Search results for 'model modeling modelling' (try it on Scholar)

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  1. Tarja Knuuttila (2011). Modelling and Representing: An Artefactual Approach to Model-Based Representation. Studies in History and Philosophy of Science 42 (2):262-271.
    The recent discussion on scientific representation has focused on models and their relationship to the real world. It has been assumed that models give us knowledge because they represent their supposed real target systems. However, here agreement among philosophers of science has tended to end as they have presented widely different views on how representation should be understood. I will argue that the traditional representational approach is too limiting as regards the epistemic value of modelling given the focus on (...)
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  2.  13
    Michael S. C. Thomas, Neil A. Forrester & Angelica Ronald (2016). Multiscale Modeling of Gene–Behavior Associations in an Artificial Neural Network Model of Cognitive Development. Cognitive Science 40 (1):51-99.
    In the multidisciplinary field of developmental cognitive neuroscience, statistical associations between levels of description play an increasingly important role. One example of such associations is the observation of correlations between relatively common gene variants and individual differences in behavior. It is perhaps surprising that such associations can be detected despite the remoteness of these levels of description, and the fact that behavior is the outcome of an extended developmental process involving interaction of the whole organism with a variable environment. Given (...)
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  3.  12
    Tarja Knuuttila & Andrea Loettgers (forthcoming). Modelling as Indirect Representation? The Lotka–Volterra Model Revisited. British Journal for the Philosophy of Science:axv055.
    Is there something specific about modelling that distinguishes it from many other theoretical endeavours? We consider Michael Weisberg’s thesis that modelling is a form of indirect representation through a close examination of the historical roots of the Lotka–Volterra model. While Weisberg discusses only Volterra’s work, we also study Lotka’s very different design of the Lotka–Volterra model. We will argue that while there are elements of indirect representation in both Volterra’s and Lotka’s modelling approaches, they are (...)
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  4.  23
    Caterina Marchionni (2013). Model-Based Explanation in the Social Sciences: Modeling Kinship Terminologies and Romantic Networks. Perspectives on Science 21 (2):175-180.
    Read argues that modeling cultural idea systems serves to make explicit the cultural rules through which "cultural idea systems" frame behaviors that are culturally meaningful. Because cultural rules are typically "invisible" to us, one of the anthropologists' tasks is to elicit these rules, make them explicit and then use them to build explanations for patterns in cultural phenomena. The main example of Read's approach to cultural idea systems is the formal modeling of kinship terminologies. I reconstruct Read's (...) strategy as comprising the following steps:From the way in which culture-bearers compute kin relations a data model is construed that makes explicit the cultural theory embedded in a kinship .. (shrink)
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  5.  32
    Jerry Batzel & Mostafa Bachar (2010). Modeling the Cardiovascular-Respiratory Control System: Data, Model Analysis, and Parameter Estimation. Acta Biotheoretica 58 (4):369-380.
    Several key areas in modeling the cardiovascular and respiratory control systems are reviewed and examples are given which reflect the research state of the art in these areas. Attention is given to the interrelated issues of data collection, experimental design, and model application including model development and analysis. Examples are given of current clinical problems which can be examined via modeling, and important issues related to model adaptation to the clinical setting.
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    Masudul Alam Choudhury (2011). A Critique of Economic Theory and Modeling: A Meta-Epistemological General-System Model of Islamic Economics. Social Epistemology 25 (4):423 - 446.
    The scientific methodology underlying model-building is critically investigated. The modeling views of Popper and Samuelson and their prototypes are critically examined in the light of the theme of the moral law of unity of knowledge and unity of the world-system configured by the meta-epistemology of organic unity of knowledge. Upon such critical examination of received methodology of model-building in economics, the extended perspective?namely of integrating the moral law derived from the divine roots as the meta-epistemology?is rigorously studied. (...)
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    Aris Spanos (2011). Foundational Issues in Statistical Modeling : Statistical Model Specification. Philosophy of Science 2 (47):146-178.
    Statistical model specification and validation raise crucial foundational problems whose pertinent resolution holds the key to learning from data by securing the reliability of frequentist inference. The paper questions the judiciousness of several current practices, including the theory-driven approach, and the Akaike-type model selection procedures, arguing that they often lead to unreliable inferences. This is primarily due to the fact that goodness-of-fit/prediction measures and other substantive and pragmatic criteria are of questionable value when the estimated model is (...)
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  8.  1
    Gaku Ito & Susumu Yamakage (2015). From KISS to TASS Modeling: A Preliminary Analysis of the Segregation Model Incorporated with Spatial Data on Chicago. Japanese Journal of Political Science 16 (4):553-573.
