Results for 'modeling'

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  1. Modeling without models.Arnon Levy - 2015 - Philosophical Studies 172 (3):781-798.
    Modeling is an important scientific practice, yet it raises significant philosophical puzzles. Models are typically idealized, and they are often explored via imaginative engagement and at a certain “distance” from empirical reality. These features raise questions such as what models are and how they relate to the world. Recent years have seen a growing discussion of these issues, including a number of views that treat modeling in terms of indirect representation and analysis. Indirect views treat the model as (...)
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  2.  67
    Modeling causal structures: Volterra’s struggle and Darwin’s success.Raphael Scholl & Tim Räz - 2013 - European Journal for Philosophy of Science 3 (1):115-132.
    The Lotka–Volterra predator-prey-model is a widely known example of model-based science. Here we reexamine Vito Volterra’s and Umberto D’Ancona’s original publications on the model, and in particular their methodological reflections. On this basis we develop several ideas pertaining to the philosophical debate on the scientific practice of modeling. First, we show that Volterra and D’Ancona chose modeling because the problem in hand could not be approached by more direct methods such as causal inference. This suggests a philosophically insightful (...)
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  3. Experimental Modeling in Biology: In Vivo Representation and Stand-ins As Modeling Strategies.Marcel Weber - 2014 - Philosophy of Science 81 (5):756-769.
    Experimental modeling in biology involves the use of living organisms (not necessarily so-called "model organisms") in order to model or simulate biological processes. I argue here that experimental modeling is a bona fide form of scientific modeling that plays an epistemic role that is distinct from that of ordinary biological experiments. What distinguishes them from ordinary experiments is that they use what I call "in vivo representations" where one kind of causal process is used to stand in (...)
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  4.  89
    Modeling without representation.Alistair M. C. Isaac - 2013 - Synthese 190 (16):3611-3623.
    How can mathematical models which represent the causal structure of the world incompletely or incorrectly have any scientific value? I argue that this apparent puzzle is an artifact of a realist emphasis on representation in the philosophy of modeling. I offer an alternative, pragmatic methodology of modeling, inspired by classic papers by modelers themselves. The crux of the view is that models developed for purposes other than explanation may be justified without reference to their representational properties.
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  5.  53
    Computational modeling in philosophy: introduction to a topical collection.Simon Scheller, Christoph Merdes & Stephan Hartmann - 2022 - Synthese 200 (2):1-10.
    Computational modeling should play a central role in philosophy. In this introduction to our topical collection, we propose a small topology of computational modeling in philosophy in general, and show how the various contributions to our topical collection fit into this overall picture. On this basis, we describe some of the ways in which computational models from other disciplines have found their way into philosophy, and how the principles one found here still underlie current trends in the field. (...)
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  6. Optimality modeling in a suboptimal world.Angela Potochnik - 2009 - Biology and Philosophy 24 (2):183-197.
    The fate of optimality modeling is typically linked to that of adaptationism: the two are thought to stand or fall together (Gould and Lewontin, Proc Relig Soc Lond 205:581–598, 1979; Orzack and Sober, Am Nat 143(3):361–380, 1994). I argue here that this is mistaken. The debate over adaptationism has tended to focus on one particular use of optimality models, which I refer to here as their strong use. The strong use of an optimality model involves the claim that selection (...)
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  7. Perspectival Modeling.Michela Massimi - 2018 - Philosophy of Science 85 (3):335-359.
    The goal of this article is to address the problem of inconsistent models and the challenge it poses for perspectivism. I analyze the argument, draw attention to some hidden premises behind it, and deflate them. Then I introduce the notion of perspectival models as a distinctive class of modeling practices whose primary function is exploratory. I illustrate perspectival modeling with two examples taken from contemporary high-energy physics at the Large Hadron Collider at the European Organization for Nuclear Research, (...)
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  8.  84
    Computational Modeling in Cognitive Science: A Manifesto for Change.Caspar Addyman & Robert M. French - 2012 - Topics in Cognitive Science 4 (3):332-341.
