Search results for 'Semantics Mathematical models' (try it on Scholar)

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  1. M. V. Aldridge (1992). The Elements of Mathematical Semantics. Mouton De Gruyter.score: 369.0
    Chapter Some topics in semantics Aims of this study The central preoccupation of this study is semantic. It is intended as a modest contribution to the ...
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  2. Gian-Carlo Rota, David H. Sharp & Robert Sokolowski (1988). Syntax, Semantics, and the Problem of the Identity of Mathematical Objects. Philosophy of Science 55 (3):376-386.score: 216.0
    A plurality of axiomatic systems can be interpreted as referring to one and the same mathematical object. In this paper we examine the relationship between axiomatic systems and their models, the relationships among the various axiomatic systems that refer to the same model, and the role of an intelligent user of an axiomatic system. We ask whether these relationships and this role can themselves be formalized.
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  3. Curt F. Fey (1961). An Investigation of Some Mathematical Models for Learning. Journal of Experimental Psychology 61 (6):455.score: 196.0
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  4. Waldemar Skrzypczak (2006). Analog-Based Modelling of Meaning Representations in English. Nicolaus Copernicus University Press.score: 195.0
     
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  5. Christian Hennig (2010). Mathematical Models and Reality: A Constructivist Perspective. [REVIEW] Foundations of Science 15 (1):29-48.score: 194.7
    To explore the relation between mathematical models and reality, four different domains of reality are distinguished: observer-independent reality (to which there is no direct access), personal reality, social reality and mathematical/formal reality. The concepts of personal and social reality are strongly inspired by constructivist ideas. Mathematical reality is social as well, but constructed as an autonomous system in order to make absolute agreement possible. The essential problem of mathematical modelling is that within mathematics there is (...)
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  6. Marcello Barbieri (2003). The Organic Codes: An Introduction to Semantic Biology. Cambridge University Press.score: 189.0
    The genetic code appeared on Earth with the first cells. The codes of cultural evolution arrived almost four billion years later. These are the only codes that are recognized by modern biology. In this book, however, Marcello Barbieri explains that there are many more organic codes in nature, and their appearance not only took place throughout the history of life but marked the major steps of that history. A code establishes a correspondence between two independent 'worlds', and the codemaker is (...)
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  7. David Michael Kaplan & Carl F. Craver (2011). The Explanatory Force of Dynamical and Mathematical Models in Neuroscience: A Mechanistic Perspective. Philosophy of Science 78 (4):601-627.score: 180.0
    We argue that dynamical and mathematical models in systems and cognitive neuro- science explain (rather than redescribe) a phenomenon only if there is a plausible mapping between elements in the model and elements in the mechanism for the phe- nomenon. We demonstrate how this model-to-mechanism-mapping constraint, when satisfied, endows a model with explanatory force with respect to the phenomenon to be explained. Several paradigmatic models including the Haken-Kelso-Bunz model of bimanual coordination and the difference-of-Gaussians model of visual (...)
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  8. Harry C. Bunt (1985). Mass Terms and Model-Theoretic Semantics. Cambridge University Press.score: 179.0
    'Mass terms', words like water, rice and traffic, have proved very difficult to accommodate in any theory of meaning since, unlike count nouns such as house or dog, they cannot be viewed as part of a logical set and differ in their grammatical properties. In this study, motivated by the need to design a computer program for understanding natural language utterances incorporating mass terms, Harry Bunt provides a thorough analysis of the problem and offers an original and detailed solution. An (...)
     
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  9. Roberta L. Millstein, Robert A. Skipper Jr & Michael R. Dietrich (2009). (Mis)Interpreting Mathematical Models: Drift as a Physical Process. Philosophy and Theory in Biology 1 (20130604):e002.score: 168.0
    Recently, a number of philosophers of biology (e.g., Matthen and Ariew 2002; Walsh, Lewens, and Ariew 2002; Pigliucci and Kaplan 2006; Walsh 2007) have endorsed views about random drift that, we will argue, rest on an implicit assumption that the meaning of concepts such as drift can be understood through an examination of the mathematical models in which drift appears. They also seem to implicitly assume that ontological questions about the causality (or lack thereof) of terms appearing in (...)