    The slogan, or the KISS principle has been the basic guideline in agent-based modeling. While the KISS principle or parsimony is vital in modeling attempts, conventional agent-based models remain abstract and are rarely incorporated or validated with empirical data, leaving the links between theoretical models and empirical phenomena rather loose. This article reexamines the KISS principle and discusses the recent modeling attempts that incorporate and validate agent-based models with spatial data, moving beyond the KISS principle. This article (...)
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  9.  14
    Arthur M. Jacobs & Jonathan Grainger (1999). Modeling a Theory Without a Model Theory, or, Computational Modeling “After Feyerabend”. Behavioral and Brain Sciences 22 (1):46-47.
    Levelt et al. attempt to “model their theory” with WEAVER ++. Modeling theories requires a model theory. The time is ripe for a methodology for building, testing, and evaluating computational models. We propose a tentative, five-step framework for tackling this problem, within which we discuss the potential strengths and weaknesses of Levelt et al.'s modeling approach.
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  10. Peter Spiegler (2015). Behind the Model: A Constructive Critique of Economic Modeling. Cambridge University Press.
    This ambitious book looks 'behind the model' to reveal how economists use formal models to generate insights into the economy. Drawing on recent work in the philosophy of science and economic methodology, the book presents a novel framework for understanding the logic of economic modeling. It also reveals the ways in which economic models can mislead rather than illuminate. Importantly, the book goes beyond purely negative critique, proposing a concrete program of methodological reform to better equip economists to (...)
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  11.  12
    Saikou Y. Diallo, Jose J. Padilla, Ross Gore, Heber Herencia‐Zapana & Andreas Tolk (2014). Toward a Formalism of Modeling and Simulation Using Model Theory. Complexity 19 (3):56-63.
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  12.  24
    Patrick Brézillon (2007). Context Modeling: Task Model and Practice Model. In D. C. Richardson B. Kokinov (ed.), Modeling and Using Context. Springer 122--135.
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  13.  1
    Renate Motschnig-Pitrik & Ladislav Nykl (2001). The Role and Modeling of Context in a Cognitive Model of Rogers' Person-Centred Approach. In P. Bouquet V. Akman (ed.), Modeling and Using Context. Springer 275--289.
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  14.  10
    Uwe Meixner (2010). Modelling Metaphysics: The Metaphysics of a Model. Ontos.
    This book models and simulates metaphysics by presenting the metaphysics of a model.
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  15.  37
    Raphael Scholl & Tim Räz (2013). Modeling Causal Structures. 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 (...)
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  16.  17
    Han-Liang Chang (2009). Semioticians Make Strange Bedfellows! Or, Once Again: “Is Language a Primary Modelling System?”. [REVIEW] Biosemiotics 2 (2):169-179.
    Like other sciences, biosemiotics also has its time-honoured archive, consisting of writings by those who have been invented and revered as ancestors of the discipline. One such example is Jakob von Uexküll. As to the people who ‘invented’ him, they are either, to paraphrase a French cliché, ‘agents du cosmopolitisme sémiotique’ like Thomas Sebeok, or de jure and de facto progenitor like Thure von Uexküll. In the archive is the special issue of Semiotica 42. 1 (1982) edited by the late (...)
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    Robert L. Ashenhurst (1996). Ontological Aspects of Information Modeling. Minds and Machines 6 (3):287-394.
    Information modeling (also known as conceptual modeling or semantic data modeling) may be characterized as the formulation of a model in which information aspects of objective and subjective reality are presented (the application), independent of datasets and processes by which they may be realized (the system).A methodology for information modeling should incorporate a number of concepts which have appeared in the literature, but should also be formulated in terms of constructs which are understandable to and (...)
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  18. Karin Schermelleh-Engel, Martin Kerwer & Andreas G. Klein (2014). Evaluation of Model Fit in Nonlinear Multilevel Structural Equation Modeling. Frontiers in Psychology 5.
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  19.  13
    Douglas Jondle, Alexandre Ardichvili & James Mitchell (2014). Modeling Ethical Business Culture: Development of the Ethical Business Culture Survey and Its Use to Validate the CEBC Model of Ethical Business Culture. Journal of Business Ethics 119 (1):29-43.
    This article reports the results of research to develop a survey instrument and its use to validate an ethical business culture construct (CEBC Model). The reported three-stage quantitative study builds on our previous qualitative work, aimed at identifying dimensions of ethical business cultures. The research resulted in a parsimonious construct, covering five dimensions of ethical business cultures, and a ten-question instrument, measuring this construct. In this article, we report results of exploratory and confirmatory factor analyses and convergent construct validity (...)
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  20. Conrad Perry, Johannes C. Ziegler & Marco Zorzi (2007). Nested Incremental Modeling in the Development of Computational Theories: The CDP+ Model of Reading Aloud. Psychological Review 114 (2):273-315.
