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  1. Barbara Abbott (1998). Seiki Akama, Ed., Logic, Language and Computation Reviewed By. Philosophy in Review 18 (5):313-314.
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  2. Barbara Abbott (1998). Seiki Akama, Ed., Logic, Language and Computation. [REVIEW] Philosophy in Review 18:313-314.
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  3. Harold Abelson & Nancy Forbes (2000). Amorphous Computing. Complexity 5 (3):22.
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  4. Karl Aberer (1995). Algebra of Approximate Computation. In Erwin Engeler (ed.), The Combinatory Programme. Birkhäuser. pp. 77--96.
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  5. Andrew Adamatzky & Jeff Jones (2016). On Using Compressibility to Detect When Slime Mould Completed Computation. Complexity 21 (5):162-175.
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  6. K. Aihara & J. K. Ryeu (2001). Toward an Interpretation of Dynamic Neural Activity in Terms of Chaotic Dynamical Systems-Open Peer Commentary-Chaotic Neurons and Analog Computation. Behavioral and Brain Sciences 24 (5):810-810.
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  7. Bill Albrecht, Ken Christensen, Venu Dasigi, Jim Huggins & Jody Paul (2012). The Pledge of the Computing Professional: Recognizing and Promoting Ethics in the Computing Professions. Acm Sigcas Computers and Society 42 (1):6-8.
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  8. Martyn Amos, Alan Gibbons & Paul E. Dunne (1998). Toward Feasible and Efficient DNA Computation. Complexity 4 (1):20-24.
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  9. Bruno Bachimont (2008). Formal Signs and Numerical Computation: Between Intuitionism and Formalism. Critique of Computational Reason. In Jan Lazardzig, Ludger Schwarte & Helmar Schramm (eds.), Theatrum Scientiarum - English Edition, Volume 2, Instruments in Art and Science: On the Architectonics of Cultural Boundaries in the 17th Century. De Gruyter. pp. 362-382.
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  10. David H. Bailey & Jonathan M. Borwein, Experimental Computation as an Ontological Game Changer: The Impact of Modern Mathematical Computation Tools on the Ontology of Mathematics.
    Robust, concrete and abstract, mathematical computation and inference on the scale now becoming possible should change the discourse about many matters mathematical. These include: what mathematics is, how we know something, how we persuade each other, what suffices as a proof, the infinite, mathematical discovery or invention, and other such issues.
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  11. Anouk Barberousse & Cyrille Imbert (2014). Recurring Models and Sensitivity to Computational Constraints. The Monist 97 (3):259-279.
    Why are some models, like the harmonic oscillator, the Ising model, a few Hamiltonian equations in quantum mechanics, the poisson equation, or the Lokta-Volterra equations, repeatedly used within and across scientific domains, whereas theories allow for many more modeling possibilities? Some historians and philosophers of science have already proposed plausible explanations. For example, Kuhn and Cartwright point to a tendency toward conservatism in science, and Humphreys emphasizes the importance of the intractability of what he calls “templates.” This paper investigates more (...)
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  12. H. B. Barlow (1994). What is the Computational Goal of the Neocortex. In Christof Koch & J. Davis (eds.), Large-Scale Neuronal Theories of the Brain. MIT Press. pp. 1--22.
  13. O. Barrera & A. C. F. Cocks (2013). Computational Modelling of Hydrogen Embrittlement in Welded Structures. Philosophical Magazine 93 (20):2680-2700.
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  14. Umut Baysan (2015). Realization Relations in Metaphysics. Minds and Machines (3):1-14.
    “Realization” is a technical term that is used by metaphysicians, philosophers of mind, and philosophers of science to denote some dependence relation that is thought to obtain between higher-level properties and lower-level properties. It is said that mental properties are realized by physical properties; functional and computational properties are realized by first-order properties that occupy certain causal/functional roles; dispositional properties are realized by categorical properties; so on and so forth. Given this wide usage of the term “realization”, it would be (...)
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  15. William Bechtel (forthcoming). Using Computational Models to Discover and Understand Mechanisms. Studies in History and Philosophy of Science Part A.
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  16. R. Becker (1988). The Practical Limitations on Computing. South African Journal of Philosophy-Suid-Afrikaanse Tydskrif Vir Wysbegeerte 7 (2):66-72.
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  17. Lev Beklemishev, Ruy de Queiroz & Andre Scedrov (2012). 18th Workshop on Logic, Language, Information and Computation (Wollic 2011). Bulletin of Symbolic Logic 18 (1):152-153.
