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  1. A Subject with No Object: Strategies for Nominalistic Interpretation of Mathematics.John P. Burgess & Gideon Rosen - 1997 - Oxford, England: Oxford University Press.
    Numbers and other mathematical objects are exceptional in having no locations in space or time or relations of cause and effect. This makes it difficult to account for the possibility of the knowledge of such objects, leading many philosophers to embrace nominalism, the doctrine that there are no such objects, and to embark on ambitious projects for interpreting mathematics so as to preserve the subject while eliminating its objects. This book cuts through a host of technicalities that have obscured previous (...)
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  • A Subject with No Object.Zoltan Gendler Szabo, John P. Burgess & Gideon Rosen - 1999 - Philosophical Review 108 (1):106.
    This is the first systematic survey of modern nominalistic reconstructions of mathematics, and for this reason alone it should be read by everyone interested in the philosophy of mathematics and, more generally, in questions concerning abstract entities. In the bulk of the book, the authors sketch a common formal framework for nominalistic reconstructions, outline three major strategies such reconstructions can follow, and locate proposals in the literature with respect to these strategies. The discussion is presented with admirable precision and clarity, (...)
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  • Triviality Arguments Reconsidered.Paul Schweizer - 2019 - Minds and Machines 29 (2):287-308.
    Opponents of the computational theory of mind have held that the theory is devoid of explanatory content, since whatever computational procedures are said to account for our cognitive attributes will also be realized by a host of other ‘deviant’ physical systems, such as buckets of water and possibly even stones. Such ‘triviality’ claims rely on a simple mapping account of physical implementation. Hence defenders of CTM traditionally attempt to block the trivialization critique by advocating additional constraints on the implementation relation. (...)
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  • Computers in Abstraction/Representation Theory.Samuel Fletcher - 2018 - Minds and Machines 28 (3):445-463.
    Recently, Horsman et al. have proposed a new framework, Abstraction/Representation theory, for understanding and evaluating claims about unconventional or non-standard computation. Among its attractive features, the theory in particular implies a novel account of what is means to be a computer. After expounding on this account, I compare it with other accounts of concrete computation, finding that it does not quite fit in the standard categorization: while it is most similar to some semantic accounts, it is not itself a semantic (...)
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  • II—Mechanistic Explanation: Its Scope and Limits.James Woodward - 2013 - Aristotelian Society Supplementary Volume 87 (1):39-65.
    This paper explores the question of whether all or most explanations in biology are, or ideally should be, ‘mechanistic’. I begin by providing an account of mechanistic explanation, making use of the interventionist ideas about causation I have developed elsewhere. This account emphasizes the way in which mechanistic explanations, at least in the biological sciences, integrate difference‐making and spatio‐temporal information, and exhibit what I call fine‐tunedness of organization. I also emphasize the role played by modularity conditions in mechanistic explanation. I (...)
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  • The New Mechanical Philosophy.Stuart Glennan - 2017 - Oxford University Press.
    This volume argues for a new image of science that understands both natural and social phenomena to be the product of mechanisms, casting the work of science as an effort to understand those mechanisms. Glennan offers an account of the nature of mechanisms and of the models used to represent them in physical, life, and social sciences.
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  • Triviality Arguments Against Functionalism.Peter Godfrey-Smith - 2009 - Philosophical Studies 145 (2):273 - 295.
    “Triviality arguments” against functionalism in the philosophy of mind hold that the claim that some complex physical system exhibits a given functional organization is either trivial or has much less content than is usually supposed. I survey several earlier arguments of this kind, and present a new one that overcomes some limitations in the earlier arguments. Resisting triviality arguments is possible, but requires functionalists to revise popular views about the “autonomy” of functional description.
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  • 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 computation against (...)
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  • Missing Systems and the Face Value Practice.Martin Thomson-Jones - 2010 - Synthese 172 (2):283-299.
    Call a bit of scientific discourse a description of a missing system when (i) it has the surface appearance of an accurate description of an actual, concrete system (or kind of system) from the domain of inquiry, but (ii) there are no actual, concrete systems in the world around us fitting the description it contains, and (iii) that fact is recognised from the outset by competent practitioners of the scientific discipline in question. Scientific textbooks, classroom lectures, and journal articles abound (...)
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  • Computational Versus Causal Complexity.Matthias Scheutz - 2001 - Minds and Machines 11 (4):543-566.
    The main claim of this paper is that notions of implementation based on an isomorphic correspondence between physical and computational states are not tenable. Rather, ``implementation'' has to be based on the notion of ``bisimulation'' in order to be able to block unwanted implementation results and incorporate intuitions from computational practice. A formal definition of implementation is suggested, which satisfies theoretical and practical requirements and may also be used to make the functionalist notion of ``physical realization'' precise. The upshot of (...)
