About this topic
Summary A crucial problem in the philosophy of computing is represented by the nature of computation. On the one hand, a computation is thought of as some representation of a formal process composed by well-defined steps, which allows to reach in a finite amount of time a given output from a given input. This is tantamount to the formulation of a mathematical or biological function or the design of an algorithm. On the other hand, a computation is inherently bound to its execution and thus to an implementation. This strongly relates to the problem of determining which physical systems can be said to implement a computation, in turn which systems can be said to be properly computational. The answer to this question can be offered by reduction to other relations (such as causation), but it triggered a widespread debate on whether it implies that almost any physical system is then by definition computational. This has been a particularly intense debate in the cognitive sciences. The duality formal-physical that affects the nature of computation is also of especially great importance in the philosophical debate on the nature of algorithms and programs, where the latter are considered physical implementations of the former.
Key works The thesis that certain human abilities cannot be considered implementation of computations is notoriously held by Dreyfus 1972 and Putnam 1987. This argument is even stronger in Searle 1980, where it is argued that even the interpretation of human abilites as implementation of computations is not enough for the mind. The thesis that a physical system implements a computation if the causal structure of the former reflects the formal structure of the latter is defended in Chalmers 1994. See also Piccinini 2007. A starting point for the  debate on the nature of algorithms is represented by Moschovakis 2001 and Gurevich 2012. Fetzer 1988 offers the very first critique of program verification in view of the formal-physical divide, with a large debate following.
Introductions See Piccinini 2010 for an overview of the notion of computation in physical systems, including an assessment of varieties of the physical Church-Turing thesis. 
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  1. Many-Valued Logics. A Mathematical and Computational Introduction.Luis M. Augusto - 2017 - London: College Publications.
    Many-valued logics are those logics that have more than the two classical truth values, to wit, true and false; in fact, they can have from three to infinitely many truth values. This property, together with truth-functionality, provides a powerful formalism to reason in settings where classical logic—as well as other non-classical logics—is of no avail. Indeed, originally motivated by philosophical concerns, these logics soon proved relevant for a plethora of applications ranging from switching theory to cognitive modeling, and they are (...)
  2. Dancing with Pixies: Strong Artificial Intelligence and Panpsychism.J. M. Bishop - 2002 - In John Preston & John Mark Bishop (eds.), Views into the Chinese Room: New Essays on Searle and Artificial Intelligence. pp. 360-379.
    The argument presented in this paper is not a direct attack or defence of the Chinese Room Argument (CRA), but relates to the premise at its heart, that syntax is not sufficient for semantics, via the closely associated propositions that semantics is not intrinsic to syntax and that syntax is not intrinsic to physics. However, in contrast to the CRA’s critique of the link between syntax and semantics, this paper will explore the associated link between syntax and physics. The main (...)
  3. A Cognitive Computation Fallacy? Cognition, Computations and Panpsychism.John Mark Bishop - 2009 - Cognitive Computation 1 (3):221-233.
    The journal of Cognitive Computation is defined in part by the notion that biologically inspired computational accounts are at the heart of cognitive processes in both natural and artificial systems. Many studies of various important aspects of cognition (memory, observational learning, decision making, reward prediction learning, attention control, etc.) have been made by modelling the various experimental results using ever-more sophisticated computer programs. In this manner progressive inroads have been made into gaining a better understanding of the many components of (...)
  4. Why Computers Can't Feel Pain.John Mark Bishop - 2009 - Minds and Machines 19 (4):507-516.
    The most cursory examination of the history of artificial intelligence highlights numerous egregious claims of its researchers, especially in relation to a populist form of ‘strong’ computationalism which holds that any suitably programmed computer instantiates genuine conscious mental states purely in virtue of carrying out a specific series of computations. The argument presented herein is a simple development of that originally presented in Putnam’s (Representation & Reality, Bradford Books, Cambridge in 1988 ) monograph, “Representation & Reality”, which if correct, has (...)
  5. Counterfactuals Cannot Count: A Rejoinder to David Chalmers.John Mark Bishop - 2002 - Consciousness and Cognition 11 (4):642-652.
    The initial argument presented herein is not significantly original—it is a simple reflection upon a notion of computation originally developed by Putnam and criticised by Chalmers et al. . In what follows, instead of seeking to justify Putnam’s conclusion that every open system implements every Finite State Automaton and hence that psychological states of the brain cannot be functional states of a computer, I will establish the weaker result that, over a finite time window every open system implements the trace (...)
