Results for 'computation'

971 found
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  1. The fortieth annual lecture series 1999-2000.Brain Computations & an Inevitable Conflict - 2000 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 31:199-200.
  2. Randomness and Recursive Enumerability.Siam J. Comput - unknown
    One recursively enumerable real α dominates another one β if there are nondecreasing recursive sequences of rational numbers (a[n] : n ∈ ω) approximating α and (b[n] : n ∈ ω) approximating β and a positive constant C such that for all n, C(α − a[n]) ≥ (β − b[n]). See [R. M. Solovay, Draft of a Paper (or Series of Papers) on Chaitin’s Work, manuscript, IBM Thomas J. Watson Research Center, Yorktown Heights, NY, 1974, p. 215] and [G. J. (...)
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  3.  7
    A Model for Proustian Decay.Computer Lars - 2024 - Nordic Journal of Aesthetics 33 (67).
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  4.  10
    Computer Science Logic: 11th International Workshop, CSL'97, Annual Conference of the EACSL, Aarhus, Denmark, August 23-29, 1997, Selected Papers.M. Nielsen, Wolfgang Thomas & European Association for Computer Science Logic - 1998 - Springer Verlag.
    This book constitutes the strictly refereed post-workshop proceedings of the 11th International Workshop on Computer Science Logic, CSL '97, held as the 1997 Annual Conference of the European Association on Computer Science Logic, EACSL, in Aarhus, Denmark, in August 1997. The volume presents 26 revised full papers selected after two rounds of refereeing from initially 92 submissions; also included are four invited papers. The book addresses all current aspects of computer science logics and its applications and thus presents the state (...)
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  5. Paul M. kjeldergaard.Pittsburgh Computations Centers - 1968 - In T. Dixon & Deryck Horton, Verbal Behavior and General Behavior Theory. Prentice-Hall.
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  6.  22
    Hector freytes, Antonio ledda, Giuseppe sergioli and.Roberto Giuntini & Probabilistic Logics in Quantum Computation - 2013 - In Hanne Andersen, Dennis Dieks, Wenceslao J. Gonzalez, Thomas Uebel & Gregory Wheeler, New Challenges to Philosophy of Science. Springer Verlag. pp. 49.
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  7. Section 2. Model Theory.Va Vardanyan, On Provability Resembling Computability, Proving Aa Voronkov & Constructive Logic - 1989 - In Jens Erik Fenstad, Ivan Timofeevich Frolov & Risto Hilpinen, Logic, methodology, and philosophy of science VIII: proceedings of the Eighth International Congress of Logic, Methodology, and Philosophy of Science, Moscow, 1987. New York, NY, U.S.A.: Sole distributors for the U.S.A. and Canada, Elsevier Science.
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  8. The general problem of the primitive was finally solved in 1912 by A. Den-joy. But his integration process was more complicated than that of Lebesgue. Denjoy's basic idea was to first calculate the definite integral∫ b. [REVIEW]How to Compute Antiderivatives - 1995 - Bulletin of Symbolic Logic 1 (3).
     
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  9.  7
    A logical formalisation of false belief tasks.R. Velázquez-Quesada A. Institute for Logic Anthia Solaki Fernando, Computation Language, Netherlandsb Netherlands Organization for Applied Scientific Research, Media Studies Netherlandsc Information Science & Norway - forthcoming - Journal of Applied Non-Classical Logics:1-51.
    Theory of Mind (ToM), the cognitive capacity to attribute internal mental states to oneself and others, is a crucial component of social skills. Its formal study has become important, witness recent research on reasoning and information update by intelligent agents, and some proposals for its formal modelling have put forward settings based on Epistemic Logic (EL). Still, due to intrinsic idealisations, it is questionable whether EL can be used to model the high-order cognition of ‘real’ agents. This manuscript proposes a (...)
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  10.  4
    Intentional identity revisited.Ahti Pietarinen A. School of Cognitive, Computing Sciences, Falmer, Brighton, BN1 9QH & Uk - 2010 - Nordic Journal of Philosophical Logic 6 (2):147-188.
