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.  83
    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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  3.  61
    Modeling the social consequences of testimonial norms.Kevin J. S. Zollman - 2015 - Philosophical Studies 172 (9):2371-2383.
    This paper approaches the problem of testimony from a new direction. Rather than focusing on the epistemic grounds for testimony, it considers the problem from the perspective of an individual who must choose whom to trust from a population of many would-be testifiers. A computer simulation is presented which illustrates that in many plausible situations, those who trust without attempting to judge the reliability of testifiers outperform those who attempt to seek out the more reliable members of the community. In (...)
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  4. 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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  5.  53
    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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  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.  66
    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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  9. Causal Modeling Semantics for Counterfactuals with Disjunctive Antecedents.Giuliano Rosella & Jan Sprenger - manuscript
    This paper applies Causal Modeling Semantics (CMS, e.g., Galles and Pearl 1998; Pearl 2000; Halpern 2000) to the evaluation of the probability of counterfactuals with disjunctive antecedents. Standard CMS is limited to evaluating (the probability of) counterfactuals whose antecedent is a conjunction of atomic formulas. We extend this framework to disjunctive antecedents, and more generally, to any Boolean combinations of atomic formulas. Our main idea is to assign a probability to a counterfactual ( A ∨ B ) > C (...)
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  10.  16
    Modeling individual differences in text reading fluency: a different pattern of predictors for typically developing and dyslexic readers.Pierluigi Zoccolotti, Maria De Luca, Chiara V. Marinelli & Donatella Spinelli - 2014 - Frontiers in Psychology 5.
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  11.  12
    Modeling language and cognition with deep unsupervised learning: a tutorial overview.Marco Zorzi, Alberto Testolin & Ivilin P. Stoianov - 2013 - Frontiers in Psychology 4.
  12. 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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  13.  85
    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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  14.  11
    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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  15.  96
    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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  16.  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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  17. 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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  18. Modeling future indeterminacy in possibility semantics.Fabrizio Cariani - manuscript
    I consider the application of possibility semantics to the modeling of the indeterminacy of the future. I argue that interesting problems arise in connection to the addition of object-language determinacy operator. I show that adding a two-dimensional layer to possibility semantics can help solve these problems.
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  19. Modeling practical thinking.Matthew Mosdell - 2019 - 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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  20.  9
    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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  21. 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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  22. 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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  23.  69
    Computational modeling as a philosophical methodology.Patrick Grim - 2003 - In Luciano Floridi (ed.), The Blackwell Guide to the Philosophy of Computing and Information. 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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  24. Modeling as a Case for the Empirical Philosophy of Science.Ekaterina Svetlova - 2015 - In Hanne Andersen, Nancy J. Nersessian & Susann Wagenknecht (eds.), Empirical Philosophy of Science. Springer Verlag. pp. 65-82.
    In recent years, the emergence of a new trend in contemporary philosophy has been observed in the increasing usage of empirical research methods to conduct philosophical inquiries. Although philosophers primarily use secondary data from other disciplines or apply quantitative methods (experiments, surveys, etc.), the rise of qualitative methods (e.g., in-depth interviews, participant observations and qualitative text analysis) can also be observed. In this paper, I focus on how qualitative research methods can be applied within philosophy of science, namely within the (...)
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  25. Modeling Truth for Semantics.Ori Simchen - 2020 - 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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  26. 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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  27.  15
    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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  28. 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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  29. Modeling economic systems as locally-constructive sequential games.Leigh Tesfatsion - 2017 - Journal of Economic Methodology 24 (4):1-26.
    Real-world economies are open-ended dynamic systems consisting of heterogeneous interacting participants. Human participants are decision-makers who strategically take into account the past actions and potential future actions of other participants. All participants are forced to be locally constructive, meaning their actions at any given time must be based on their local states; and participant actions at any given time affect future local states. Taken together, these essential properties imply real-world economies are locally-constructive sequential games. This paper discusses a modeling (...)
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  30.  20
    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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  31. Modeling, Truth, and Philosophy.Paul Teller - 2012 - Metaphilosophy 43 (3):257-274.