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  21.  3
    Tarja Knuuttila (2011). Modelling and Representing: An Artefactual Approach to Model-Based Representation. Studies in History and Philosophy of Science Part A 42 (2):262-271.
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  22. S. M. J. Janssen, A. G. Chessa & J. M. J. Murre (2003). Modeling the Reminiscence Bump in Autobiographical Memory with the Memory Chain Model. In B. Kokinov & W. Hirst (eds.), Constructive Memory. New Bulgarian University
     
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  23.  15
    David E. Huber & Rosemary A. Cowell (2010). Theory-Driven Modeling or Model-Driven Theorizing? Comment on McClelland Et Al. And Griffiths Et Al. Trends in Cognitive Sciences 14 (8):343-344.
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  24.  29
    Nino Cocchiarella (2010). Review of Uwe Meixner, Modelling Metaphysics: The Metaphysics of a Model. [REVIEW] Notre Dame Philosophical Reviews 2010 (5).
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  25.  6
    Ileana Baldi, Alberto Ferrando, Francesca Foltran, Giovannino Ciccone & Dario Gregori (2010). Studying Factors Related to Pressure Ulcers Prevention: A Marginal Scale Model for Modelling Heterogeneity Among Hospitals. Journal of Evaluation in Clinical Practice 16 (6):1085-1089.
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  26.  3
    Małgorzata Haładewicz-Grzelak (2012). Dynamic Modeling of Visual Texts: A Relational Model. Semiotica 2012 (190).
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  27.  1
    Leon Wansleben (2014). Consistent Forecasting Vs. Anchoring of Market Stories: Two Cultures of Modeling and Model Use in a Bank. Science in Context 27 (4):605-630.
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    C.-Y. Yeh, L.-J. Chen, P.-W. Ku & C.-M. Chen (2015). Modelling the Vicious Circle Between Obesity and Physical Activity in Children and Adolescents Using a Bivariate Probit Model with Endogenous Regressors. Journal of Biosocial Science 47 (1):61-74.
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  29.  1
    Peeter Torop (1999). Language, Text, Structure, Model,(Secondary) Modeling System Are These Notions the Dynamism of Which—in the Volume of Their Meaning—Gives a Good Overview of the Semiotics of Lotman and the Tartu–Moscow Semiotic School Until the Birth of Cultural Semiotics in 1973. K. Eimermacher has Called Lotmans Ability to Conjoin Different Terms and to Provide Them with Novel Meanings Integrativity, and to This He Also Dedicated an Article “JM Lotman: Semiotic Version of Integrative Culturology”(Eimermacher 1998 ... [REVIEW] Sign Systems Studies 27:9-23.
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  30. J. Cotton & A. R. Othman (1991). Modeling Perception of Temperature-Change Using the Generalized Additive-Model. Bulletin of the Psychonomic Society 29 (6):499-499.
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  31. Chandra Prakash Dubey, Hans-Jürgen Götze, Sabine Schmidt & Virendra Mani Tiwari (2014). A 3D Model of the Wathlingen Salt Dome in the Northwest German Basin From Joint Modeling of Gravity, Gravity Gradient, and Curvature. Interpretation 2 (4):SJ103-SJ115.
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  32. Karine Gouriet, Philippe Carrez, Patrick Cordier, Antoine Guitton, Anne Joulain, Ludovic Thilly & Christophe Tromas (2015). Dislocation Modelling in Ti2AlN MAX Phase Based on the Peierls–Nabarro Model. Philosophical Magazine 95 (23):2539-2552.
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  33. Gerd Grasshoff (1998). Modelling the Astrophysical Object SS433: Methodology of Model Construction by a Research Collective. Philosophia Naturalis 35 (1):161-199.
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  34. Gerd Graßhoff (1998). Modelling the Astrophysical Object SS433 - Methodology of Model Construction by a Research Collective. Philosophia Naturalis 35:161-200.
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  35. D. R. Hayhurst *, J. Makin, M. T. Wong & Q. Xu (2005). Modelling of Combined Creep and Cyclic Plasticity in a Model Component Undergoing Ratchetting Using Continuum Damage Mechanics. Philosophical Magazine 85 (16):1701-1728.
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  36. James L. McClelland (1993). The GRAIN Model: A Framework for Modeling the Dynamics of Information Processing. In David E. Meyer & Sylvan Kornblum (eds.), Attention and Performance Xiv. The MIT Press 655--688.
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  37. Alan Rosen & David B. Rosen (2006). Neurobiological Modeling and Analysis-An Electromechanical Neural Network Robotic Model of the Human Body and Brain: Sensory-Motor Control by Reverse Engineering Biological Somatic Sensors. In O. Stock & M. Schaerf (eds.), Lecture Notes in Computer Science. Springer-Verlag 4232--105.