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  18. Meurig Beynon (2011). Computing, and Consciousness. In David Clarke & Eric F. Clarke (eds.), Music and Consciousness: Philosophical, Psychological, and Cultural Perspectives. Oxford University Press. pp. 157.
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  19. Mark J. Bishop & Yasemin Erden (eds.) (2013). The Scandal of Computation - What is Computation? - AISB Convention 2013. AISB.
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  20. John Black (1999). Symbols, Computation, and Intentionality. Review of Metaphysics 52 (4):945-947.
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  21. Patrick Blackburn, Nick Braisby, Lawrence Cavedon & Atsushi Shimojima (eds.) (2001). Logic, Language and Computation, Volume 3. Center for the Study of Language and Inf.
    With the rise of the internet and the proliferation of technology to gather and organize data, our era has been defined as "the information age." With the prominence of information as a research concept, there has arisen an increasing appreciation of the intertwined nature of fields such as logic, linguistics, and computer science that answer the questions about information and the ways it can be processed. The many research traditions do not agree about the exact nature of information. By bringing (...)
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  22. Max O. Bloomfield, David F. Richards & Timothy S. Cale† (2003). A Computational Framework for Modelling Grain-Structure Evolution in Three Dimensions. Philosophical Magazine 83 (31-34):3549-3568.
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  23. M. A. Boden (2008). An Evaluation of Computational Modeling in Cognitive Science. In Ron Sun (ed.), The Cambridge Handbook of Computational Psychology. Cambridge University Press. pp. 667--683.
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  24. Kovas Boguta (2005). Complexity and the Paradigm of Wolfram's A New Kind of Science: From the Computational Sciences to the Science of Computation. Complexity 10 (4):15-21.
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  25. Eduardo Bonelli & Federico Feller (2012). Justification Logic as a Foundation for Certifying Mobile Computation. Annals of Pure and Applied Logic 163 (7):935-950.
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  26. Adrienne van den Boogaard (2008). Inleiding: 'History of Computing'. Geschiedschrijving Over Computers En Computergebruik in Nederland. Studium 1 (2):89.
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  27. Fabio Boschetti (forthcoming). Models and People: An Alternative View of the Emergent Properties of Computational Models. Complexity:n/a-n/a.
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  28. Fabio Boschetti (2011). Causality, Emergence, Computation and Unreasonable Expectations. Synthese 181 (3):405-412.
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  29. Stephen Boyd Davis & Simone Gristwood (2016). Computing, Design, Art: Reflections on an Innovative Moment in History. In .
    The chapter is concerned with the role of art and design in the history and philosophy of computing, and the role of computing in models of design and art. It offers insights arising from research into a period in the 1960s and 70s, particularly in the UK, when computing became more available to artists and designers, focusing on Bruce Archer and John Lansdown in London. It suggests that models of computing interacted with conceptualisations of art, design and creative activities in (...)
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  30. A. Briggle (2008). Representation in Digital Systems. In P. Brey, A. Briggle & K. Waelbers (eds.), Current Issues in Computing and Philosophy. Ios Press. pp. 175--116.
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  31. Adam Briggle, Katinka Waelbers & Brey Philip (eds.) (2008). Current Issues in Computing and Philosophy. IOS Press.
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  32. Sehner Bringsjord (1998). Philosophy and 'Super'computation. In T. W. Bynum & J. Moor (eds.), The Digital Phoenix. Cambridge: Blackwell. pp. 231--252.
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  33. Selmer Bringsjord, Explaining Phi Without Dennett's Exotica: Good Ol' Computation Suffices.
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  34. Selmer Bringsjord & Michael Zenzen (2002). Toward a Formal Philosophy of Hypercomputation. Minds and Machines 12 (2):241-258.
    Does what guides a pastry chef stand on par, from the standpoint of contemporary computer science, with what guides a supercomputer? Did Betty Crocker, when telling us how to bake a cake, provide an effective procedure, in the sense of `effective' used in computer science? According to Cleland, the answer in both cases is ``Yes''. One consequence of Cleland's affirmative answer is supposed to be that hypercomputation is, to use her phrase, ``theoretically viable''. Unfortunately, though we applaud Cleland's ``gadfly philosophizing'' (...)