    Computational modeling has long been one of the traditional pillars of cognitive science. Unfortunately, the computer models of cognition being developed today have not kept up with the enormous changes that have taken place in computer technology and, especially, in human-computer interfaces. For all intents and purposes, modeling is still done today as it was 25, or even 35, years ago. Everyone still programs in his or her own favorite programming language, source code is rarely made available, accessibility (...)
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  9.  57
    Modeling without Mathematics.Martin Thomson-Jones - 2012 - Philosophy of Science 79 (5):761-772.
    Inquiries into the nature of scientific modeling have tended to focus their attention on mathematical models and, relatedly, to think of nonconcrete models as mathematical structures. The arguments of this article are arguments for rethinking both tendencies. Nonmathematical models play an important role in the sciences, and our account of scientific modeling must accommodate that fact. One key to making such accommodations, moreover, is to recognize that one kind of thing we use the term ‘model’ to refer to (...)
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  10.  67
    Wayward Modeling: Population Genetics and Natural Selection.Bruce Glymour - 2006 - Philosophy of Science 73 (4):369-389.
    Since the introduction of mathematical population genetics, its machinery has shaped our fundamental understanding of natural selection. Selection is taken to occur when differential fitnesses produce differential rates of reproductive success, where fitnesses are understood as parameters in a population genetics model. To understand selection is to understand what these parameter values measure and how differences in them lead to frequency changes. I argue that this traditional view is mistaken. The descriptions of natural selection rendered by population genetics models are (...)
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  11. Causal Modeling Semantics for Counterfactuals with Disjunctive Antecedents.Giuliano Rosella & Jan Sprenger - manuscript
    Causal Modeling Semantics (CMS, e.g., Galles and Pearl 1998; Pearl 2000; Halpern 2000) is a powerful framework for evaluating counterfactuals whose antecedent is a conjunction of atomic formulas. We extend CMS to an evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to counterfactuals whose antecedent is an arbitrary Boolean combination of atomic formulas. Our main idea is to assign a probability to a counterfactual (A ∨ B) > C at a causal model M as a (...)
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  12.  4
    Modeling Misretrieval and Feature Substitution in Agreement Attraction: A Computational Evaluation.Dario Paape, Serine Avetisyan, Sol Lago & Shravan Vasishth - 2021 - Cognitive Science 45 (8):e13019.
    We present computational modeling results based on a self‐paced reading study investigating number attraction effects in Eastern Armenian. We implement three novel computational models of agreement attraction in a Bayesian framework and compare their predictive fit to the data using k‐fold cross‐validation. We find that our data are better accounted for by an encoding‐based model of agreement attraction, compared to a retrieval‐based model. A novel methodological contribution of our study is the use of comprehension questions with open‐ended responses, so (...)
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  13.  14
    Modeling the Structure and Dynamics of Semantic Processing.Armand S. Rotaru, Gabriella Vigliocco & Stefan L. Frank - 2018 - Cognitive Science 42 (8):2890-2917.
    The contents and structure of semantic memory have been the focus of much recent research, with major advances in the development of distributional models, which use word co‐occurrence information as a window into the semantics of language. In parallel, connectionist modeling has extended our knowledge of the processes engaged in semantic activation. However, these two lines of investigation have rarely been brought together. Here, we describe a processing model based on distributional semantics in which activation spreads throughout a semantic (...)
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  14. Modeling practical thinking.Matthew Mosdell - 2018 - Mind and Language 34 (4):445-464.
    Intellectualists about knowledge how argue that knowing how to do something is knowing the content of a proposition (i.e, a fact). An important component of this view is the idea that propositional knowledge is translated into behavior when it is presented to the mind in a peculiarly practical way. Until recently, however, intellectualists have not said much about what it means for propositional knowledge to be entertained under thought's practical guise. Carlotta Pavese fills this gap in the intellectualist view by (...)