    The problem of intentional identity, as originally offered by Peter Geach, says that there can be an anaphoric link between an indefinite term and a pronoun across a sentential boundary and across propositional attitude contexts, where the actual existence of an individual for the indefinite term is not presupposed. In this paper, a semantic resolution to this elusive puzzle is suggested, based on a new quantified intensional logic and game-theoretic semantics (GTS) of imperfect information. This constellation leads to an expressive (...)
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  11.  11
    Proceedings of the 1986 Conference on Theoretical Aspects of Reasoning about Knowledge: March 19-22, 1988, Monterey, California.Joseph Y. Halpern, International Business Machines Corporation, American Association of Artificial Intelligence, United States & Association for Computing Machinery - 1986
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  12.  10
    Computer Science Logic 5th Workshop, Csl '91, Berne, Switzerland, October 7-11, 1991 : Proceedings'.Egon Börger, Gerhard Jäger, Hans Kleine Büning & Michael M. Richter - 1992 - Springer Verlag.
    This volume presents the proceedings of the workshop CSL '91 (Computer Science Logic) held at the University of Berne, Switzerland, October 7-11, 1991. This was the fifth in a series of annual workshops on computer sciencelogic (the first four are recorded in LNCS volumes 329, 385, 440, and 533). The volume contains 33 invited and selected papers on a variety of logical topics in computer science, including abstract datatypes, bounded theories, complexity results, cut elimination, denotational semantics, infinitary queries, Kleene algebra (...)
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  13.  20
    Unconventional Computing, Arts, Philosophy.Andrew Adamatzky (ed.) - 2022 - World Scientific Publishing Company.
    The unique compendium re-assesses the value of future and emergent computing technologies via artistic and philosophical means. The book encourages scientists to adopt inspiring thinking of artists and philosophers to reuse scientific concepts in their works.The useful reference text consists of non-typical topics, where artistic and philosophical concepts encourage readers to adopt unconventional approaches towards computing and immerse themselves into discoveries of future emerging landscape.
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  14.  32
    Quantum Computation and Quantum Information.Michael A. Nielsen & Isaac L. Chuang - 2000 - Cambridge University Press.
    First-ever comprehensive introduction to the major new subject of quantum computing and quantum information.
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  15. A computational foundation for the study of cognition.David Chalmers - 2011 - Journal of Cognitive Science 12 (4):323-357.
    Computation is central to the foundations of modern cognitive science, but its role is controversial. Questions about computation abound: What is it for a physical system to implement a computation? Is computation sufficient for thought? What is the role of computation in a theory of cognition? What is the relation between different sorts of computational theory, such as connectionism and symbolic computation? In this paper I develop a systematic framework that addresses all of these (...)
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  16. Computability and Logic.George Boolos, John Burgess, Richard P. & C. Jeffrey - 1980 - New York: Cambridge University Press. Edited by John P. Burgess & Richard C. Jeffrey.
    Computability and Logic has become a classic because of its accessibility to students without a mathematical background and because it covers not simply the staple topics of an intermediate logic course, such as Godel's incompleteness theorems, but also a large number of optional topics, from Turing's theory of computability to Ramsey's theorem. This 2007 fifth edition has been thoroughly revised by John Burgess. Including a selection of exercises, adjusted for this edition, at the end of each chapter, it offers a (...)
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  17. The Computational Complexity of Quantified Reciprocals.Jakub Szymanik - 2009 - In Peter Bosch, David Gabelaia & Jérôme Lang, Lecture Notes on Artificial Intelligence 5422, Logic, Language, and Computation 7th International Tbilisi Symposium on Logic, Language, and Computation. Springer.
    We study the computational complexity of reciprocal sentences with quantified antecedents. We observe a computational dichotomy between different interpretations of reciprocity, and shed some light on the status of the so-called Strong Meaning Hypothesis.
     
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  18.  20
    Proof, Computation and Agency: Logic at the Crossroads.Johan van Benthem, Amitabha Gupta & Rohit Parikh (eds.) - 2011 - Dordrecht, Netherland: Springer.
