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  1. Paulo Abrantes (1999). Analogical Reasoning and Modeling in the Sciences. Foundations of Science 4 (3):237-270.
    This paper aims at integrating the work onanalogical reasoning in Cognitive Science into thelong trend of philosophical interest, in this century,in analogical reasoning as a basis for scientificmodeling. In the first part of the paper, threesimulations of analogical reasoning, proposed incognitive science, are presented: Gentner''s StructureMatching Engine, Mitchel''s and Hofstadter''s COPYCATand the Analogical Constraint Mapping Engine, proposedby Holyoak and Thagard. The differences andcontroversial points in these simulations arehighlighted in order to make explicit theirpresuppositions concerning the nature of analogicalreasoning. In the (...)
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  2. Peter Achinstein (1972). Models and Analogies: A Reply to Girill. Philosophy of Science 39 (2):235-240.
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  3. Peter Achinstein (1964). Models, Analogies, and Theories. Philosophy of Science 31 (4):328-350.
    Recent accounts of scientific method suggest that a model, or analogy, for an axiomatized theory is another theory, or postulate set, with an identical calculus. The present paper examines five central theses underlying this position. In the light of examples from physical science it seems necessary to distinguish between models and analogies and to recognize the need for important revisions in the position under study, especially in claims involving an emphasis on logical structure and similarity in form between theory and (...)
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  4. Joseph Agassi (1964). Analogies as Generalizations. Philosophy of Science 31 (4):351-356.
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  5. Theodore Bach (2014). A Unified Account of General Learning Mechanisms and Theory‐of‐Mind Development. Mind and Language 29 (3):351-381.
    Modularity theorists have challenged that there are, or could be, general learning mechanisms that explain theory-of-mind development. In response, supporters of the ‘scientific theory-theory’ account of theory-of-mind development have appealed to children's use of auxiliary hypotheses and probabilistic causal modeling. This article argues that these general learning mechanisms are not sufficient to meet the modularist's challenge. The article then explores an alternative domain-general learning mechanism by proposing that children grasp the concept belief through the progressive alignment of relational structure that (...)
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  6. Jack C. Carloye (1971). An Interpretation of Scientific Models Involving Analogies. Philosophy of Science 38 (4):562-569.
    In order to account for the actual function of analogue models in extending theories to new domains, we argue that it is necessary to analyze the inference involved into a complex two dimensional form. This form must go horizontally from descriptions of entities used as a model to redescriptions of entities in the new domain, and it must go vertically from an observation language to a theoretical language having a different and exclusive logical syntax. This complex inference can only be (...)
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  7. A. Charles Catania (2000). Metaphors, Models, and Mathematics in the Science of Behavior. Behavioral and Brain Sciences 23 (1):94-95.
    Metaphors and models involve correspondences between events in separate domains. They differ in the form and precision of how the correspondences are expressed. Examples include correspondences between phylogenic and ontogenic selection, and wave and particle metaphors of the mathematics of quantum physics. An implication is that the target article's metaphors of resistance to change may have heuristic advantages over those of momentum.
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  8. Lindley Darden (1982). Artificial Intelligence and Philosophy of Science: Reasoning by Analogy in Theory Construction. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1982:147 - 165.
    This paper examines the hypothesis that analogies may play a role in the generation of new ideas that are built into new explanatory theories. Methods of theory construction by analogy, by failed analogy, and by modular components from several analogies are discussed. Two different analyses of analogy are contrasted: direct mapping (Mary Hesse) and shared abstraction (Michael Genesereth). The structure of Charles Darwin's theory of natural selection shows various analogical relations. Finally, an "abstraction for selection theories" is shown to be (...)
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  9. Eric Dietrich (2010). Analogical Insight: Toward Unifying Categorization and Analogy. Cognitive Processing 11 (4):331-.
    The purpose of this paper is to present two kinds of analogical representational change, both occurring early in the analogy-making process, and then, using these two kinds of change, to present a model unifying one sort of analogy-making and categorization. The proposed unification rests on three key claims: (1) a certain type of rapid representational abstraction is crucial to making the relevant analogies (this is the first kind of representational change; a computer model is presented that demonstrates this kind of (...)