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  15.  11
    Computational Modeling of Cognition and Behavior.Simon Farrell & Stephan Lewandowsky - 2017 - Cambridge University Press.
    Computational modeling is now ubiquitous in psychology, and researchers who are not modelers may find it increasingly difficult to follow the theoretical developments in their field. This book presents an integrated framework for the development and application of models in psychology and related disciplines. Researchers and students are given the knowledge and tools to interpret models published in their area, as well as to develop, fit, and test their own models. Both the development of models and key features of (...)
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  16.  5
    The Modeling of Nature: Philosophy of Science and Philosophy of Nature in Synthesis.William A. Wallace - 1996 - Catholic University of Amer Press.
    The Modeling of Nature provides an excellent introduction to the fundamentals of natural philosophy, psychology, logic, and epistemology.
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  17.  97
    Modeling the Emergence of Lexicons in Homesign Systems.Russell Richie, Charles Yang & Marie Coppola - 2014 - Topics in Cognitive Science 6 (1):183-195.
    It is largely acknowledged that natural languages emerge not just from human brains but also from rich communities of interacting human brains (Senghas, ). Yet the precise role of such communities and such interaction in the emergence of core properties of language has largely gone uninvestigated in naturally emerging systems, leaving the few existing computational investigations of this issue at an artificial setting. Here, we take a step toward investigating the precise role of community structure in the emergence of linguistic (...)
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  18. Optimality modeling and explanatory generality.Angela Potochnik - 2007 - Philosophy of Science 74 (5):680-691.
    The optimality approach to modeling natural selection has been criticized by many biologists and philosophers of biology. For instance, Lewontin (1979) argues that the optimality approach is a shortcut that will be replaced by models incorporating genetic information, if and when such models become available. In contrast, I think that optimality models have a permanent role in evolutionary study. I base my argument for this claim on what I think it takes to best explain an event. In certain contexts, (...)
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  19. Teleosemantic modeling of cognitive representations.Marc Artiga - 2016 - Biology and Philosophy 31 (4):483-505.
    Naturalistic theories of representation seek to specify the conditions that must be met for an entity to represent another entity. Although these approaches have been relatively successful in certain areas, such as communication theory or genetics, many doubt that they can be employed to naturalize complex cognitive representations. In this essay I identify some of the difficulties for developing a teleosemantic theory of cognitive representations and provide a strategy for accommodating them: to look into models of signaling in evolutionary game (...)
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  20.  11
    Modeling the Development of Children's Use of Optional Infinitives in Dutch and English Using MOSAIC.Daniel Freudenthal, Julian M. Pine & Fernand Gobet - 2006 - Cognitive Science 30 (2):277-310.
    In this study we use a computational model of language learning called model of syntax acquisition in children (MOSAIC) to investigate the extent to which the optional infinitive (OI) phenomenon in Dutch and English can be explained in terms of a resource-limited distributional analysis of Dutch and English child-directed speech. The results show that the same version of MOSAIC is able to simulate changes in the pattern of finiteness marking in 2 children learning Dutch and 2 children learning English as (...)
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  21. Modeling Truth.Paul Teller - manuscript
    Many in philosophy understand truth in terms of precise semantic values, true propositions. Following Braun and Sider, I say that in this sense almost nothing we say is, literally, true. I take the stand that this account of truth nonetheless constitutes a vitally useful idealization in understanding many features of the structure of language. The Fregean problem discussed by Braun and Sider concerns issues about application of language to the world. In understanding these issues I propose an alternative modeling (...)
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  22. Modeling and experimenting.Isabelle Peschard - 2009 - In Paul Humphreys & Cyrille Imbert (eds.), Models, Simulations, and Representations. Routledge.