  6. Inter-Level Relations in Computer Science, Biology, and Psychology.Fred Boogerd, Frank Bruggeman, Catholijn Jonker, Huib Looren de Jong, Allard Tamminga, Jan Treur, Hans Westerhoff & Wouter Wijngaards - 2002 - Philosophical Psychology 15 (4):463–471.
    Investigations into inter-level relations in computer science, biology and psychology call for an *empirical* turn in the philosophy of mind. Rather than concentrate on *a priori* discussions of inter-level relations between 'completed' sciences, a case is made for the actual study of the way inter-level relations grow out of the developing sciences. Thus, philosophical inquiries will be made more relevant to the sciences, and, more importantly, philosophical accounts of inter-level relations will be testable by confronting them with what really happens (...)
  7. Artificial Intelligence: The Case Against.Rainer P. Born (ed.) - 1987 - St Martin's Press.
  8. Quantity of Experience: Brain-Duplication and Degrees of Consciousness. [REVIEW]Nick Bostrom - 2006 - Minds and Machines 16 (2):185-200.
    If a brain is duplicated so that there are two brains in identical states, are there then two numerically distinct phenomenal experiences or only one? There are two, I argue, and given computationalism, this has implications for what it is to implement a computation. I then consider what happens when a computation is implemented in a system that either uses unreliable components or possesses varying degrees of parallelism. I show that in some of these cases there can be, in a (...)
  9. Parallel Machines.Andrew Boucher - 1997 - Minds and Machines 7 (4):543-551.
    Because it is time-dependent, parallel computation is fundamentally different from sequential computation. Parallel programs are non-deterministic and are not effective procedures. Given the brain operates in parallel, this casts doubt on AI's attempt to make sequential computers intelligent.
  10. In Computation, Parallel is Nothing, Physical Everything.Selmer Bringsjord - 2001 - Minds and Machines 11 (1):95-99.
    Andrew Boucher (1997) argues that ``parallel computation is fundamentally different from sequential computation'' (p. 543), and that this fact provides reason to be skeptical about whether AI can produce a genuinely intelligent machine. But parallelism, as I prove herein, is irrelevant. What Boucher has inadvertently glimpsed is one small part of a mathematical tapestry portraying the simple but undeniable fact that physical computation can be fundamentally different from ordinary, ``textbook'' computation (whether parallel or sequential). This tapestry does indeed immediately imply (...)
  11. Computation, Among Other Things, is Beneath Us.Selmer Bringsjord - 1994 - Minds and Machines 4 (4):469-88.
    What''s computation? The received answer is that computation is a computer at work, and a computer at work is that which can be modelled as a Turing machine at work. Unfortunately, as John Searle has recently argued, and as others have agreed, the received answer appears to imply that AI and Cog Sci are a royal waste of time. The argument here is alarmingly simple: AI and Cog Sci (of the Strong sort, anyway) are committed to the view that cognition (...)
  12. Implementation and Indeterminacy.Curtis Brown - 2004 - Conferences in Research and Practice in Information Technology 37.
    David Chalmers has defended an account of what it is for a physical system to implement a computation. The account appeals to the idea of a “combinatorial-state automaton” or CSA. It is unclear whether Chalmers intends the CSA to be a computational model in the usual sense, or merely a convenient formalism into which instances of other models can be translated. I argue that the CSA is not a computational model in the usual sense because CSAs do not perspicuously represent (...)
  13. Confirmation and the Computational Paradigm (Or: Why Do You Think They Call Itartificial Intelligence?). [REVIEW]David J. Buller - 1993 - Minds and Machines 3 (2):155-181.
    The idea that human cognitive capacities are explainable by computational models is often conjoined with the idea that, while the states postulated by such models are in fact realized by brain states, there are no type-type correlations between the states postulated by computational models and brain states (a corollary of token physicalism). I argue that these ideas are not jointly tenable. I discuss the kinds of empirical evidence available to cognitive scientists for (dis)confirming computational models of cognition and argue that (...)
  14. Building Computational Institutions for Agents with Rolex.Giacomo Cabri, Luca Ferrari & Rossella Rubino - 2008 - Artificial Intelligence and Law 16 (1):129-145.