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  10. Philippe Tracqui (1995). From Passive Diffusion to Active Cellular Migration in Mathematical Models of Tumour Invasion. Acta Biotheoretica 43 (4).score: 168.0
    Mathematical models of tumour invasion appear as interesting tools for connecting the information extracted from medical imaging techniques and the large amount of data collected at the cellular and molecular levels. Most of the recent studies have used stochastic models of cell translocation for the comparison of computer simulations with histological solid tumour sections in order to discriminate and characterise expansive growth and active cell movements during host tissue invasion. This paper describes how a deterministic approach based (...)
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  11. R. Paul Thompson (2010). Causality, Mathematical Models and Statistical Association: Dismantling Evidence‐Based Medicine. Journal of Evaluation in Clinical Practice 16 (2):267-275.score: 166.7
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  12. Steffen Ducheyne (2005). Mathematical Models in Newton's Principia: A New View of the 'Newtonian Style'. International Studies in the Philosophy of Science 19 (1):1 – 19.score: 164.0
    In this essay I argue against I. Bernard Cohen's influential account of Newton's methodology in the Principia: the 'Newtonian Style'. The crux of Cohen's account is the successive adaptation of 'mental constructs' through comparisons with nature. In Cohen's view there is a direct dynamic between the mental constructs and physical systems. I argue that his account is essentially hypothetical-deductive, which is at odds with Newton's rejection of the hypothetical-deductive method. An adequate account of Newton's methodology needs to show how Newton's (...)
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  13. Alex Mintz, Nehemia Geva & Karl Derouen (1994). Mathematical Models of Foreign Policy Decision-Making: Compensatory Vs. Noncompensatory. Synthese 100 (3):441 - 460.score: 164.0
    There are presently two leading foreign policy decision-making paradigms in vogue. The first is based on the classical or rational model originally posited by von Neumann and Morgenstern to explain microeconomic decisions. The second is based on the cybernetic perspective whose groundwork was laid by Herbert Simon in his early research on bounded rationality. In this paper we introduce a third perspective — thepoliheuristic theory of decision-making — as an alternative to the rational actor and cybernetic paradigms in international relations. (...)
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  14. Alex Mintz, Nehemia Geva & Karl Derouen Jr (1994). Mathematical Models of Foreign Policy Decision-Making: Compensatory Vs. Noncompensatory. Synthese 100 (3):441 - 460.score: 164.0
    There are presently two leading foreign policy decision-making paradigms in vogue. The first is based on the classical or rational model originally posited by von Neumann and Morgenstern to explain microeconomic decisions. The second is based on the cybernetic perspective whose groundwork was laid by Herbert Simon in his early research on bounded rationality. In this paper we introduce a third perspective -- the poliheuristic theory of decision-making -- as an alternative to the rational actor and cybernetic paradigms in international (...)
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  15. Jeff Mitchell & Mirella Lapata (2010). Composition in Distributional Models of Semantics. Cognitive Science 34 (8):1388-1429.score: 156.0
    Vector-based models of word meaning have become increasingly popular in cognitive science. The appeal of these models lies in their ability to represent meaning simply by using distributional information under the assumption that words occurring within similar contexts are semantically similar. Despite their widespread use, vector-based models are typically directed at representing words in isolation, and methods for constructing representations for phrases or sentences have received little attention in the literature. This is in marked contrast to experimental (...)
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  16. Wesley H. Holliday (forthcoming). Partiality and Adjointness in Modal Logic. In Rajeev Gore, Barteld Kooi & Agi Kurucz (eds.), Advances in Modal Logic, Vol. 10. College Publications. 313-332.score: 153.0
    Following a proposal of Humberstone, this paper studies a semantics for modal logic based on partial “possibilities” rather than total “worlds.” There are a number of reasons, philosophical and mathematical, to find this alternative semantics attractive. Here we focus on the construction of possibility models with a finitary flavor. Our main completeness result shows that for a number of standard modal logics, we can build a canonical possibility model, wherein every logically consistent formula is satisfied, by (...)
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  17. Paolo Palladino (1991). Defining Ecology: Ecological Theories, Mathematical Models, and Applied Biology in the 1960s and 1970s. [REVIEW] Journal of the History of Biology 24 (2):223 - 243.score: 152.0
    Ever since the early decades of this century, there have emerged a number of competing schools of ecology that have attempted to weave the concepts underlying natural resource management and natural-historical traditions into a formal theoretical framework. It was widely believed that the discovery of the fundamental mechanisms underlying ecological phenomena would allow ecologists to articulate mathematically rigorous statements whose validity was not predicated on contingent factors. The formulation of such statements would elevate ecology to the standing of a rigorous (...)