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  • Impossible Worlds.Franz Berto & Mark Jago - 2013 - Oxford: Oxford University Press.
    We need to understand the impossible. Francesco Berto and Mark Jago start by considering what the concepts of meaning, information, knowledge, belief, fiction, conditionality, and counterfactual supposition have in common. They are all concepts which divide the world up more finely than logic does. Logically equivalent sentences may carry different meanings and information and may differ in how they're believed. Fictions can be inconsistent yet meaningful. We can suppose impossible things without collapsing into total incoherence. Yet for the leading philosophical (...)
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  • The Mind as Neural Software?: Understanding Functionalism, Computationalism, and Computational Functionalism.Gualtiero Piccinini - 2010 - Philosophy and Phenomenological Research 81 (2):269-311.
    Defending or attacking either functionalism or computationalism requires clarity on what they amount to and what evidence counts for or against them. My goalhere is not to evaluatc their plausibility. My goal is to formulate them and their relationship clearly enough that we can determine which type of evidence is relevant to them. I aim to dispel some sources of confusion that surround functionalism and computationalism. recruit recent philosophical work on mechanisms and computation to shed light on them, and clarify (...)
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  • The Philosophy of Philosophy.Timothy Williamson - 2007 - Wiley-Blackwell.
    The second volume in the _Blackwell Brown Lectures in Philosophy_, this volume offers an original and provocative take on the nature and methodology of philosophy. Based on public lectures at Brown University, given by the pre-eminent philosopher, Timothy Williamson Rejects the ideology of the 'linguistic turn', the most distinctive trend of 20th century philosophy Explains the method of philosophy as a development from non-philosophical ways of thinking Suggests new ways of understanding what contemporary and past philosophers are doing.
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  • Physical Computation: A Mechanistic Account.Gualtiero Piccinini - 2015 - 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 vs. analog, serial vs. (...)
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  • Explanatory Unification and the Causal Structure of the World.Philip Kitcher - 1989 - In Philip Kitcher & Wesley Salmon (eds.), Scientific Explanation. Minneapolis: University of Minnesota Press. pp. 410-505.
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  • Origins of Objectivity.Tyler Burge - 2010 - Oxford University Press.
    Tyler Burge presents an original study of the most primitive ways in which individuals represent the physical world. By reflecting on the science of perception and related psychological and biological sciences, he gives an account of constitutive conditions for perceiving the physical world, and thus aims to locate origins of representational mind.
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  • Computation Without Representation.Gualtiero Piccinini - 2008 - Philosophical Studies 137 (2):205-241.
    The received view is that computational states are individuated at least in part by their semantic properties. I offer an alternative, according to which computational states are individuated by their functional properties. Functional properties are specified by a mechanistic explanation without appealing to any semantic properties. The primary purpose of this paper is to formulate the alternative view of computational individuation, point out that it supports a robust notion of computational explanation, and defend it on the grounds of how computational (...)
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  • Measurement and Computational Skepticism.Robert J. Matthews & Eli Dresner - 2017 - Noûs 51 (4):832-854.
    Putnam and Searle famously argue against computational theories of mind on the skeptical ground that there is no fact of the matter as to what mathematical function a physical system is computing: both conclude that virtually any physical object computes every computable function, implements every program or automaton. There has been considerable discussion of Putnam's and Searle's arguments, though as yet there is little consensus as to what, if anything, is wrong with these arguments. In the present paper we show (...)
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  • In Defense of the Semantic View of Computation.Oron Shagrir - 2020 - Synthese 197 (9):4083-4108.
    The semantic view of computation is the claim that semantic properties play an essential role in the individuation of physical computing systems such as laptops and brains. The main argument for the semantic view rests on the fact that some physical systems simultaneously implement different automata at the same time, in the same space, and even in the very same physical properties. Recently, several authors have challenged this argument. They accept the premise of simultaneous implementation but reject the semantic conclusion. (...)
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  • 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 implementation relation is (...)
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  • Recipes, Algorithms, and Programs.Carol E. Cleland - 2001 - Minds and Machines 11 (2):219-237.
    In the technical literature of computer science, the concept of an effective procedure is closely associated with the notion of an instruction that precisely specifies an action. Turing machine instructions are held up as providing paragons of instructions that "precisely describe" or "well define" the actions they prescribe. Numerical algorithms and computer programs are judged effective just insofar as they are thought to be translatable into Turing machine programs. Nontechnical procedures (e.g., recipes, methods) are summarily dismissed as ineffective on the (...)