    Experimental activity is traditionally identified with testing the empirical implications or numerical simulations of models against data. In critical reaction to the ‘tribunal view’ on experiments, this essay will show the constructive contribution of experimental activity to the processes of modeling and simulating. Based on the analysis of a case in fluid mechanics, it will focus specifically on two aspects. The first is the controversial specification of the conditions in which the data are to be obtained. The second is (...)
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  23.  4
    Modeling the relationship between perceived service quality, tourist satisfaction, and tourists’ behavioral intentions amid COVID-19 pandemic: Evidence of yoga tourists’ perspectives.Ahmed Hassan Abdou, Shaimaa Abo Khanger Mohamed, Ayman Ahmed Farag Khalil, Azzam Ibrahem Albakhit & Ali Jukhayer Nader Alarjani - 2022 - Frontiers in Psychology 13:1003650.
    PurposeThis study aims to investigate the impact of perceived service quality on tourist satisfaction and behavioral intentions and explore the potential mediating role of tourist satisfaction in the relationship between service quality and behavioral intentions in the yoga tourism context during the COVID-19 pandemic. Further, this is to examine to what extent yoga tourist satisfaction directly affects their behavioral intentions.Design/methodology/approachBased on a review of literature, the study proposes a conceptual model to test four hypothesized relationships among the constructs of perceived (...)
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    Causal modeling: New directions for statistical explanation.Gurol Irzik & Eric Meyer - 1987 - Philosophy of Science 54 (4):495-514.
    Causal modeling methods such as path analysis, used in the social and natural sciences, are also highly relevant to philosophical problems of probabilistic causation and statistical explanation. We show how these methods can be effectively used (1) to improve and extend Salmon's S-R basis for statistical explanation, and (2) to repair Cartwright's resolution of Simpson's paradox, clarifying the relationship between statistical and causal claims.
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  25. Computational Modeling as a Philosophical Methodology.Patrick Grim - 2004 - In Luciano Floridi (ed.), The Blackwell Guide to the Philosophy of Computing and Information. Oxford, UK: Blackwell. pp. 337–349.
    Since the sixties, computational modeling has become increasingly important in both the physical and the social sciences, particularly in physics, theoretical biology, sociology, and economics. Sine the eighties, philosophers too have begun to apply computational modeling to questions in logic, epistemology, philosophy of science, philosophy of mind, philosophy of language, philosophy of biology, ethics, and social and political philosophy. This chapter analyzes a selection of interesting examples in some of those areas.
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  26. Modeling and corpus methods in experimental philosophy.Louis Chartrand - 2022 - Philosophy Compass 17 (6).
    Research in experimental philosophy has increasingly been turning to corpus methods to produce evidence for empirical claims, as they open up new possibilities for testing linguistic claims or studying concepts across time and cultures. The present article reviews the quasi-experimental studies that have been done using textual data from corpora in philosophy, with an eye for the modeling and experimental design that enable statistical inference. I find that most studies forego comparisons that could control for confounds, and that only (...)
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  27.  31
    Mathematical Modeling in the Social Sciences.Paul Humphreys - 2003 - In Stephen P. Turner & Paul A. Roth (eds.), The Blackwell Guide to the Philosophy of the Social Sciences. Oxford, UK: Blackwell. pp. 166–184.
    This chapter contains sections titled: Why Use Mathematical Models? Theory‐based Models Data‐based Modeling Computational Approaches Conclusions Notes.
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  28. Modeling Measurement: Error and Uncertainty.Alessandro Giordani & Luca Mari - 2014 - In Marcel Boumans, Giora Hon & Arthur Petersen (eds.), Error and Uncertainty in Scientific Practice. Pickering & Chatto. pp. 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 thus alternative (...)
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  29.  13
    Modeling intentional agency: a neo-Gricean framework.Matti Sarkia - 2021 - Synthese 199 (3-4):7003-7030.
    This paper analyzes three contrasting strategies for modeling intentional agency in contemporary analytic philosophy of mind and action, and draws parallels between them and similar strategies of scientific model-construction. Gricean modeling involves identifying primitive building blocks of intentional agency, and building up from such building blocks to prototypically agential behaviors. Analogical modeling is based on picking out an exemplary type of intentional agency, which is used as a model for other agential types. Theoretical modeling involves reasoning (...)