    While the sociality of software agents drives toward the definition of institutions for multi agent systems, their autonomy requires that such institutions are ruled by appropriate norm mechanisms. Computational institutions represent useful abstractions. In this paper we show how computational institutions can be built on top of the RoleX infrastructure, a role-based system with interesting features for our aim. We achieve a twofold goal: on the one hand, we give concreteness to the institution abstractions; on the other hand, we demonstrate (...)
  15. The Transfer of Functions From Man To Machine.R. Caussin & W. F. Chamberlin - 1959 - Diogenes 7 (28):107-125.
  16. Proving Darwin: Making Biology Mathematical.G. J. Chaitin - 2012 - Pantheon.
    Groundbreaking mathematician Gregory Chaitin gives us the first book to posit that we can prove how Darwin’s theory of evolution works on a mathematical level.
  17. The Varieties of Computation: A Reply.David Chalmers - 2012 - Journal of Cognitive Science 2012 (3):211-248.
  18. A Computational Foundation for the Study of Cognition.David J. 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 (...)
  19. 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 (...)
  20. 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 (...)
  21. The Ontological Status of Computational States.Ronald L. Chrisley - 1994 - In Gianfranco Soldati (ed.), European Review of Philosophy, 1: Philosophy of Mind. CSLI Publications. pp. 55-75.
  22. 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 (...)
  23. 'Turing Limit'. Some of Them (Steinhart, Copeland) Represent Extensions of Tur-Ing's Account, Whereas Others Defend Alternatives Notions of Effective Computability (Bringsjord and Zenzen, Wells).Carol E. Cleland - 2002 - Minds and Machines 12:157-158.
  24. 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 (...)
  25. Effective Procedures and Computable Functions.Carol E. Cleland - 1995 - Minds and Machines 5 (1):9-23.
    Horsten and Roelants have raised a number of important questions about my analysis of effective procedures and my evaluation of the Church-Turing thesis. They suggest that, on my account, effective procedures cannot enter the mathematical world because they have a built-in component of causality, and, hence, that my arguments against the Church-Turing thesis miss the mark. Unfortunately, however, their reasoning is based upon a number of misunderstandings. Effective mundane procedures do not, on my view, provide an analysis of ourgeneral concept (...)
  26. Information, Causation and Computation.John Collier - unknown
    Causation can be understood as a computational process once we understand causation in informational terms. I argue that if we see processes as information channels, then causal processes are most readily interpreted as the transfer of information from one state to another. This directly implies that the later state is a computation from the earlier state, given causal laws, which can also be interpreted computationally. This approach unifies the ideas of causation and computation.
  27. Why Build a Virtual Brain? Large-Scale Neural Simulations as Jump Start for Cognitive Computing.Matteo Colombo - 2016 - Journal of Experimental and Theoretical Artificial Intelligence.
    Despite the impressive amount of financial resources recently invested in carrying out large-scale brain simulations, it is controversial what the pay-offs are of pursuing this project. One idea is that from designing, building, and running a large-scale neural simulation, scientists acquire knowledge about the computational performance of the simulating system, rather than about the neurobiological system represented in the simulation. It has been claimed that this knowledge may usher in a new era of neuromorphic, cognitive computing systems. This study elucidates (...)
  28. Deep and Beautiful. The Reward Prediction Error Hypothesis of Dopamine.Matteo Colombo - 2014 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 45 (1):57-67.
    According to the reward-prediction error hypothesis of dopamine, the phasic activity of dopaminergic neurons in the midbrain signals a discrepancy between the predicted and currently experienced reward of a particular event. It can be claimed that this hypothesis is deep, elegant and beautiful, representing one of the largest successes of computational neuroscience. This paper examines this claim, making two contributions to existing literature. First, it draws a comprehensive historical account of the main steps that led to the formulation and subsequent (...)
  29. Accelerating Turing Machines.B. Jack Copeland - 2002 - Minds and Machines 12 (2):281-300.
    Accelerating Turing machines are Turing machines of a sort able to perform tasks that are commonly regarded as impossible for Turing machines. For example, they can determine whether or not the decimal representation of contains n consecutive 7s, for any n; solve the Turing-machine halting problem; and decide the predicate calculus. Are accelerating Turing machines, then, logically impossible devices? I argue that they are not. There are implications concerning the nature of effective procedures and the theoretical limits of computability. Contrary (...)
