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

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  1. 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.score: 486.0
    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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  2. Mostafa Bachar (forthcoming). Modeling the Cardiovascular-Respiratory Control System: Data, Model Analysis, and Parameter Estimation. Acta Biotheoretica.score: 486.0
    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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  3. Caterina Marchionni (2013). Model-Based Explanation in the Social Sciences: Modeling Kinship Terminologies and Romantic Networks. Perspectives on Science 21 (2):175-180.score: 486.0
    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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  4. 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.score: 486.0
    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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  5. Aris Spanos (2011). Foundational Issues in Statistical Modeling : Statistical Model Specification. Philosophy of Science 2 (47):146-178.score: 486.0
    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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  6. 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.score: 459.0
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  7. Tyler D. Bancroft, William E. Hockley & Philip Servos (2011). Vibrotactile Working Memory as a Model Paradigm for Psychology, Neuroscience, and Computational Modeling. Frontiers in Human Neuroscience 5.score: 441.0
  8. Tarja Knuuttila (2011). Modelling and Representing: An Artefactual Approach to Model-Based Representation. Studies in History and Philosophy of Science 42 (2):262-271.score: 436.0
    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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  9. Patrick Brézillon (2007). Context Modeling: Task Model and Practice Model. In. In D. C. Richardson B. Kokinov (ed.), Modeling and Using Context. Springer. 122--135.score: 432.0
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  10. Renate Motschnig-Pitrik & Ladislav Nykl (2001). The Role and Modeling of Context in a Cognitive Model of Rogers' Person-Centred Approach. In. In P. Bouquet V. Akman (ed.), Modeling and Using Context. Springer. 275--289.score: 432.0
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  11. 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.score: 425.0
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  12. 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.score: 423.0
    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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  13. 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.score: 405.0
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  14. Małgorzata Haładewicz-Grzelak (2012). Dynamic Modeling of Visual Texts: A Relational Model. Semiotica 2012 (190).score: 405.0
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  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.score: 405.0
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  16. 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.score: 405.0
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  17. 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.score: 405.0
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  18. 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.score: 405.0
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  19. Arnon Levy (forthcoming). Modeling Without Models. Philosophical Studies:1-18.score: 390.0
    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 (...)
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  20. Robert L. Ashenhurst (1996). Ontological Aspects of Information Modeling. Minds and Machines 6 (3):287-394.score: 378.0
    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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  21. Raphael Scholl & Tim Räz (2013). Modeling Causal Structures. European Journal for Philosophy of Science 3 (1):115-132.score: 378.0
    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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  22. Michael E. Brown & Linda K. Treviño (2013). Do Role Models Matter? An Investigation of Role Modeling as an Antecedent of Perceived Ethical Leadership. Journal of Business Ethics:1-12.score: 375.0
    Thus far, we know much more about the significant outcomes of perceived ethical leadership than we do about its antecedents. In this study, we focus on multiple types of ethical role models as antecedents of perceived ethical leadership. According to social learning theory, role models facilitate the acquisition of moral and other types of behavior. Yet, we do not know whether having had ethical role models influences follower perceptions of one’s ethical leadership and, if so, what kinds of role models (...)
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  23. Uwe Meixner (2010). Modelling Metaphysics: The Metaphysics of a Model. Ontos.score: 352.0
    This book models and simulates metaphysics by presenting the metaphysics of a model.
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  24. Han-Liang Chang (2009). Semioticians Make Strange Bedfellows! Or, Once Again: “Is Language a Primary Modelling System?”. [REVIEW] Biosemiotics 2 (2):169-179.score: 346.0
    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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  25. Cyril Hédoin (2014). Models in Economics Are Not (Always) Nomological Machines A Pragmatic Approach to Economists' Modeling Practices. Philosophy of the Social Sciences 44 (4):424-459.score: 340.0
    This paper evaluates Nancy Cartwright’s critique of economic models. Cartwright argues that economics fails to build relevant “nomological machines” able to isolate capacities. In this paper, I contend that many economic models are not used as nomological machines. I give some evidence for this claim and build on an inferential and pragmatic approach to economic modeling. Modeling in economics responds to peculiar inferential norms where a “good” model is essentially a model that enhances our knowledge about (...)