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  18. Luke Jerzykiewicz (2012). Mathematical Realism and Conceptual Semantics. In Oleg Prosorov & Vladimir Orevkov (eds.), Philosophy, Mathematics, Linguistics: Aspects of Interaction. Euler International Mathematical Institute.score: 146.0
    The dominant approach to analyzing the meaning of natural language sentences that express mathematical knowl- edge relies on a referential, formal semantics. Below, I discuss an argument against this approach and in favour of an internalist, conceptual, intensional alternative. The proposed shift in analytic method offers several benefits, including a novel perspective on what is required to track mathematical content, and hence on the Benacerraf dilemma. The new perspective also promises to facilitate discussion between philosophers of mathematics (...)
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  19. Anatol Rapoport (1963). Mathematical Models of Social Interaction. In D. Luce (ed.), Handbook of Mathematical Psychology. John Wiley & Sons.. 2--493.score: 146.0
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  20. P. B. Andrews (2002). An Introduction to Mathematical Logic and Type Theory: To Truth Through Proof. Kluwer Academic Publishers.score: 144.0
    This introduction to mathematical logic starts with propositional calculus and first-order logic. Topics covered include syntax, semantics, soundness, completeness, independence, normal forms, vertical paths through negation normal formulas, compactness, Smullyan's Unifying Principle, natural deduction, cut-elimination, semantic tableaux, Skolemization, Herbrand's Theorem, unification, duality, interpolation, and definability. The last three chapters of the book provide an introduction to type theory (higher-order logic). It is shown how various mathematical concepts can be formalized in this very expressive formal language. This expressive (...)
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  21. Harry Howard (2004). Neuromimetic Semantics: Coordination, Quantification, and Collective Predicates. Elsevier.score: 144.0
    This book attempts to marry truth-conditional semantics with cognitive linguistics in the church of computational neuroscience. To this end, it examines the truth-conditional meanings of coordinators, quantifiers, and collective predicates as neurophysiological phenomena that are amenable to a neurocomputational analysis. Drawing inspiration from work on visual processing, and especially the simple/complex cell distinction in early vision (V1), we claim that a similar two-layer architecture is sufficient to learn the truth-conditional meanings of the logical coordinators and logical quantifiers. As a (...)
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  22. Pietro Galliani (2013). General Models and Entailment Semantics for Independence Logic. Notre Dame Journal of Formal Logic 54 (2):253-275.score: 144.0
    We develop a semantics for independence logic with respect to what we will call general models. We then introduce a simpler entailment semantics for the same logic, and we reduce the validity problem in the former to the validity problem in the latter. Then we build a proof system for independence logic and prove its soundness and completeness with respect to entailment semantics.
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  23. Anne Preller & Mehrnoosh Sadrzadeh (2011). Semantic Vector Models and Functional Models for Pregroup Grammars. Journal of Logic, Language and Information 20 (4):419-443.score: 142.7
    We show that vector space semantics and functional semantics in two-sorted first order logic are equivalent for pregroup grammars. We present an algorithm that translates functional expressions to vector expressions and vice-versa. The semantics is compositional, variable free and invariant under change of order or multiplicity. It includes the semantic vector models of Information Retrieval Systems and has an interior logic admitting a comprehension schema. A sentence is true in the interior logic if and only if (...)
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  24. Jacques Ricard & Käty Ricard (1997). Mathematical Models in Biology. In. In Evandro Agazzi & György Darvas (eds.), Philosophy of Mathematics Today. Kluwer. 299--304.score: 142.0
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  25. C. L. Hamblin (1971). Mathematical Models of Dialogue. Theoria 37 (2):130-155.score: 140.0
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  26. Christopher Pincock (2012). Mathematical Models of Biological Patterns: Lessons From Hamilton's Selfish Herd. Biology and Philosophy 27 (4):481-496.score: 140.0
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  27. David Berlinski (1975). Mathematical Models of the World. Synthese 31 (2):211 - 227.score: 140.0
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  28. Adam Morton (1993). Mathematical Models: Questions of Trustworthiness. British Journal for the Philosophy of Science 44 (4):659-674.score: 140.0
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  29. Eugen Altschul & Erwin Biser (1948). The Validity of Unique Mathematical Models in Science. Philosophy of Science 15 (1):11-24.score: 140.0
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  30. Frank M. Doan (1960). On the Organizational Base of Language with Special Reference to Mathematical Models. Philosophy and Phenomenological Research 21 (2):239-247.score: 140.0
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  31. Mehmet Elgin (2010). Mathematical Models, Explanation, Laws, and Evolutionary Biology. History and Philosophy of the Life Sciences 32 (4).score: 140.0
  32. David H. Helman (1986). Situation Semantics and Models of Analogy. Philosophical Studies 49 (2):231 - 244.score: 140.0
    The preceding theory represents, I believe, a large improvement over conceptual graph theories of analogy. In particular, it is possible for analogical reasoning to be flexible or ‘creative’ on this approach, an aspect of analogy that is not accounted for in conceptual graph theories. I also believe that searching by constraint violations is a more reasonable way to organize memory search than to look for properties of conceptual hierarchies. Proof of this last point, however, awaits an more detailed classification of (...)