  30. Hypercomputation.B. Jack Copeland - 2002 - Minds and Machines 12 (4):461-502.
  31. 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 (...)
  32. Do Accelerating Turing Machines Compute the Uncomputable?B. Jack Copeland & Oron Shagrir - 2011 - Minds and Machines 21 (2):221-239.
  33. Physical Computation: How General Are Gandy's Principles for Mechanisms?B. Jack Copeland & Oron Shagrir - 2007 - Minds and Machines 17 (2):217-231.
    What are the limits of physical computation? In his ‘Church’s Thesis and Principles for Mechanisms’, Turing’s student Robin Gandy proved that any machine satisfying four idealised physical ‘principles’ is equivalent to some Turing machine. Gandy’s four principles in effect define a class of computing machines (‘Gandy machines’). Our question is: What is the relationship of this class to the class of all (ideal) physical computing machines? Gandy himself suggests that the relationship is identity. We do not share this view. We (...)
  34. Even Turing Machines Can Compute Uncomputable Functions.Jack Copeland - unknown
    Accelerated Turing machines are Turing machines that perform tasks commonly regarded as impossible, such as computing the halting function. The existence of these notional machines has obvious implications concerning the theoretical limits of computability.
  35. Physical Perspectives on Computation, Computational Perspectives on Physics.Michael E. Cuffaro & Samuel C. Fletcher (eds.) - 2018 - Cambridge University Press.
    Although computation and the science of physical systems would appear to be unrelated, there are a number of ways in which computational and physical concepts can be brought together in ways that illuminate both. This volume examines fundamental questions which connect scholars from both disciplines: is the universe a computer? Can a universal computing machine simulate every physical process? What is the source of the computational power of quantum computers? Are computational approaches to solving physical problems and paradoxes always fruitful? (...)
  36. Individuation Without Representation.Joe Dewhurst - 2016 - British Journal for the Philosophy of Science:axw018.
    Shagrir (2001) and Sprevak (2010) explore the apparent necessity of representation for the individuation of digits (and processors) in computational systems. 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 (...)
  37. A Counterexample T o All Future Dynamic Systems Theories of Cognition.Eric Dietrich - 2000 - J. Of Experimental and Theoretical AI 12 (2):377-382.
    Years ago, when I was an undergraduate math major at the University of Wyoming, I came across an interesting book in our library. It was a book of counterexamples t o propositions in real analysis (the mathematics of the real numbers). Mathematicians work more or less like the rest of us. They consider propositions. If one seems to them to be plausibly true, then they set about to prove it, to establish the proposition as a theorem. Instead o f setting (...)
  38. A Dialogue Concerning Two World Systems: Info-Computational Vs. Mechanistic.Gordana Dodig-Crnkovic & Vincent C. Müller - 2011 - In Gordana Dodig-Crnkovic & Mark Burgin (eds.), Information and computation: Essays on scientific and philosophical understanding of foundations of information and computation. World Scientific. pp. 149-184.
    The dialogue develops arguments for and against a broad new world system - info-computationalist naturalism - that is supposed to overcome the traditional mechanistic view. It would make the older mechanistic view into a special case of the new general info-computationalist framework (rather like Euclidian geometry remains valid inside a broader notion of geometry). We primarily discuss what the info-computational paradigm would mean, especially its pancomputationalist component. This includes the requirements for a the new generalized notion of computing that would (...)
  39. Resolving Arguments by Different Conceptual Traditions of Realization.Ronald P. Endicott - 2012 - Philosophical Studies 159 (1):41-59.
    There is currently a significant amount of interest in understanding and developing theories of realization. Naturally arguments have arisen about the adequacy of some theories over others. Many of these arguments have a point. But some can be resolved by seeing that the theories of realization in question are not genuine competitors because they fall under different conceptual traditions with different but compatible goals. I will first describe three different conceptual traditions of realization that are implicated by the arguments under (...)