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  10. Eric Dietrich (2008). Toward Extending the Relational Priming Model: Six Questions. Behavioral and Brain Sciences 31 (4):383-384.
    Six questions are posed that are really specific versions of this question: How can Leech et al.'s system be extended to handle adult-level analogies that frequently combine concepts from semantically distant domains sharing few relational labels and that involve the production of abstractions? It is Leech et al. who stress development; finding such an extension would seem to have to be high on their priority list.
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  11. Eric Dietrich (2000). Analogy and Conceptual Change, or You Can't Step Into the Same Mind Twice. In Eric Dietrich Art Markman (ed.), Cognitive Dynamics: Conceptual change in humans and machines. Lawrence Erlbaum. 265--294.
    Sometimes analogy researchers talk as if the freshness of an experience of analogy resides solely in seeing that something is like something else -- seeing that the atom is like a solar system, that heat is like flowing water, that paint brushes work like pumps, or that electricity is like a teeming crowd. But analogy is more than this. Analogy isn't just seeing that the atom is like a solar system; rather, it is seeing something new about the atom, an (...)
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  12. Eric Dietrich, Arthur B. Markman & Michael Winkley (2003). The Prepared Mind: The Role of Representational Change in Chance Discovery. In Yukio Ohsawa Peter McBurney (ed.), Chance Discovery by Machines. Springer-Verlag, pp. 208-230..
    Analogical reminding in humans and machines is a great source for chance discoveries because analogical reminding can produce representational change and thereby produce insights. Here, we present a new kind of representational change associated with analogical reminding called packing. We derived the algorithm in part from human data we have on packing. Here, we explain packing and its role in analogy making, and then present a computer model of packing in a micro-domain. We conclude that packing is likely used in (...)
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  13. Yves Gingras & Alexandre Guay (2011). The Uses of Analogies in Seventeenth and Eighteenth Century Science. Perspectives on Science 19 (2):154-191.
    The uses of analogy are ancient. It can even be argued that analogical thinking is the most basic cognitive tool humans have to move from the unknown to the known (Gentner et al. 2001). As Olson succinctly puts it, “analogies are useful when it is desired to compare an unfamiliar system with one that is better known” (Olson 1943, p. i). Analogical thinking is thus ubiquitous and found in many texts at least since Homer in Antiquity (Lloyd 1966). For example, (...)
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  14. T. R. Girill (1972). Analogies and Models Revisited. Philosophy of Science 39 (2):241-244.
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  15. R. Harre (1988). Where Models and Analogies Really Count. International Studies in the Philosophy of Science 2 (2):118 – 133.
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  16. Mary B. Hesse (1966). Models and Analogies in Science. University of Notre Dame Press.
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  17. Mary B. Hesse (1953). Models in Physics. British Journal for the Philosophy of Science 4 (15):198-214.
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  18. Jürgen Hollatz (1999). Analogy Making in Legal Reasoning with Neural Networks and Fuzzy Logic. Artificial Intelligence and Law 7 (2-3):289-301.
    Analogy making from examples is a central task in intelligent system behavior. A lot of real world problems involve analogy making and generalization. Research investigates these questions by building computer models of human thinking concepts. These concepts can be divided into high level approaches as used in cognitive science and low level models as used in neural networks. Applications range over the spectrum of recognition, categorization and analogy reasoning. A major part of legal reasoning could be formally interpreted as an (...)
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  19. Xiaoxi Huang, Huaxin Huang, Beishui Liao & Cihua Xu (2013). An Ontology-Based Approach to Metaphor Cognitive Computation. Minds and Machines 23 (1):105-121.
    Language understanding is one of the most important characteristics for human beings. As a pervasive phenomenon in natural language, metaphor is not only an essential thinking approach, but also an ingredient in human conceptual system. Many of our ways of thinking and experiences are virtually represented metaphorically. With the development of the cognitive research on metaphor, it is urgent to formulate a computational model for metaphor understanding based on the cognitive mechanism, especially with the view to promoting natural language understanding. (...)
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  20. Tokuyasu Kakuta, Makoto Haraguchi & Yoshiaki Okubo (1997). A Goal-Dependent Abstraction for Legal Reasoning by Analogy. Artificial Intelligence and Law 5 (1-2):97-118.