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  33. Joseph Goguen (2006). Mathematical Models of Cognitive Space and Time. In. In D. Andler, M. Okada & I. Watanabe (eds.), Reasoning and Cognition. 125--128.score: 140.0
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  34. Timothy J. O'Donnell, Marc D. Hauser & W. Tecumseh Fitch (2005). Using Mathematical Models of Language Experimentally. Trends in Cognitive Sciences 9 (6):284-289.score: 140.0
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  35. M. Thieullen (2009). Self Organization and Evolution in Mathematical Models. In Maryvonne Gérin & Marie-Christine Maurel (eds.), Origins of Life: Self-Organization and/or Biological Evolution? Edp Sciences. 37--46.score: 140.0
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  36. Richard M. Warren (1989). The Use of Mathematical Models in Perceptual Theory. Behavioral and Brain Sciences 12 (4):776.score: 140.0
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  37. Linda B. Greaver, G. Wei, Stephen M. Marson, Cynthia H. Herndon & James Rogers (2006). United States Low Birth Weight Since 1950: Distributions, Impacts, Causes, Costs, Patterns, Mathematical Models, Prediction and Prevention (I). Inquiry 7 (2):131-144.score: 140.0
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  38. Andrea Loettgers (2007). Getting Abstract Mathematical Models in Touch with Nature. Science in Context 20 (1):97.score: 140.0
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  39. Dominik Wodarz & Martin A. Nowak (2002). Mathematical Models of HIV Pathogenesis and Treatment. Bioessays 24 (12):1178-1187.score: 140.0
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  40. Joseph S. Alper & Robert V. Lange (1984). Mathematical Models for Gene–Culture Coevolution. Behavioral and Brain Sciences 7 (4):739.score: 140.0
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  41. John C. Barrett (1974). Conception and Birth Mathematical Models of Conception and Birth Mindel C. Sheps Jane A. Menken. Bioscience 24 (10):598-598.score: 140.0
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  42. S. G. Bloom & G. E. Raines (1971). Mathematical Models for Predicting the Transport of Radionuclides in a Marine Environment. Bioscience 21 (12):691-696.score: 140.0
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  43. Daniel Breslau & Yuval Yonay (1999). Beyond Metaphor: Mathematical Models in Economics as Empirical Research. Science in Context 12 (2).score: 140.0
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  44. Valerie Debuiche (2013). Leibnizian Expression and its Mathematical Models. Journal of the History of Philosophy 51 (3):409-439.score: 140.0
     
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  45. Josephine Donaghy (2014). Temporal Decomposition: A Strategy for Building Mathematical Models of Complex Metabolic Systems. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 48:1-11.score: 140.0
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  46. John V. Gillespie & Dina A. Zinnes (1975). Progressions in Mathematical Models of International Conflict. Synthese 31 (2):289 - 321.score: 140.0
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  47. Robert J. Good (1977). Modeling Cell Rearrangement Mathematical Models for Cell Rearrangement G. D. Mostow. Bioscience 27 (12):816-816.score: 140.0
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  48. Ileana Maria Greca & Marco Antonio Moreira (2002). Mental, Physical, and Mathematical Models in the Teaching and Learning of Physics. Science Education 86 (1):106-121.score: 140.0
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  49. L. E. Grinin & A. V. Korotayev (2009). Urbanization and Political Instability: To the Working Out Mathematical Models of Political Processes. Polis 4:34-52.score: 140.0
     
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  50. Carlos Madrid (2009). Do Mathematical Models Represent the World? : The Case of Quantum Mathematical Models. In González Recio & José Luis (eds.), Philosophical Essays on Physics and Biology. G. Olms.score: 140.0
     
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