    Proof, Computation and Agency: Logic at the Crossroads provides an overview of modern logic and its relationship with other disciplines. As a highlight, several articles pursue an inspiring paradigm called 'social software', which studies patterns of social interaction using techniques from logic and computer science. The book also demonstrates how logic can join forces with game theory and social choice theory. A second main line is the logic-language-cognition connection, where the articles collected here bring several fresh perspectives. Finally, the (...)
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  19.  58
    Physical Computation: A Mechanistic Account.Gualtiero Piccinini - 2015 - Oxford, GB: Oxford University Press UK.
    Gualtiero Piccinini articulates and defends a mechanistic account of concrete, or physical, computation. A physical system is a computing system just in case it is a mechanism one of whose functions is to manipulate vehicles based solely on differences between different portions of the vehicles according to a rule defined over the vehicles. Physical Computation discusses previous accounts of computation and argues that the mechanistic account is better. Many kinds of computation are explicated, such as digital (...)
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  20.  30
    Computational History of Philosophy of Science Dataset.Daniel J. Hicks, Rick Morris & Evelyn Brister - unknown
    The Computational History of Philosophy of Science Dataset aims to be a comprehensive set of article and book chapter metadata for philosophy of science. The dataset covers the full run of over 40 journals and 3 major book series in the field. An automated author disambiguation script is used to construct canonical names for each author, and a combination of gender attribution methods is used to attribute the gender of each author. The full code used to generate the dataset is (...)
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  21. Mechanistic Computational Individuation without Biting the Bullet.Nir Fresco & Marcin Miłkowski - 2021 - British Journal for the Philosophy of Science 72 (2):431-438.
    Is the mathematical function being computed by a given physical system determined by the system’s dynamics? This question is at the heart of the indeterminacy of computation phenomenon (Fresco et al. [unpublished]). A paradigmatic example is a conventional electrical AND-gate that is often said to compute conjunction, but it can just as well be used to compute disjunction. Despite the pervasiveness of this phenomenon in physical computational systems, it has been discussed in the philosophical literature only indirectly, mostly with (...)
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  22. Computational Explanation in Cognitive Sciences: The Mechanist Turn.S. Delarivière & J. Frans - 2015 - Constructivist Foundations 10 (3):426-429.
    Upshot: The computational theory of mind has been elaborated in many different ways throughout the last decades. In Explaining the Computational Mind, Milkowski defends his view that the mind can be explained as computational through his defense of mechanistic explanation. At no point in this book is there explicit mention of constructivist approaches to this topic. We will, nevertheless, argue that it is interesting for constructivist readers.
     
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  23. Computation and cognition: Issues in the foundation of cognitive science.Zenon W. Pylyshyn - 1980 - Behavioral and Brain Sciences 3 (1):111-32.
    The computational view of mind rests on certain intuitions regarding the fundamental similarity between computation and cognition. We examine some of these intuitions and suggest that they derive from the fact that computers and human organisms are both physical systems whose behavior is correctly described as being governed by rules acting on symbolic representations. Some of the implications of this view are discussed. It is suggested that a fundamental hypothesis of this approach is that there is a natural domain (...)
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  24. Extending Ourselves: Computational Science, Empiricism, and Scientific Method.Paul Humphreys - 2004 - New York, US: Oxford University Press.
    Computational methods such as computer simulations, Monte Carlo methods, and agent-based modeling have become the dominant techniques in many areas of science. Extending Ourselves contains the first systematic philosophical account of these new methods, and how they require a different approach to scientific method. Paul Humphreys draws a parallel between the ways in which such computational methods have enhanced our abilities to mathematically model the world, and the more familiar ways in which scientific instruments have expanded our access to the (...)
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  25.  12
    , "Computers as modelers of climate," in the greatest inventions of the past.William Calvin - manuscript
    Computer simulations may allow us to understand the earth’s fickle climate and how it is affected by detours of the great ocean currents. These detours cause abrupt coolings -- the average global temperature can drop dramatically in just a few years, with droughts that set up El-Niño-like forest fires even in the tropics. While volcanic eruptions and Antarctic ice shelf collapses can also abruptly cool things, what we’re talking about here is a flip-flop: a few centuries later, there’s an equally (...)