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  • Real Realization: Dennett’s Real Patterns Versus Putnam’s Ubiquitous Automata. [REVIEW]David Joslin - 2006 - Minds and Machines 16 (1):29-41.
    Both Putnam and Searle have argued that that every abstract automaton is realized by every physical system, a claim that leads to a reductio argument against Cognitivism or Strong AI: if it is possible for a computer to be conscious by virtue of realizing some abstract automaton, then by Putnam’s theorem every physical system also realizes that automaton, and so every physical system is conscious—a conclusion few supporters of Strong AI would be willing to accept. Dennett has suggested a criterion (...)
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  • A Theory of Computational Implementation.Michael Rescorla - 2014 - Synthese 191 (6):1277-1307.
    I articulate and defend a new theory of what it is for a physical system to implement an abstract computational model. According to my descriptivist theory, a physical system implements a computational model just in case the model accurately describes the system. Specifically, the system must reliably transit between computational states in accord with mechanical instructions encoded by the model. I contrast my theory with an influential approach to computational implementation espoused by Chalmers, Putnam, and others. I deploy my theory (...)
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  • Does a Rock Implement Every Finite-State Automaton?David J. Chalmers - 1996 - Synthese 108 (3):309-33.
    Hilary Putnam has argued that computational functionalism cannot serve as a foundation for the study of the mind, as every ordinary open physical system implements every finite-state automaton. I argue that Putnam's argument fails, but that it points out the need for a better understanding of the bridge between the theory of computation and the theory of physical systems: the relation of implementation. It also raises questions about the class of automata that can serve as a basis for understanding the (...)
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  • Why Everything Doesn't Realize Every Computation.Ronald L. Chrisley - 1994 - Minds and Machines 4 (4):403-20.
    Some have suggested that there is no fact to the matter as to whether or not a particular physical system relaizes a particular computational description. This suggestion has been taken to 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 (...)
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  • The Rediscovery of the Mind.John Searle - 1992 - MIT Press.
    The title of The Rediscovery of the Mind suggests the question "When was the mind lost?" Since most people may not be aware that it ever was lost, we must also then ask "Who lost it?" It was lost, of course, only by philosophers, by certain philosophers. This passed unnoticed by society at large. The "rediscovery" is also likely to pass unnoticed. But has the mind been rediscovered by the same philosophers who "lost" it? Probably not. John Searle is an (...)
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  • Counterfactuals.David Lewis - 1975 - Foundations of Language 13 (1):145-151.
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  • Acceptable Notation.Stewart Shapiro - 1982 - Notre Dame Journal of Formal Logic 23 (1):14-20.
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  • Explanatory Unification.Philip Kitcher - 1981 - Philosophy of Science 48 (4):507-531.
    The official model of explanation proposed by the logical empiricists, the covering law model, is subject to familiar objections. The goal of the present paper is to explore an unofficial view of explanation which logical empiricists have sometimes suggested, the view of explanation as unification. I try to show that this view can be developed so as to provide insight into major episodes in the history of science, and that it can overcome some of the most serious difficulties besetting the (...)
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  • 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 questions. Justifying the role of computation (...)
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  • The Rediscovery of the Mind.John R. Searle - 1992 - Inquiry: An Interdisciplinary Journal of Philosophy 37:253.
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  • Against Structuralist Theories of Computational Implementation.Michael Rescorla - 2013 - British Journal for the Philosophy of Science 64 (4):681-707.
    Under what conditions does a physical system implement or realize a computation? Structuralism about computational implementation, espoused by Chalmers and others, holds that a physical system realizes a computation just in case the system instantiates a pattern of causal organization isomorphic to the computation’s formal structure. I argue against structuralism through counter-examples drawn from computer science. On my opposing view, computational implementation sometimes requires instantiating semantic properties that outstrip any relevant pattern of causal organization. In developing my argument, I defend (...)
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  • A Simplicity Criterion for Physical Computation.Tyler Millhouse - 2019 - British Journal for the Philosophy of Science 70 (1):153-178.
    The aim of this paper is to offer a formal criterion for physical computation that allows us to objectively distinguish between competing computational interpretations of a physical system. The criterion construes a computational interpretation as an ordered pair of functions mapping (1) states of a physical system to states of an abstract machine, and (2) inputs to this machine to interventions in this physical system. This interpretation must ensure that counterfactuals true of the abstract machine have appropriate counterparts which are (...)
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  • Individuation Without Representation.Joe Dewhurst - 2018 - British Journal for the Philosophy of Science 69 (1):103-116.
