literature that radically refrains from approaching modeling as a scientific endeavor. Modeling surpasses “doing science” because it is frequently incorporated into decision-making processes in politics and management, i.e., areas which are not solely epistemically oriented. We do not refer to the production of models in academia for abstract or imaginary applications in practical fields, but instead highlight the real entwinement of (...)
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  38. Adrian P. Banks & Lynne J. Millward (2009). Distributed Mental Models: Mental Models in Distributed Cognitive Systems. Journal of Mind and Behavior 30 (4):249-266.
    The function of groups as information processors is increasingly being recognised in a number of theories of group cognition. A theme of many of these is an emphasis on sharing cognition. This paper extends current conceptualisations of groups by critiquing the focus on shared cognition and emphasising the distribution of cognition in groups. In particular, it develops an account of the distribution of one cognitive construct, mental models. Mental models have been chosen as a focus because they are (...)
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  39.  34
    James R. Griesemer (1990). Modeling in the Museum: On the Role of Remnant Models in the Work of Joseph Grinnell. [REVIEW] Biology and Philosophy 5 (1):3-36.
    Accounts of the relation between theories and models in biology concentrate on mathematical models. In this paper I consider the dual role of models as representations of natural systems and as a material basis for theorizing. In order to explicate the dual role, I develop the concept of a remnant model, a material entity made from parts of the natural system(s) under study. I present a case study of an important but neglected naturalist, Joseph Grinnell, to illustrate (...)
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  40.  16
    Raoul Gervais (2015). Mechanistic and Non-Mechanistic Varieties of Dynamical Models in Cognitive Science: Explanatory Power, Understanding, and the ‘Mere Description’ Worry. 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. (...)
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  41.  5
    Petr Hájek (2010). On Witnessed Models in Fuzzy Logic III - Witnessed Gödel Logics. Mathematical Logic Quarterly 56 (2):171-174.
    Gödel logics with truth sets being countable closed subsets of the unit real interval containing 0 and 1 are studied under their usual semantics and under the witnessed semantics, the latter admitting only models in which the truth value of each universally quantified formula is the minimum of truth values of its instances and dually for existential quantification and maximum. An infinite system of such truth sets is constructed such that under the usual semantics the corresponding logics have pairwise (...)
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  42.  16
    David Ludwig (2013). Mediating Objects. Scientific and Public Functions of Models in Nineteenth-Century Biology. History and Philosophy of the Life Sciences 35 (2).
    The aim of this article is to examine the scientific and public functions of two- andthree-dimensional models in the context of three episodes from nineteenth-century biology. Iargue that these models incorporate both data and theory by presenting theoretical assumptions inthe light of concrete data or organizing data through theoretical assumptions. Despite their diverseroles in scientific practice, they all can be characterized as mediators between data and theory.Furthermore, I argue that these different mediating functions often reflect their different audiencesthat (...)
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  43.  4
    Lara Huber & Lara Kutschenko (2009). Medicine in a Neurocentric World: About the Explanatory Power of Neuroscientific Models in Medical Research and Practice. [REVIEW] Medicine Studies 1 (4):307-313.
    Medicine in a Neurocentric World: About the Explanatory Power of Neuroscientific Models in Medical Research and Practice Content Type Journal Article Category Editorial Notes Pages 307-313 DOI 10.1007/s12376-009-0036-2 Authors Lara Huber, University Medical Center of the Johannes Gutenberg University Mainz Institute for History, Philosophy and Ethics of Medicine Am Pulverturm 13 55131 Mainz Germany Lara K. Kutschenko, University Medical Center of the Johannes Gutenberg University Mainz Institute for History, Philosophy and Ethics of Medicine Am Pulverturm 13 55131 Mainz Germany (...)
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  44. Jay Odenbaugh, Models in Biology.
    In recent years, there has much attention given by philosophers to the ubiquitous role of models and modeling in the biological sciences. Philosophical debates has focused on several areas of discussion. First, what are models in the biological sciences? The term ‘model’ is applied to mathematical structures, graphical displays, computer simulations, and even concrete organisms. Is there an account which unifies these disparate structures? Second, scientists routinely distinguish between theories and models; however, this distinction is more difficult (...)
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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.  4
    Liam James Heaphy (2015). The Role of Climate Models in Adaptation Decision-Making: The Case of the UK Climate Projections 2009. European Journal for Philosophy of Science 5 (2):233-257.
    When attendant to the agency of models and the general context in which they perform, climate models can be seen as instrumental policy tools that may be evaluated in terms of their adequacy for purpose. In contrast, when analysed independently of their real-world usage for informing decision-making, the tendency can be to prioritise their representative role rather than their instrumental role. This paper takes as a case study the development of the UK Climate Projections 2009 in relation to (...)
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  47. Jay Odenbaugh (2005). Idealized, Inaccurate but Successful: A Pragmatic Approach to Evaluating Models in Theoretical Ecology. [REVIEW] Biology and Philosophy 20 (2-3):231-255.
    Ecologists attempt to understand the diversity of life with mathematical models. Often, mathematical models contain simplifying idealizations designed to cope with the blooming, buzzing confusion of the natural world. This strategy frequently issues in models whose predictions are inaccurate. Critics of theoretical ecology argue that only predictively accurate models are successful and contribute to the applied work of conservation biologists. Hence, they think that much of the mathematical work of ecologists is poor science. Against this view, (...)
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  48.  42
    Chuang Liu, Fictional Models in Science.
    In this paper, I begin with a discussion of Giere’s recent work arguing against taking models as works of fiction. I then move on to explore a spectrum of scientific models that goes from the obviously fictional to the not so obviously fictional. And then I discuss the modeling of the unobservable and make a case for the idea that despite difficulties of defining them, unobservable systems are modeled in a fundamentally different way than the observable systems. While (...)
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  49.  17
    Răzvan Diaconescu & Marius Petria (2010). Saturated Models in Institutions. Archive for Mathematical Logic 49 (6):693-723.
    Saturated models constitute one of the powerful methods of conventional model theory, with many applications. Here we develop a categorical abstract model theoretic approach to saturated models within the theory of institutions. The most important consequence is that the method of saturated models becomes thus available to a multitude of logical systems from logic or from computing science. In this paper we define the concept of saturated model at an abstract institution-independent level and develop the fundamental existence (...)
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  50. 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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