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  26. Neural Computation and the Computational Theory of Cognition.Gualtiero Piccinini & Sonya Bahar - 2013 - Cognitive Science 37 (3):453-488.
    We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism—neural processes are computations in the generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation (...)
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  27. Computation and content.Frances Egan - 1995 - Philosophical Review 104 (2):181-203.
  28. Computer Simulations in Science and Engineering. Concept, Practices, Perspectives.Juan Manuel Durán - 2018 - Springer.
    This book addresses key conceptual issues relating to the modern scientific and engineering use of computer simulations. It analyses a broad set of questions, from the nature of computer simulations to their epistemological power, including the many scientific, social and ethics implications of using computer simulations. The book is written in an easily accessible narrative, one that weaves together philosophical questions and scientific technicalities. It will thus appeal equally to all academic scientists, engineers, and researchers in industry interested in questions (...)
  29. Are Computers Digital? Should Constructivists Care?S. Franchi - 2013 - Constructivist Foundations 9 (1):17-19.
    Open peer commentary on the article “Constructivism and Computation: Can Computer-Based Modeling Add to the Case for Constructivism?” by Manfred Füllsack. Upshot: While I do agree with Füllsack’s positive assessment of the use of computer simulations in advancing constructivism’s program, I am less convinced by the alleged opposition between computers and constructivism he builds up. In my opinion, his depiction of computers and computation is inaccurate in several respects. As a result, the alleged incompatibility with constructivism Füllsack objects (...)
     
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  30.  12
    A Computational Model of Wason's Selection Task.Emmanuel Genot - unknown
    We apply an algorithmic learning model of inquiry to model reasoning carried by experimental subjects in Wason's _Selection Task_ that represents reasoning in the task as computation of a decision tree that supervenes on semantic representations. We argue that the resulting model improves on previous probabilistic and pragmatic models of the task. In particular, it suggests that subjects' selection could in fact be guided by sophisticated patterns of argumentative reasoning.
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  31.  42
    Educational computing: What can philosophy of education contribute?Peter Scrimshaw - 1989 - Journal of Philosophy of Education 23 (1):103–112.
    Peter Scrimshaw; Educational Computing: what can philosophy of education contribute?, Journal of Philosophy of Education, Volume 23, Issue 1, 30 May 2006, Pages.
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  32.  65
    Computability and Randomness.André Nies - 2008 - Oxford, England: Oxford University Press UK.
    The interplay between computability and randomness has been an active area of research in recent years, reflected by ample funding in the USA, numerous workshops, and publications on the subject. The complexity and the randomness aspect of a set of natural numbers are closely related. Traditionally, computability theory is concerned with the complexity aspect. However, computability theoretic tools can also be used to introduce mathematical counterparts for the intuitive notion of randomness of a set. Recent research shows that, conversely, concepts (...)
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  33.  30
    Computation, Dynamics, and Cognition.Marco Giunti - 1997 - Oxford University Press.
    This book explores the application of dynamical theory to cognitive science. Giunti shows how the dynamical approach can illuminate problems of cognition, information processing, consciousness, meaning, and the relation between body and mind.
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  34. On implementing a computation.David J. Chalmers - 1994 - Minds and Machines 4 (4):391-402.
    To clarify the notion of computation and its role in cognitive science, we need an account of implementation, the nexus between abstract computations and physical systems. I provide such an account, based on the idea that a physical system implements a computation if the causal structure of the system mirrors the formal structure of the computation. The account is developed for the class of combinatorial-state automata, but is sufficiently general to cover all other discrete computational formalisms. The (...)
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  35. Analogue Computation and Representation.Corey J. Maley - 2023 - British Journal for the Philosophy of Science 74 (3):739-769.