    ABSTRACT Shagrir and Sprevak explore the apparent necessity of representation for the individuation of digits in computational systems.1 1 I will first offer a response to Sprevak’s argument that does not mention Shagrir’s original formulation, which was more complex. I then extend my initial response to cover Shagrir’s argument, thus demonstrating that it is possible to individuate digits in non-representational computing mechanisms. I also consider the implications that the non-representational individuation of digits would have for the broader theory of computing (...)
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  • On Effective Procedures.Carol E. Cleland - 2002 - Minds and Machines 12 (2):159-179.
    Since the mid-twentieth century, the concept of the Turing machine has dominated thought about effective procedures. This paper presents an alternative to Turing's analysis; it unifies, refines, and extends my earlier work on this topic. I show that Turing machines cannot live up to their billing as paragons of effective procedure; at best, they may be said to provide us with mere procedure schemas. I argue that the concept of an effective procedure crucially depends upon distinguishing procedures as definite courses (...)
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  • The Rediscovery of the Mind.John R. Searle - 1992 - Philosophy 68 (265):415-418.
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  • Do Accelerating Turing Machines Compute the Uncomputable?B. Jack Copeland & Oron Shagrir - 2011 - Minds and Machines 21 (2):221-239.
    Accelerating Turing machines have attracted much attention in the last decade or so. They have been described as “the work-horse of hypercomputation”. But do they really compute beyond the “Turing limit”—e.g., compute the halting function? We argue that the answer depends on what you mean by an accelerating Turing machine, on what you mean by computation, and even on what you mean by a Turing machine. We show first that in the current literature the term “accelerating Turing machine” is used (...)
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  • Implementation is Semantic Interpretation.Willam J. Rapaport - 1999 - The Monist 82 (1):109-130.
    What is the computational notion of “implementation”? It is not individuation, instantiation, reduction, or supervenience. It is, I suggest, semantic interpretation.
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  • Computational Models: A Modest Role for Content.Frances Egan - 2010 - Studies in History and Philosophy of Science Part A 41 (3):253-259.
    The computational theory of mind construes the mind as an information-processor and cognitive capacities as essentially representational capacities. Proponents of the view claim a central role for representational content in computational models of these capacities. In this paper I argue that the standard view of the role of representational content in computational models is mistaken; I argue that representational content is to be understood as a gloss on the computational characterization of a cognitive process.Keywords: Computation; Representational content; Cognitive capacities; Explanation.
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  • Computation, Individuation, and the Received View on Representation.Mark Sprevak - 2010 - Studies in History and Philosophy of Science Part A 41 (3):260-270.
    The ‘received view’ about computation is that all computations must involve representational content. Egan and Piccinini argue against the received view. In this paper, I focus on Egan’s arguments, claiming that they fall short of establishing that computations do not involve representational content. I provide positive arguments explaining why computation has to involve representational content, and how that representational content may be of any type. I also argue that there is no need for computational psychology to be individualistic. Finally, I (...)
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  • The Representational Foundations of Computation.Michael Rescorla - 2015 - Philosophia Mathematica 23 (3):338-366.
    Turing computation over a non-linguistic domain presupposes a notation for the domain. Accordingly, computability theory studies notations for various non-linguistic domains. It illuminates how different ways of representing a domain support different finite mechanical procedures over that domain. Formal definitions and theorems yield a principled classification of notations based upon their computational properties. To understand computability theory, we must recognize that representation is a key target of mathematical inquiry. We must also recognize that computability theory is an intensional enterprise: it (...)
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  • Why Everything Doesn't Realize Every Computation.Ronald L. Chrisley - 1994 - Minds and Machines 4 (4):403-420.
    Some have suggested that there is no fact to the matter as to whether or not a particular physical system relaizes a particular computational description. This suggestion has been taken to 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 (...)
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  • Explaining Science.Ronald Giere - 1991 - Noûs 25 (3):386-388.
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  • Representation and Reality.H. Putnam - 1990 - Tijdschrift Voor Filosofie 52 (1):168-168.
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  • On Founding the Theory of Algorithms.Yiannis N. Moschovakis - 1998 - In H. G. Dales & Gianluigi Oliveri (eds.), Truth in Mathematics. Oxford University Press, Usa. pp. 71--104.
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  • [Omnibus Review].Tyler Burge - 1981 - Journal of Symbolic Logic 46 (2):412-415.
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  • K-Graph Machines: Generalizing Turing's Machines and Arguments.Wilfried Sieg & John Byrnes - unknown
    Wilfred Sieg and John Byrnes. K-Graph Machines: Generalizing Turing's Machines and Arguments.
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