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  30.  20
    Modeling the Instructional Effectiveness of Responsible Conduct of Research Education: A Meta-Analytic Path-Analysis.Logan L. Watts, Tyler J. Mulhearn, Kelsey E. Medeiros, Logan M. Steele, Shane Connelly & Michael D. Mumford - 2017 - Ethics and Behavior 27 (8):632-650.
    Predictive modeling in education draws on data from past courses to forecast the effectiveness of future courses. The present effort sought to identify such a model of instructional effectiveness in scientific ethics. Drawing on data from 235 courses in the responsible conduct of research, structural equation modeling techniques were used to test a predictive model of RCR course effectiveness. Fit statistics indicated the model fit the data well, with the instructional characteristics included in the model explaining approximately 85% (...)
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  31.  21
    Modeling behavioral adaptations.Colin W. Clark - 1991 - Behavioral and Brain Sciences 14 (1):85-93.
    Optimization models have often been useful in attempting to understand the adaptive significance of behavioral traits. Originally such models were applied to isolated aspects of behavior, such as foraging, mating, or parental behavior. In reality, organisms live in complex, ever-changing environments, and are simultaneously concerned with many behavioral choices and their consequences. This target article describes a dynamic modeling technique that can be used to analyze behavior in a unified way. The technique has been widely used in behavioral studies (...)
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  32. Modeling mechanisms.Stuart Glennan - 2005 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 36 (2):443-464.
    Philosophers of science increasingly believe that much of science is concerned with understanding the mechanisms responsible for the production of natural phenomena. An adequate understanding of scientific research requires an account of how scientists develop and test models of mechanisms. This paper offers a general account of the nature of mechanical models, discussing the representational relationship that holds between mechanisms and their models as well as the techniques that can be used to test and refine such models. The analysis is (...)
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  33. Modeling Bounded Rationality.Ariel Rubinstein - 1998 - MIT Press.
    p. cm. — (Zeuthen lecture book series) Includes bibliographical references (p. ) and index. ISBN 0-262-18187-8 (hardcover : alk. paper). — ISBN 0-262-68100-5 (pbk. : alk. paper) 1. Decision-making. 2. Economic man. 3. Game theory. 4. Rational expectations (Economic theory) I. Title. II. Series.
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  34.  34
    Modeling complexity: cognitive constraints and computational model-building in integrative systems biology.Miles MacLeod & Nancy J. Nersessian - 2018 - History and Philosophy of the Life Sciences 40 (1):17.
    Modern integrative systems biology defines itself by the complexity of the problems it takes on through computational modeling and simulation. However in integrative systems biology computers do not solve problems alone. Problem solving depends as ever on human cognitive resources. Current philosophical accounts hint at their importance, but it remains to be understood what roles human cognition plays in computational modeling. In this paper we focus on practices through which modelers in systems biology use computational simulation and other (...)
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  35. Modeling prejudice reduction: Spatialized game theory and the contact hypothesis.Patrick Grim, Evan Selinger, William Braynen, Robert Rosenberger, Randy Au, Nancy Louie & John Connolly - 2005 - Public Affairs Quarterly 19 (2):95-125.
    We apply spatialized game theory and multi-agent computational modeling as philosophical tools: (1) for assessing the primary social psychological hypothesis regarding prejudice reduction, and (2) for pursuing a deeper understanding of the basic mechanisms of prejudice reduction.
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  36. Modeling future indeterminacy in possibility semantics.Fabrizio Cariani - manuscript
    Possibility semantics offers an elegant framework for a semantic analysis of modal logic that does not recruit fully determinate entities such as possible worlds. The present papers considers the application of possibility semantics to the modeling of the indeterminacy of the future. Interesting theoretical problems arise in connection to the addition of object-language determinacy operator. We argue that adding a two-dimensional layer to possibility semantics can help solve these problems. The resulting system assigns to the two-dimensional determinacy operator a (...)