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  26. Michael Weisberg, Models for Modeling.score: 336.0
    Contemporary literature in philosophy of science has begun to emphasize the practice of modeling, which differs in important respects from other forms of representation and analysis central to standard philosophical accounts. This literature has stressed the constructed nature of models, their autonomy, and the utility of their high degrees of idealization. What this new literature about modeling lacks, however, is a comprehensive account of the models that figure in to the practice of modeling. This paper offers a (...)
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  27. Eckhart Arnold, Tools of Toys? On Specific Challenges for Modeling and the Epistemology of Models and Computer Simulations in the Social Sciences.score: 336.0
    Mathematical models are a well established tool in most natural sciences. Although models have been neglected by the philosophy of science for a long time, their epistemological status as a link between theory and reality is now fairly well understood. However, regarding the epistemological status of mathematical models in the social sciences, there still exists a considerable unclarity. In my paper I argue that this results from specific challenges that mathematical models and especially computer simulations face in the social sciences. (...)
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  28. Jordi Cat (2005). Modeling Cracks and Cracking Models: Structures, Mechanisms, Boundary Conditions, Constraints, Inconsistencies and the Proper Domains of Natural Laws. Synthese 146 (3):447 - 487.score: 336.0
    The emphasis on models hasn’t completely eliminated laws from scientific discourse and philosophical discussion. Instead, I want to argue that much of physics lies beyond the strict domain of laws. I shall argue that in important cases the physics, or physical understanding, does not lie either in laws or in their properties, such as universality, consistency and symmetry. I shall argue that the domain of application commonly attributed to laws is too narrow. That is, laws can still play an important, (...)
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  29. Dwight Read (2013). Modeling Cultural Idea Systems: The Relationship Between Theory Models and Data Models. Perspectives on Science 21 (2):157-174.score: 336.0
    Subjective experience is transformed into objective reality for societal members through cultural idea systems that can be represented with theory and data models. A theory model shows relationships and their logical implications that structure a cultural idea system. A data model expresses patterning found in ethnographic observations regarding the behavioral implementation of cultural idea systems. An example of this duality for modeling cultural idea systems is illustrated with Arabic proverbs that structurally link friend and enemy as concepts (...)
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  30. Akira Utsumi (2011). Computational Exploration of Metaphor Comprehension Processes Using a Semantic Space Model. Cognitive Science 35 (2):251-296.score: 333.0
    Recent metaphor research has revealed that metaphor comprehension involves both categorization and comparison processes. This finding has triggered the following central question: Which property determines the choice between these two processes for metaphor comprehension? Three competing views have been proposed to answer this question: the conventionality view (Bowdle & Gentner, 2005), aptness view (Glucksberg & Haught, 2006b), and interpretive diversity view (Utsumi, 2007); these views, respectively, argue that vehicle conventionality, metaphor aptness, and interpretive diversity determine the choice between the categorization (...)
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  31. B. U. Forstmann, S. Brown, G. Dutilh, J. Neumann & E. J. Wagenmakers (2009). The Neural Substrate of Prior Information in Perceptual Decision Making: A Model-Based Analysis. Frontiers in Human Neuroscience 4:40-40.score: 333.0
    Prior information biases the decision process: actions consistent with prior information are executed swiftly, whereas actions inconsistent with prior information are executed slowly. How is this bias implemented in the brain? To address this question we conducted an experiment in which people had to decide quickly whether a cloud of dots moved coherently to the left or to the right. Cues provided probabilistic information about the upcoming stimulus. Behavioral data were analyzed with the linear ballistic accumulator (LBA) model, confirming (...)