  40. Searle, Syntax, and Observer-Relativity.Ronald P. Endicott - 1996 - Canadian Journal of Philosophy 26 (1):101-22.
    I critically examine some provocative arguments that John Searle presents in his book The Rediscovery of Mind to support the claim that the syntactic states of a classical computational system are "observer relative" or "mind dependent" or otherwise less than fully and objectively real. I begin by explaining how this claim differs from Searle's earlier and more well-known claim that the physical states of a machine, including the syntactic states, are insufficient to determine its semantics. In contrast, his more recent (...)
  41. Explaining Experience In Nature: The Foundations Of Logic And Apprehension.Steven Ericsson-Zenith - forthcoming - Institute for Advanced Science & Engineering.
    At its core this book is concerned with logic and computation with respect to the mathematical characterization of sentient biophysical structure and its behavior. -/- Three related theories are presented: The first of these provides an explanation of how sentient individuals come to be in the world. The second describes how these individuals operate. And the third proposes a method for reasoning about the behavior of individuals in groups. -/- These theories are based upon a new explanation of experience in (...)
  42. System, Subsystem, Hive: Boundary Problems in Computational Theories of Consciousness.Tomer Fekete, Cees van Leeuwen & Shimon Edelman - 2016 - Frontiers in Psychology 7.
    A computational theory of consciousness should include a quantitative measure of consciousness, or MoC, that (i) would reveal to what extent a given system is conscious, (ii) would make it possible to compare not only different systems, but also the same system at different times, and (iii) would be graded, because so is consciousness. However, unless its design is properly constrained, such an MoC gives rise to what we call the boundary problem: an MoC that labels a system as conscious (...)
  43. Simulating Physics with Computers.R. P. Feynman - 1982 - International Journal of Theoretical Physics 21 (6):467-488.
  44. On the Epistemological Analysis of Modeling and Computational Error in the Mathematical Sciences.Nicolas Fillion & Robert M. Corless - 2014 - Synthese 191 (7):1451-1467.
    Interest in the computational aspects of modeling has been steadily growing in philosophy of science. This paper aims to advance the discussion by articulating the way in which modeling and computational errors are related and by explaining the significance of error management strategies for the rational reconstruction of scientific practice. To this end, we first characterize the role and nature of modeling error in relation to a recipe for model construction known as Euler’s recipe. We then describe a general model (...)
  45. Realization for Causal Nondeterministic Input-Output Systems.Norman Y. Foo & Pavlos Peppas - 2001 - Studia Logica 67 (3):419-437.
    There are two well-developed formalizations of discrete time dynamic systems that evidently share many concerns but suffer from a lack of mutual awareness. One formalization is classical systems and automata theory. The other is the logic of actions in which the situation and event calculi are the strongest representatives. Researchers in artificial intelligence are likely to be familiar with the latter but not the former. This is unfortunate, for systems and automata theory have much to offer by way of insight (...)
  46. Algorithms, Abstraction and Implementation.C. Foster - 1990 - Academic Press.
  47. Explaining Computation Without Semantics: Keeping It Simple. [REVIEW]Nir Fresco - 2010 - Minds and Machines 20 (2):165-181.
    This paper deals with the question: how is computation best individuated? -/- 1. The semantic view of computation: computation is best individuated by its semantic properties. 2. The causal view of computation: computation is best individuated by its causal properties. 3. The functional view of computation: computation is best individuated by its functional properties. -/- Some scientific theories explain the capacities of brains by appealing to computations that they supposedly perform. The reason for that is usually that computation is individuated (...)
  48. An Analysis of the Criteria for Evaluating Adequate Theories of Computation.Nir Fresco - 2008 - Minds and Machines 18 (3):379-401.
    This paper deals with the question: What are the criteria that an adequate theory of computation has to meet? 1. Smith's answer: it has to meet the empirical criterion (i.e. doing justice to computational practice), the conceptual criterion (i.e. explaining all the underlying concepts) and the cognitive criterion (i.e. providing solid grounds for computationalism). 2. Piccinini's answer: it has to meet the objectivity criterion (i.e. identifying computation as a matter of fact), the explanation criterion (i.e. explaining the computer's behaviour), the (...)
  49. Computational Commitment and Physical Realization.Robert M. Harrish - 1983 - Behavioral and Brain Sciences 6 (3):408.
  50. Syntax, Semantics, Physics.John Haugeland - 2003 - In John M. Preston & Michael A. Bishop (eds.), Views Into the Chinese Room: New Essays on Searle and Artificial Intelligence. Oxford University Press.
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