    Relative to digital computation, analogue computation has been neglected in the philosophical literature. To the extent that attention has been paid to analogue computation, it has been misunderstood. The received view—that analogue computation has to do essentially with continuity—is simply wrong, as shown by careful attention to historical examples of discontinuous, discrete analogue computers. Instead of the received view, I develop an account of analogue computation in terms of a particular type of analogue representation that (...)
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  36.  66
    Computer experiments in harmonic analysis.Michael Barany - unknown
    It is conventionally understood that computers play a rather limited role in theoretical mathematics. While computation is indispensable in applied mathematics and the theory of computing and algorithms is rich and thriving, one does not, even today, expect to find computers in theoretical mathematics settings beyond the theory of computing. Where computers are used, by those studying combinatorics , algebra, number theory, or dynamical systems, the computer most often assumes the role of an automated and speedy theoretician, performing manipulations (...)
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  37. Computational explanation in neuroscience.Gualtiero Piccinini - 2006 - Synthese 153 (3):343-353.
    According to some philosophers, computational explanation is proprietary
    to psychology—it does not belong in neuroscience. But neuroscientists routinely offer computational explanations of cognitive phenomena. In fact, computational explanation was initially imported from computability theory into the science of mind by neuroscientists, who justified this move on neurophysiological grounds. Establishing the legitimacy and importance of computational explanation in neuroscience is one thing; shedding light on it is another. I raise some philosophical questions pertaining to computational explanation and outline some promising answers that (...)
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  38. What is computation?B. Jack Copeland - 1996 - Synthese 108 (3):335-59.
    To compute is to execute an algorithm. More precisely, to say that a device or organ computes is to say that there exists a modelling relationship of a certain kind between it and a formal specification of an algorithm and supporting architecture. The key issue is to delimit the phrase of a certain kind. I call this the problem of distinguishing between standard and nonstandard models of computation. The successful drawing of this distinction guards Turing's 1936 analysis of (...) against a difficulty that has persistently been raised against it, and undercuts various objections that have been made to the computational theory of mind. (shrink)
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  39. Computation as Involving Content: A Response to Egan.Christopher Peacocke - 1999 - Mind and Language 14 (2):195-202.
    Only computational explanations of a content‐involving sort can answer certain ‘how’‐questions; can support content‐involving counterfactuals; and have the generality characteristic of psychological explanations. Purely formal characteriza‐tions of computations have none of these properties, and do not determine content. These points apply not only to psychological explanation, but to Turing machines themselves. Computational explanations which involve content are not opposed to naturalism. They are also required if we are to explain the content‐involving properties of mental states.
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  40. Cognitive Computation sans Representation.Paul Schweizer - 2017 - In Thomas M. Powers, Philosophy and Computing: Essays in epistemology, philosophy of mind, logic, and ethics. Cham: Springer. pp. 65-84.
    The Computational Theory of Mind (CTM) holds that cognitive processes are essentially computational, and hence computation provides the scientific key to explaining mentality. The Representational Theory of Mind (RTM) holds that representational content is the key feature in distinguishing mental from non-mental systems. I argue that there is a deep incompatibility between these two theoretical frameworks, and that the acceptance of CTM provides strong grounds for rejecting RTM. The focal point of the incompatibility is the fact that representational content (...)
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  41. Computability & unsolvability.Martin Davis - 1958 - New York: Dover Publications.
    Classic text considersgeneral theory of computability, computable functions, operations on computable functions, Turing machines self-applied, unsolvable decision problems, applications of general theory, mathematical logic, Kleene hierarchy, computable functionals, classification of unsolvable decision problems and more.
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  42. Computer Simulations as Scientific Instruments.Ramón Alvarado - 2022 - Foundations of Science 27 (3):1183-1205.
    Computer simulations have conventionally been understood to be either extensions of formal methods such as mathematical models or as special cases of empirical practices such as experiments. Here, I argue that computer simulations are best understood as instruments. Understanding them as such can better elucidate their actual role as well as their potential epistemic standing in relation to science and other scientific methods, practices and devices.
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  43. On computational explanations.Anna-Mari Rusanen & Otto Lappi - 2016 - Synthese 193 (12):3931-3949.