    Knowledge requires truth, and truth, we suppose, involves unflawed representation. Science does not provide knowledge in this sense but rather provides models, representations that are limited in their accuracy, precision, or, most often, both. Truth as we usually think of it is an idealization, one that serves wonderfully in most ordinary applications, but one that can terribly mislead for certain issues in philosophy. This article sketches how this happens for five important issues, thereby showing how philosophical method must take into (...)
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  32.  82
    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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  33.  10
    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.
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  34. 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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  35.  89
    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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  36. 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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  37.  31
    Modeling Truth.Paul Teller - 2017 - Philosophia 45 (1):143-161.
    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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  38. 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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  39.  28
    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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  40.  66
    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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  41.  68
    Synthetic Modeling and Mechanistic Account: Material Recombination and Beyond.Tarja Knuuttila & Andrea Loettgers - 2013 - Philosophy of Science 80 (5):874-885.
    Recently, Bechtel and Abrahamsen have argued that mathematical models study the dynamics of mechanisms by recomposing the components and their operations into an appropriately organized system. We will study this claim through the practice of combinational modeling in circadian clock research. In combinational modeling, experiments on model organisms and mathematical/computational models are combined with a new type of model—a synthetic model. We argue that the strategy of recomposition is more complicated than what Bechtel and Abrahamsen indicate. Moreover, synthetic (...)
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  42.  48
    Modeling creative abduction Bayesian style.Christian J. Feldbacher-Escamilla & Alexander Gebharter - 2019 - European Journal for Philosophy of Science 9 (1):1-15.
    Schurz 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 the problem of (...)
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  43. Theoretical modeling and biological laws.Gregory Cooper - 1996 - Philosophy of Science 63 (3):35.
    Recent controversy over the existence of biological laws raises questions about the cognitive aims of theoretical modeling in that science. If there are no laws for successful theoretical models to approximate, then what is it that successful theories do? One response is to regard theoretical models as tools. But this instrumental reading cannot accommodate the explanatory role that theories are supposed to play. Yet accommodating the explanatory function, as articulated by Brandon and Sober for example, seems to involve us (...)
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  44.  17
    Fractional modeling and control of a complex nonlinear energy supply-demand system.Mohammad Pourmahmood Aghababa - 2015 - Complexity 20 (6):74-86.
  45.  3
    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.
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  46. Modeling Rational Players: Part I.Ken Binmore - 1987 - Economics and Philosophy 3 (2):179-214.
    Game theory has proved a useful tool in the study of simple economic models. However, numerous foundational issues remain unresolved. The situation is particularly confusing in respect of the non-cooperative analysis of games with some dynamic structure in which the choice of one move or another during the play of the game may convey valuable information to the other players. Without pausing for breath, it is easy to name at least 10 rival equilibrium notions for which a serious case can (...)
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  47.  12
    Modeling Affect Dynamics: State of the Art and Future Challenges.E. L. Hamaker, E. Ceulemans, R. P. P. P. Grasman & F. Tuerlinckx - 2015 - Emotion Review 7 (4):316-322.
    The current article aims to provide an up-to-date synopsis of available techniques to study affect dynamics using intensive longitudinal data. We do so by introducing the following eight dichotomies that help elucidate what kind of data one has, what process aspects are of interest, and what research questions are being considered: single- versus multiple-person data; univariate versus multivariate models; stationary versus nonstationary models; linear versus nonlinear models; discrete time versus continuous time models; discrete versus continuous variables; time versus frequency domain; (...)
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  48.  98
    How Modeling Can Go Wrong: Some Cautions and Caveats on the Use of Models.Patrick Grim & Nicholas Rescher - 2013 - Philosophy and Technology 26 (1):75-80.
    Modeling and simulation clearly have an upside. My discussion here will deal with the inevitable downside of modeling — the sort of things that can go wrong. It will set out a taxonomy for the pathology of models — a catalogue of the various ways in which model contrivance can go awry. In the course of that discussion, I also call on some of my past experience with models and their vulnerabilities.
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  49.  21
    Modeling parallelization and flexibility improvements in skill acquisition: From dual tasks to complex dynamic skills.Niels Taatgen - 2005 - Cognitive Science 29 (3):421-455.
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  50. 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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