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  32. Ivilin P. Stoianov Marco Zorzi, Alberto Testolin (2013). Modeling Language and Cognition with Deep Unsupervised Learning: A Tutorial Overview. Frontiers in Psychology 4.score: 318.0
    Deep unsupervised learning in stochastic recurrent neural networks with many layers of hidden units is a recent breakthrough in neural computation research. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. In this article we discuss the theoretical foundations of this approach and we review key issues related to training, testing and analysis of deep networks for modeling language and cognitive processing. The classic letter and word (...)
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  33. John Kingston, Burkhard Schafer & Wim Vandenberghe (2004). Towards a Financial Fraud Ontology: A Legal Modelling Approach. [REVIEW] Artificial Intelligence and Law 12 (4):419-446.score: 315.0
    This document discusses the status of research on detection and prevention of financial fraud undertaken as part of the IST European Commission funded FF POIROT (Financial Fraud Prevention Oriented Information Resources Using Ontology Technology) project. A first task has been the specification of the user requirements that define the functionality of the financial fraud ontology to be designed by the FF POIROT partners. It is claimed here that modeling fraudulent activity involves a mixture of law and facts as well (...)
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  34. Björn Kralemann & Claas Lattmann (2013). Models as Icons: Modeling Models in the Semiotic Framework of Peirce's Theory of Signs. Synthese 190 (16):3397-3420.score: 307.0
    In this paper, we try to shed light on the ontological puzzle pertaining to models and to contribute to a better understanding of what models are. Our suggestion is that models should be regarded as a specific kind of signs according to the sign theory put forward by Charles S. Peirce, and, more precisely, as icons, i.e. as signs which are characterized by a similarity relation between sign (model) and object (original). We argue for this (1) by analyzing from (...)
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  35. 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.score: 305.0
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  36. D. B. McCoach & B. Kaniskan (2009). Using Time-Varying Covariates in Multilevel Growth Models. Frontiers in Psychology 1:17-17.score: 303.0
    This article provides an illustration of growth curve modeling within a multilevel framework. Specifically, we demonstrate coding schemes that allow the researcher to model discontinuous longitudinal data using a linear growth model in conjunction with time varying covariates. Our focus is on developing a level-1 model that accurately reflects the shape of the growth trajectory. We demonstrate the importance of adequately modeling the shape of the level-1 growth trajectory in order to make inferences about the (...)
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  37. 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).score: 301.0
    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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  38. Rhiannon Weaver (2008). Parameters, Predictions, and Evidence in Computational Modeling: A Statistical View Informed by ACT–R. Cognitive Science 32 (8):1349-1375.score: 301.0
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  39. Dave Billinge & Tom Addis (2008). Seeking Allies: Modelling How Listeners Choose Their Musical Friends. [REVIEW] Foundations of Science 13 (1):53-66.score: 300.0
    In this paper we describe in some detail a formal computer model of inferential discourse based on a belief system. The key issue is that a logical model in a computer, based on rational sets, can usefully model a human situation based on irrational sets. The background of this work is explained elsewhere, as is the issue of rational and irrational sets (Billinge and Addis, in: Magnani and Dossena (eds.), Computing, philosophy and cognition, 2004; Stepney et al., (...)
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  40. James R. Griesemer (1990). Modeling in the Museum: On the Role of Remnant Models in the Work of Joseph Grinnell. [REVIEW] Biology and Philosophy 5 (1):3-36.score: 297.0
    Accounts of the relation between theories and models in biology concentrate on mathematical models. In this paper I consider the dual role of models as representations of natural systems and as a material basis for theorizing. In order to explicate the dual role, I develop the concept of a remnant model, a material entity made from parts of the natural system(s) under study. I present a case study of an important but neglected naturalist, Joseph Grinnell, to illustrate the extent (...)
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  41. Bruce Edmonds (2000). Complexity and Scientific Modelling. Foundations of Science 5 (3):379-390.score: 291.3
    It is argued that complexity is not attributable directly to systems or processes but rather to the descriptions of their `best' models, to reflect their difficulty. Thus it is relative to the modelling language and type of difficulty. This approach to complexity is situated in a model of modelling. Such an approach makes sense of a number of aspects of scientific modelling: complexity is not situated between order and disorder; noise can be explicated by approaches to (...)