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  37. Symbiotic modeling: Linguistic Anthropology and the promise of chiasmus.Jamin Pelkey - 2016 - Reviews in Anthropology 45 (1):22–50.
    Reflexive observations and observations of reflexivity: such agendas are by now standard practice in anthropology. Dynamic feedback loops between self and other, cause and effect, represented and representamen may no longer seem surprising; but, in spite of our enhanced awareness, little deliberate attention is devoted to modeling or grounding such phenomena. Attending to both linguistic and extra-linguistic modalities of chiasmus (the X figure), a group of anthropologists has recently embraced this challenge. Applied to contemporary problems in linguistic anthropology, chiasmus (...)
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  38.  12
    Modeling Human Syllogistic Reasoning: The Role of “No Valid Conclusion”.Nicolas Riesterer, Daniel Brand, Hannah Dames & Marco Ragni - 2020 - Topics in Cognitive Science 12 (1):446-459.
    After 100+ years of studying syllogistic reasoning, what have we learned? Well, Riesterer and colleagues suggest that we have learned to throw away most of the data! If that seems like a bad idea to you then, be assured, that the authors agree with you. The sad fact is that the conclusion of “No Valid Conclusion” (NVC) is one of the most frequently selected responses in syllogistic reasoning but these “majority data” have been ignored by most researchers. Riesterer and colleagues (...)
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  39. Modeling Truth for Semantics.Ori Simchen - 2019 - Analytic Philosophy 61 (1):28-36.
    The Tarskian notion of truth-in-a-model is the paradigm formal capture of our pre-theoretical notion of truth for semantic purposes. But what exactly makes Tarski’s construction so well suited for semantics is seldom discussed. In my Semantics, Metasemantics, Aboutness (OUP 2017) I articulate a certain requirement on the successful formal modeling of truth for semantics – “locality-per-reference” – against a background discussion of metasemantics and its relation to truth-conditional semantics. It is a requirement on any formal capture of sentential truth (...)
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  40. Mathematical Modeling in Biology: Philosophy and Pragmatics.Rasmus Grønfeldt Winther - 2012 - Frontiers in Plant Evolution and Development 2012:1-3.
    Philosophy can shed light on mathematical modeling and the juxtaposition of modeling and empirical data. This paper explores three philosophical traditions of the structure of scientific theory—Syntactic, Semantic, and Pragmatic—to show that each illuminates mathematical modeling. The Pragmatic View identifies four critical functions of mathematical modeling: (1) unification of both models and data, (2) model fitting to data, (3) mechanism identification accounting for observation, and (4) prediction of future observations. Such facets are explored using a recent (...)
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  41. Modeling Epistemology: Examples and Analysis in Computational Philosophy of Science.Patrick Grim - 2019 - In A. Del Barrio, C. J. Lynch, F. J. Barros & X. Hu (eds.), IEEE SpringSim Proceedings 2019. IEEE. pp. 1-12.
    What structure of scientific communication and cooperation, between what kinds of investigators, is best positioned to lead us to the truth? Against an outline of standard philosophical characteristics and a recent turn to social epistemology, this paper surveys highlights within two strands of computational philosophy of science that attempt to work toward an answer to this question. Both strands emerge from abstract rational choice theory and the analytic tradition in philosophy of science rather than postmodern sociology of science. The first (...)
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  42.  53
    Modeling creative abduction Bayesian style.Christian J. Feldbacher-Escamilla & Alexander Gebharter - 2019 - European Journal for Philosophy of Science 9 (1):1-15.
    Schurz (Synthese 164:201–234, 2008) proposed a justification of creative abduction on the basis of the Reichenbachian principle of the common cause. In this paper we take up the idea of combining creative abduction with causal principles and model instances of successful creative abduction within a Bayes net framework. We identify necessary conditions for such inferences and investigate their unificatory power. We also sketch several interesting applications of modeling creative abduction Bayesian style. In particular, we discuss use-novel predictions, confirmation, and (...)