    Computational explanations focus on information processing required in specific cognitive capacities, such as perception, reasoning or decision-making. These explanations specify the nature of the information processing task, what information needs to be represented, and why it should be operated on in a particular manner. In this article, the focus is on three questions concerning the nature of computational explanations: What type of explanations they are, in what sense computational explanations are explanatory and to what extent they involve a special, “independent” (...)
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  44. Content, computation and externalism.Oron Shagrir - 2001 - Mind 110 (438):369-400.
    The paper presents an extended argument for the claim that mental content impacts the computational individuation of a cognitive system (section 2). The argument starts with the observation that a cognitive system may simultaneously implement a variety of different syntactic structures, but that the computational identity of a cognitive system is given by only one of these implemented syntactic structures. It is then asked what are the features that determine which of implemented syntactic structures is the computational structure of the (...)
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  45.  99
    Computability, an introduction to recursive function theory.Nigel Cutland - 1980 - New York: Cambridge University Press.
    What can computers do in principle? What are their inherent theoretical limitations? These are questions to which computer scientists must address themselves. The theoretical framework which enables such questions to be answered has been developed over the last fifty years from the idea of a computable function: intuitively a function whose values can be calculated in an effective or automatic way. This book is an introduction to computability theory (or recursion theory as it is traditionally known to mathematicians). Dr Cutland (...)
  46. Computation vs. information processing: why their difference matters to cognitive science.Gualtiero Piccinini & Andrea Scarantino - 2010 - Studies in History and Philosophy of Science Part A 41 (3):237-246.
    Since the cognitive revolution, it has become commonplace that cognition involves both computation and information processing. Is this one claim or two? Is computation the same as information processing? The two terms are often used interchangeably, but this usage masks important differences. In this paper, we distinguish information processing from computation and examine some of their mutual relations, shedding light on the role each can play in a theory of cognition. We recommend that theorists of cognition be (...)
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  47.  8
    Computational physics: an introduction.Franz Vesely - 2001 - New York: Kluwer Academic/Plenum Publishers.
    Vesely (experimental physics, U. of Vienna, Austria) provides the basic numerical and computational techniques, followed by an explanation of specific problems of computational physics. Appendices address properties of computing machines and an outline of the technique of Fast Fourier Transformation. The first edition, published by Plenum Press, Ne.
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  48.  57
    Computability Theory.S. Barry Cooper - 2003 - Chapman & Hall.
    Computability theory originated with the seminal work of Gödel, Church, Turing, Kleene and Post in the 1930s. This theory includes a wide spectrum of topics, such as the theory of reducibilities and their degree structures, computably enumerable sets and their automorphisms, and subrecursive hierarchy classifications. Recent work in computability theory has focused on Turing definability and promises to have far-reaching mathematical, scientific, and philosophical consequences. Written by a leading researcher, Computability Theory provides a concise, comprehensive, and authoritative introduction to contemporary (...)
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  49. Mechanistic Computational Individuation without Biting the Bullet.Nir Fresco & Marcin Miłkowski - 2019 - British Journal for the Philosophy of Science:axz005.
    Is the mathematical function being computed by a given physical system determined by the system’s dynamics? This question is at the heart of the indeterminacy of computation phenomenon (Fresco et al. [unpublished]). A paradigmatic example is a conventional electrical AND-gate that is often said to compute conjunction, but it can just as well be used to compute disjunction. Despite the pervasiveness of this phenomenon in physical computational systems, it has been discussed in the philosophical literature only indirectly, mostly with (...)
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  50.  8
    Computational Natural Philosophy: A Thread from Presocratics through Turing to ChatGPT.Gordana Dodig Crnkovic - unknown
    Modern computational natural philosophy conceptualizes the universe in terms of information and computation, establishing a framework for the study of cognition and intelligence. Despite some critiques, this computational perspective has significantly influenced our understanding of the natural world, leading to the development of AI systems like ChatGPT based on deep neural networks. Advancements in this domain have been facilitated by interdisciplinary research, integrating knowledge from multiple fields to simulate complex systems. Large Language Models (LLMs), such as ChatGPT, represent this (...)
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