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. 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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  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. 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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  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. 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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  8.  52
    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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  9.  64
    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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  10. Modeling Mental Qualities.Andrew Y. Lee - 2021 - The Philosophical Review 130 (2):263-209.
    Conscious experiences are characterized by mental qualities, such as those involved in seeing red, feeling pain, or smelling cinnamon. The standard framework for modeling mental qualities represents them via points in geometrical spaces, where distances between points inversely correspond to degrees of phenomenal similarity. This paper argues that the standard framework is structurally inadequate and develops a new framework that is more powerful and flexible. The core problem for the standard framework is that it cannot capture precision structure: for (...)
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  11. 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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  12.  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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  13.  65
    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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  14.  15
    Fractional Modeling and Control of a Complex Nonlinear Energy Supply-Demand System.Mohammad Pourmahmood Aghababa - 2015 - Complexity 20 (6):74-86.
  15. 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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  16. 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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  17. 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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  18.  16
    Modeling Morality.Walter Veit - 2019 - In Matthieu Fontaine, Cristina Barés-Gómez, Francisco Salguero-Lamillar, Lorenzo Magnani & Ángel Nepomuceno-Fernández (eds.), Model-Based Reasoning in Science and Technology. Springer Verlag. pp. 83–102.
    Unlike any other field, the science of morality has drawn attention from an extraordinarily diverse set of disciplines. An interdisciplinary research program has formed in which economists, biologists, neuroscientists, psychologists, and even philosophers have been eager to provide answers to puzzling questions raised by the existence of human morality. Models and simulations, for a variety of reasons, have played various important roles in this endeavor. Their use, however, has sometimes been deemed as useless, trivial and inadequate. The role of models (...)
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  19.  94
    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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  20.  96
    Forward Modeling Allows Feedback Control for Fast Reaching Movements.Michel Desmurget & Scott Grafton - 2000 - Trends in Cognitive Sciences 4 (11):423-431.
  21. 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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  22.  3
    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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  23. 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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  24.  83
    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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  25. Modeling Rational Players: Part I: Ken Binmore.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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  26. 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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  27.  44
    On Modeling Cognition and Culture: Why Cultural Evolution Does Not Require Replication of Representations.Robert Boyd - 2002 - Journal of Cognition and Culture 2 (2):87-112.
    Formal models of cultural evolution analyze how cognitive processes combine with social interaction to generate the distributions and dynamics of ‘representations.’ Recently, cognitive anthropologists have criticized such models. They make three points: mental representations are non-discrete, cultural transmission is highly inaccurate, and mental representations are not replicated, but rather are ‘reconstructed’ through an inferential process that is strongly affected by cognitive ‘attractors.’ They argue that it follows from these three claims that: 1) models that assume replication or replicators are inappropriate, (...)
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  28. 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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  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.  26
    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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  31.  37
    Modeling a Paranoid Mind.Kenneth Mark Colby - 1981 - Behavioral and Brain Sciences 4 (4):515-534.
  32. 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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  33.  46
    Modeling Creative Abduction Bayesian Style.Christian 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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  34.  20
    Modeling Spatial Knowledge.Benjamin Kuipers - 1978 - Cognitive Science 2 (2):129-153.
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  35.  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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  36.  84
    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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    Modeling the Approximate Number System to Quantify the Contribution of Visual Stimulus Features.Nicholas K. DeWind, Geoffrey K. Adams, Michael L. Platt & Elizabeth M. Brannon - 2015 - Cognition 142:247-265.
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  38.  50
    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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  39. 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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  40.  86
    Modeling Rational Players: Part II: Ken Binmore.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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  41.  14
    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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  42. Computational Modeling Vs. Computational Explanation: Is Everything a Turing Machine, and Does It Matter to the Philosophy of Mind?Gualtiero Piccinini - 2007 - Australasian Journal of Philosophy 85 (1):93 – 115.
    According to pancomputationalism, everything is a computing system. In this paper, I distinguish between different varieties of pancomputationalism. I find that although some varieties are more plausible than others, only the strongest variety is relevant to the philosophy of mind, but only the most trivial varieties are true. As a side effect of this exercise, I offer a clarified distinction between computational modelling and computational explanation.<br><br>.
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  43. 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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  44. Dynamic Mechanistic Explanation: Computational Modeling of Circadian Rhythms as an Exemplar for Cognitive Science.William Bechtel & Adele Abrahamsen - 2010 - Studies in History and Philosophy of Science Part A 41 (3):321-333.
    Two widely accepted assumptions within cognitive science are that (1) the goal is to understand the mechanisms responsible for cognitive performances and (2) computational modeling is a major tool for understanding these mechanisms. The particular approaches to computational modeling adopted in cognitive science, moreover, have significantly affected the way in which cognitive mechanisms are understood. Unable to employ some of the more common methods for conducting research on mechanisms, cognitive scientists’ guiding ideas about mechanism have developed in conjunction (...)
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  45. 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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  46.  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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  47.  43
    Modeling the Social Organization of Science: Chasing Complexity Through Simulations.Carlo Martini & Manuela Fernández Pinto - 2016 - European Journal for Philosophy of Science 7 (2):221-238.
    At least since Kuhn’s Structure, philosophers have studied the influence of social factors in science’s pursuit of truth and knowledge. More recently, formal models and computer simulations have allowed philosophers of science and social epistemologists to dig deeper into the detailed dynamics of scientific research and experimentation, and to develop very seemingly realistic models of the social organization of science. These models purport to be predictive of the optimal allocations of factors, such as diversity of methods used in science, size (...)
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  48.  61
    Modeling Human Performance in Statistical Word Segmentation.Michael C. Frank, Sharon Goldwater, Thomas L. Griffiths & Joshua B. Tenenbaum - 2010 - Cognition 117 (2):107-125.
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  49. Modeling Reality.Christopher Pincock - 2011 - Synthese 180 (1):19 - 32.
    My aim in this paper is to articulate an account of scientific modeling that reconciles pluralism about modeling with a modest form of scientific realism. The central claim of this approach is that the models of a given physical phenomenon can present different aspects of the phenomenon. This allows us, in certain special circumstances, to be confident that we are capturing genuine features of the world, even when our modeling occurs independently of a wholly theoretical motivation. This (...)
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  50.  32
    Microbes Modeling Ontogeny.Alan C. Love & Michael Travisano - 2013 - Biology and Philosophy 28 (2):161-188.
    Model organisms are central to contemporary biology and studies of embryogenesis in particular. Biologists utilize only a small number of species to experimentally elucidate the phenomena and mechanisms of development. Critics have questioned whether these experimental models are good representatives of their targets because of the inherent biases involved in their selection (e.g., rapid development and short generation time). A standard response is that the manipulative molecular techniques available for experimental analysis mitigate, if not counterbalance, this concern. But the most (...)
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