     
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  38. Nicholas J. Sexton & Richard P. Cooper (2014). An Architecturally Constrained Model of Random Number Generation and its Application to Modeling the Effect of Generation Rate. Frontiers in Psychology 5.
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  39. Michael Zehetleitner, Emil Ratko-Dehnert & Hermann J. Müller (2015). Modeling Violations of the Race Model Inequality in Bimodal Paradigms: Co-Activation From Decision and Non-Decision Components. Frontiers in Human Neuroscience 9.
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  40. Nicolas Fillion & Robert M. Corless (2014). On the Epistemological Analysis of Modeling and Computational Error in the Mathematical Sciences. Synthese 191 (7):1451-1467.
    Interest in the computational aspects of modeling has been steadily growing in philosophy of science. This paper aims to advance the discussion by articulating the way in which modeling and computational errors are related and by explaining the significance of error management strategies for the rational reconstruction of scientific practice. To this end, we first characterize the role and nature of modeling error in relation to a recipe for model construction known as Euler’s recipe. We then (...)
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  41. Franck Varenne (2001). What Does a Computer Simulation Prove? The Case of Plant Modeling at CIRAD. In N. Giambiasi & C. Frydman (eds.), Simulation in industry - ESS 2001, Proc. of the 13th European Simulation Symposium. Society for Computer Simulation (SCS)
    The credibility of digital computer simulations has always been a problem. Today, through the debate on verification and validation, it has become a key issue. I will review the existing theses on that question. I will show that, due to the role of epistemological beliefs in science, no general agreement can be found on this matter. Hence, the complexity of the construction of sciences must be acknowledged. I illustrate these claims with a recent historical example. Finally I temperate this diversity (...)
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  42.  24
    Anna Conte & Peter G. Moffatt (2014). The Econometric Modelling of Social Preferences. Theory and Decision 76 (1):119-145.
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  43.  9
    Kenneth D. Forbus, Ronald W. Ferguson, Andrew Lovett & Dedre Gentner (2016). Extending SME to Handle Large‐Scale Cognitive Modeling. Cognitive Science 40 (6).
    Analogy and similarity are central phenomena in human cognition, involved in processes ranging from visual perception to conceptual change. To capture this centrality requires that a model of comparison must be able to integrate with other processes and handle the size and complexity of the representations required by the tasks being modeled. This paper describes extensions to Structure-Mapping Engine since its inception in 1986 that have increased its scope of operation. We first review the basic SME algorithm, describe psychological (...)
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  44.  9
    Ekaterina Svetlova (2015). Modeling as a Case for the Empirical Philosophy of Science. In Hanne Andersen, Nancy J. Nersessian & Susann Wagenknecht (eds.), Empirical Philosophy of Science. Springer International Publishing 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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  45.  4
    Rhiannon Weaver (2008). Parameters, Predictions, and Evidence in Computational Modeling: A Statistical View Informed by ACT–R. Cognitive Science 32 (8):1349-1375.
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  46. Sabrina Scherer, Maria A. Wimmer & Suvad Markisic (2013). Bridging Narrative Scenario Texts and Formal Policy Modeling Through Conceptual Policy Modeling. Artificial Intelligence and Law 21 (4):455-484.
  47.  30
    Mike Page (2000). Connectionist Modelling in Psychology: A Localist Manifesto. Behavioral and Brain Sciences 23 (4):443-467.
    Over the last decade, fully distributed models have become dominant in connectionist psychological modelling, whereas the virtues of localist models have been underestimated. This target article illustrates some of the benefits of localist modelling. Localist models are characterized by the presence of localist representations rather than the absence of distributed representations. A generalized localist model is proposed that exhibits many of the properties of fully distributed models. It can be applied to a number of problems that are (...)
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  48. Marcel Weber (2014). Experimental Modeling in Biology: In Vivo Representation and Stand-Ins As Modeling Strategies. 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 (...)
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  49.  18
    Tjerk Gauderis (2013). Modelling Abduction in Science by Means of a Modal Adaptive Logic. Foundations of Science 18 (4):611-624.
    Scientists confronted with multiple explanatory hypotheses as a result of their abductive inferences, generally want to reason further on the different hypotheses one by one. This paper presents a modal adaptive logic MLA s that enables us to model abduction in such a way that the different explanatory hypotheses can be derived individually. This modelling is illustrated with a case study on the different hypotheses on the origin of the Moon.
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  50.  11
    Russell Richie, Charles Yang & Marie Coppola (2014). Modeling the Emergence of Lexicons in Homesign Systems. 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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