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  42. 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.score: 290.0
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  43. Vladimir Kuznetsov (2009). Variables of Scientific Concept Modeling and Their Formalization. In В.И Маркин (ed.), Philosophy of mathematics: current problems. Proceedings of the second international conference (Философия математики: актуальные проблемы. Тезисы второй международной конференции). Макс Пресс. 268-270.score: 288.0
    There are no universally adopted answers to the natural questions about scientific concepts: What are they? What is their structure? What are their functions? How many kinds of them are there? Do they change? Ironically, most if not all scientific monographs or articles mention concepts, but the scientific studies of scientific concepts are rare in occurrence. It is well known that the necessary stage of any scientific study is constructing the model of objects in question. Many years logical (...) was dominant in the concept studies. Last decades, concepts came to be regarded as the subject of mathematical modeling. However, different authors take different features of concepts as independent variables of their models. Our objective is to characterize informally the spectra of relevant variables for the modeling of scientific concepts. (shrink)
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  44. Mike Page (2000). Connectionist Modelling in Psychology: A Localist Manifesto. Behavioral and Brain Sciences 23 (4):443-467.score: 287.3
    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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  45. Guillaume Wunsch, Michel Mouchart & Federica Russo (2014). Functions and Mechanisms in Structural-Modelling Explanations. Journal for General Philosophy of Science 45 (1):187-208.score: 287.3
    One way social scientists explain phenomena is by building structural models. These models are explanatory insofar as they manage to perform a recursive decomposition on an initial multivariate probability distribution, which can be interpreted as a mechanism. Explanations in social sciences share important aspects that have been highlighted in the mechanisms literature. Notably, spelling out the functioning the mechanism gives it explanatory power. Thus social scientists should choose the variables to include in the model on the basis of their (...)
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  46. Rosanna Keefe (2012). Modelling Vagueness: What Can We Ignore? Philosophical Studies 161 (3):453-470.score: 286.0
    A theory of vagueness gives a model of vague language and of reasoning within the language. Among the models that have been offered are Degree Theorists’ numerical models that assign values between 0 and 1 to sentences, rather than simply modelling sentences as true or false. In this paper, I ask whether we can benefit from employing a rich, well-understood numerical framework, while ignoring those aspects of it that impute a level of mathematical precision that is not present (...)
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  47. Tjerk Gauderis (2013). Modelling Abduction in Science by Means of a Modal Adaptive Logic. Foundations of Science 18 (4):611-624.score: 286.0
    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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  48. John Stewart & Olivier Gapenne (2004). Reciprocal Modelling of Active Perception of 2-D Forms in a Simple Tactile-Vision Substitution System. Minds and Machines 14 (3):309-330.score: 286.0
    The strategies of action employed by a human subject in order to perceive simple 2-D forms on the basis of tactile sensory feedback have been modelled by an explicit computer algorithm. The modelling process has been constrained and informed by the capacity of human subjects both to consciously describe their own strategies, and to apply explicit strategies; thus, the strategies effectively employed by the human subject have been influenced by the modelling process itself. On this basis, good qualitative (...)
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  49. Zhengxin Chen (1993). From Participatory Design to Participating Problem Solving: Enhancing System Adaptability Through User Modelling. [REVIEW] AI and Society 7 (3):238-247.score: 286.0
    The issue on the role of users in knowledge-based systems can be investigated from two aspects: the design aspect and the functionality aspect. Participatory design is an important approach for the first aspect while system adaptability supported by user modelling is crucial to the second aspect. In the article, we discuss the second aspect. We view a knowledge-based computer system as the partner of users' problem-solving process, and we argue that the system functionality can be enhanced by adapting the (...)
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  50. Anna Conte & Peter G. Moffatt (2014). The Econometric Modelling of Social Preferences. Theory and Decision 76 (1):119-145.score: 284.0
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