    This paper presents a new algorithm to find an appropriate similarityunder which we apply legal rules analogically. Since there may exist a lotof similarities between the premises of rule and a case in inquiry, we haveto select an appropriate similarity that is relevant to both thelegal rule and a top goal of our legal reasoning. For this purpose, a newcriterion to distinguish the appropriate similarities from the others isproposed and tested. The criterion is based on Goal-DependentAbstraction (GDA) to select a (...)
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  21. Ehud Lamm (2008). Hopeful Heretic – Richard Goldschmidt’s Genetic Metaphors. History and Philosophy of the Life Sciences 30 (3-4):387-406.
    Richard Goldschmidt famously rejected the notion of atomic and corpuscular genes, arranged on the chromosome like beads-on-a-string. I provide an exegesis of Goldschmidt’s intuition by analyzing his repeated and extensive use of metaphorical language and analogies in his attempts to convey his notion of the nature of the genetic material and specifically the significance of chromosomal pattern. The paper concentrates on Goldschmidt’s use of metaphors in publications spanning 1940-1955. -/- .
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  22. W. H. Leatherdale (1974). The Role of Analogy, Model, and Metaphor in Science. American Elsevier Pub. Co..
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  23. Chuang Liu, Fictional Models in Science.
    In this paper, I begin with a discussion of Giere’s recent work arguing against taking models as works of fiction. I then move on to explore a spectrum of scientific models that goes from the obviously fictional to the not so obviously fictional. And then I discuss the modeling of the unobservable and make a case for the idea that despite difficulties of defining them, unobservable systems are modeled in a fundamentally different way than the observable systems. While idealization and (...)
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  24. Mohan P. Matthen (1997). Teleology and the Product Analogy. Australasian Journal of Philosophy 75 (1):21 – 37.
    This article presents an analogical account of the meaning of function attributions in biology. To say that something has a function analogizes it with an artifact, but since the analogy rests on a necessary (but possibly insufficient) basis, function statements can still be assessed as true or false in an objective sense.
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  25. D. H. Mellor (1968). Models and Analogies in Science. Isis 59:282-90.
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  26. Nicola Mößner (2013). Das Beste aus zwei Welten? Ludwik Fleck über den sozialen Ursprung wissenschaftlicher Kreativität. In Philipp Hubmann & Till Julian Huss (eds.), Simultaneität - Modelle der Gleichzeitigkeit in den Wissenschaften und Künsten. transcript.
  27. María G. Navarro (2006). Analogía, Prudencia y Abducción En la Racionalidad Interpretativa. In Mauricio Beuchot (ed.), Contextos de la hermenéutica analógica. Editorial Torres Asociados.
  28. Darrell P. Rowbottom (2011). The Instrumentalist's New Clothes. Philosophy of Science 78 (5):1200-1211.
    This paper develops a new version of instrumentalism, in light of progress in the realism debate in recent decades, and thereby defends the view that instrumentalism remains a viable philosophical position on science. The key idea is that talk of unobservable objects should be taken literally only when those objects are assigned properties (or described in terms of analogies involving things) with which we are experientially (or otherwise) acquainted. This is derivative from the instrumentalist tradition in so far as the (...)
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  29. Michael Ruse (1973). The Nature of Scientific Models : Formal V Material Analogy. Philosophy of the Social Sciences 3 (1):63-80.
  30. Eric Steinhart (2001). The Logic of Metaphor: Analogous Parts of Possible Worlds. Kluwer Academic.
    The Logic of Metaphor uses techniques from possible worlds semantics to provide formal truth-conditions for many grammatical classes of metaphors. It gives logically precise and practically useful syntactic and semantic rules for generating and interpreting metaphors. These rules are implemented in a working computer program. The book treats the lexicon as a conceptual network with semantics provided by an intensional predicate calculus. It gives rules for finding analogies in such networks. It shows how to syntactically and semantically analyze texts containing (...)
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  31. Michael Weisberg (2013). Simulation and Similarity: Using Models to Understand the World. Oxford University Press.
    one takes to be the most salient, any pair could be judged more similar to each other than to the third. Goodman uses this second problem to showthat there can be no context-free similarity metric, either in the trivial case or in a scientifically ...
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