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  35. Stephen D. Brown, Robert J. Francis, Jonathan Rose & Zvonko G. Vranesic (1992). Field-Programmable Gate Arrays.
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  36. Terrell Ward Bynum (2014). On the Possibility of Quantum Informational Structural Realism. Minds and Machines 24 (1):123-139.
    In The Philosophy of Information, Luciano Floridi presents an ontological theory of Being qua Being, which he calls “Informational Structural Realism”, a theory which applies, he says, to every possible world. He identifies primordial information (“dedomena”) as the foundation of any structure in any possible world. The present essay examines Floridi’s defense of that theory, as well as his refutation of “Digital Ontology” (which some people might confuse with his own). Then, using Floridi’s ontology as a starting point, the present (...)
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  37. Laureano Cabanero & C. G. Small (2009). Intentionality and Computationalism: A Diagonal Argument. Mind and Matter 7 (1):81-90.
    Computationalism is the claim that all possible thoughts are computations, i.e. executions of algorithms. The aim of the paper is to show that if intentionality is semantically clear, in a way defined in the paper, then computationalism must be false. Using a convenient version of the phenomenological relation of intentionality and a diagonalization device inspired by Thomson's theorem of 1962, we show there exists a thought that cannot be a computation.
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  38. Liming Cai, Jianer Chen, Rodney G. Downey & Michael R. Fellows (1997). On the Parameterized Complexity of Short Computation and Factorization. Archive for Mathematical Logic 36 (4-5):321-337.
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  39. C. S. Calude & J. L. Casti (1998). Introduction to Unconventional Models of Computation. Complexity 4 (1):13-13.
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  40. Wesley Calvert (forthcoming). On Three Notions of Effective Computation Over R. Logic Journal of the IGPL.
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  41. Paco Calvo & John Symons, Radical Embodiment and Morphological Computation: Against the Autonomy of (Some) Special Sciences.
    An asymmetry between the demands at the computational and algorithmic levels of description furnishes the illusion that the abstract profile at the computational level can be multiply realized, and that something is actually being shared at the algorithmic one. A disembodied rendering of the situation lays the stress upon the different ways in which an algorithm can be implemented. However, from an embodied approach, things look rather different. The relevant pairing, I shall argue, is not between implementation and algorithm, but (...)
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  42. John L. Casti (1997). Computing the Uncomputable. Complexity 2 (3):7-12.
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  43. Ronald L. Chrisley, (Ronc@Cogs.Susx.Ac. Uk).
    imply that computational states are not "real", and cannot, for example, provide a foundation for the cognitive sciences. In particular, Putnam has argued that every ordinary open physical system realizes every abstract finite automaton, implying that the fact that a particular computational characterization applies to a physical system does not tell one anything about the nature of that system. Putnam's argument is scrutinized, and found inadequate because, among other things, it employs a notion of causation that is too weak. I (...)
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  44. Ronald L. Chrisley (1992). Taking Embodiment Seriously Non-Conceptual Content and Computation. School of Cognitive and Computing Sciences, University of Sussex.
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  45. Andy Clark (1998). Magic Words: How Language Augments Human Computation. In Peter Carruthers & Jill Boucher (eds.), Language and Thought: Interdisciplinary Themes. Cambridge: Cambridge University Press. pp. 162-183.
    Of course, words aren’t magic. Neither are sextants, compasses, maps, slide rules and all the other paraphenelia which have accreted around the basic biological brains of homo sapiens. In the case of these other tools and props, however, it is transparently clear that they function so as to either carry out or to facilitate computational operations important to various human projects. The slide rule transforms complex mathematical problems (ones that would baffle or tax the unaided subject) into simple tasks of (...)
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  46. D. S. Clarke Jr (1988). Consciousness and the Computational Mind. Review of Metaphysics 42 (1):147-149.
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  47. Carol Cleland (forthcoming). Effective Procedures and Causal Processes. Minds and Machines.
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  48. Cristian Cocos (2002). Computational Processes: A Reply to Chalmers and Copeland. SATS 3 (2).
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  49. Jonathan Cohen (2012). Computation and the Ambiguity of Perception. In Gary Hatfield & Sarah Allred (eds.), Visual Experience: Sensation, Cognition, and Constancy. Oxford University Press. pp. 160.
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  50. William P. Coleman (forthcoming). Models of Computational Processes. Journal of Symbolic Logic.(Presented at the Spring Meeting 1989 of the Association for Symbolic Logic. Manuscript in Progress.).
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