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  43. Modeling as a teaching learning process for understanding materials: A case study in primary education.Andrés Acher, María Arcà & Neus Sanmartí - 2007 - Science Education 91 (3):398-418.
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  44. Causal Modeling and the Efficacy of Action.Holly Andersen - 2022 - In Michael Brent & Lisa Miracchi Titus (eds.), Mental Action and the Conscious Mind. Routledge.
    This paper brings together Thompson's naive action explanation with interventionist modeling of causal structure to show how they work together to produce causal models that go beyond current modeling capabilities, when applied to specifically selected systems. By deploying well-justified assumptions about rationalization, we can strengthen existing causal modeling techniques' inferential power in cases where we take ourselves to be modeling causal systems that also involve actions. The internal connection between means and end exhibited in naive action (...)
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  45.  30
    Modeling: Neutral, Null, and Baseline.William C. Bausman - 2018 - Philosophy of Science 85 (4):594-616.
    Two strategies for using a model as “null” are distinguished. Null modeling evaluates whether a process is causally responsible for a pattern by testing it against a null model. Baseline modeling measures the relative significance of various processes responsible for a pattern by detecting deviations from a baseline model. When these strategies are conflated, models are illegitimately privileged as accepted until rejected. I illustrate this using the neutral theory of ecology and draw general lessons from this case. First, (...)
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  46.  3
    Multimodal Modeling: Bridging Biosemiotics and Social Semiotics.Alin Olteanu - 2021 - Biosemiotics 14 (3):783-805.
    This paper explores a semiotic notion of body as starting point for bridging biosemiotic with social semiotic theory. The cornerstone of the argument is that the social semiotic criticism of the classic view of meaning as double articulation can support the criticism of language-centrism that lies at the foundation of biosemiotics. Besides the pragmatic epistemological advantages implicit in a theoretical synthesis, I argue that this brings a semiotic contribution to philosophy of mind broadly. Also, it contributes to overcoming the polemic (...)
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  47.  38
    Information modeling aspects of software development.Timothy R. Colburn - 1998 - Minds and Machines 8 (3):375-393.
    The distinction between the modeling of information and the modeling of data in the creation of automated systems has historically been important because the development tools available to programmers have been wedded to machine oriented data types and processes. However, advances in software engineering, particularly the move toward data abstraction in software design, allow activities reasonably described as information modeling to be performed in the software creation process. An examination of the evolution of programming languages and development (...)
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  48.  21
    Computational Modeling in Philosophy.Simon Scheller, Merdes Christoph & Stephan Hartmann (eds.) - 2022
    Computational modeling should play a central role in philosophy. In this introduction to our topical collection, we propose a small topology of computational modeling in philosophy in general, and show how the various contributions to our topical collection ft into this overall picture. On this basis, we describe some of the ways in which computational models from other disciplines have found their way into philosophy, and how the principles one found here still underlie current trends in the feld. (...)
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  49.  29
    Modeling herding behavior and its risks.Michael Weisberg - 2013 - Journal of Economic Methodology 20 (1):6 - 18.
    (2013). Modeling herding behavior and its risks. Journal of Economic Methodology: Vol. 20, Methodology, Systemic Risk, and the Economics Profession, pp. 6-18. doi: 10.1080/1350178X.2013.774843.
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    Modeling Rational Players: Part II.Ken Binmore - 1988 - Economics and Philosophy 4 (1):9-55.
    This is the second part of a two-part paper. It can be read independently of the first part provided that the reader is prepared to go along with the unorthodox views on game theory which were advanced in Part I and are summarized below. The body of the paper is an attempt to study some of the positive implications of such a viewpoint. This requires an exploration of what is involved in modeling “rational players” as computing machines.
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