Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-22T11:43:56.527Z Has data issue: false hasContentIssue false
  • English
  • Français

Computing Mechanisms

Published online by Cambridge University Press:  01 January 2022

Gualtiero Piccinini
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper offers an account of what it is for a physical system to be a computing mechanism—a system that performs computations. A computing mechanism is a mechanism whose function is to generate output strings from input strings and (possibly) internal states, in accordance with a general rule that applies to all relevant strings and depends on the input strings and (possibly) internal states for its application. This account is motivated by reasons endogenous to the philosophy of computing, namely, doing justice to the practices of computer scientists and computability theorists. It is also an application of recent literature on mechanisms, because it assimilates computational explanation to mechanistic explanation. The account can be used to individuate computing mechanisms and the functions they compute and to taxonomize computing mechanisms based on their computing power.

Type
Research Article
Information
Philosophy of Science , Volume 74 , Issue 4 , October 2007 , pp. 501 - 526
Copyright
Copyright © The Philosophy of Science Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

‡

Thanks to the many people who discussed computing mechanisms with me. For comments on previous drafts, I'm especially indebted to Carl Craver,, John Gabriel, Peter Machamer, Corey Maley, the editor, and members of the St. Louis Philosophy of Science Reading Group. Between 2002 and 2005, ancestors of this paper were presented to the Canadian Society for the History and Philosophy of Science, Computing and Philosophy (CAP@CMU), the Second Reichenbach Conference, the SSPP, the University of Pittsburgh, the University of Georgia, and Georgia State University. Thanks to the audiences for their feedback. The writing of this paper was supported in part by a grant from the University of Missouri—St. Louis.

References

Allen, C., Bekoff, M., and Lauder, G. (eds.) (1998), Nature’s Purposes: Analysis of Function and Design in Biology. Cambridge, MA: MIT Press.Google Scholar
Ariew, A., Cummins, R., and Perlman, M. (eds.) (2002), Functions: New Essays in the Philosophy of Psychology and Biology. Oxford: Oxford University Press.Google Scholar
Bechtel, W., and Richardson, R. C. (1993), Discovering Complexity: Decomposition and Localization as Scientific Research Strategies. Princeton, NJ: Princeton University Press.Google Scholar
Boorse, C. (2002), “A Rebuttal on Functions”, in Ariew, A., Cummins, R., and Perlman, M. (eds.), Functions: New Essays in the Philosophy of Psychology and Biology. Oxford: Oxford University Press, 63–112.Google Scholar
Buller, D. J. (ed.) (1999), Function, Selection, and Design. Albany: State University of New York Press.Google Scholar
Chalmers, D. J. (1996), “Does a Rock Implement Every Finite-State Automaton?”, Does a Rock Implement Every Finite-State Automaton? 108:310–333.Google Scholar
Chrisley, R. L. (1995), “Why Everything Doesn’t Realize Every Computation”, Why Everything Doesn’t Realize Every Computation 4:403–430.Google Scholar
Christensen, W. D., and Bickhard, M. H. (2002), “The Process Dynamics of Normative Function”, The Process Dynamics of Normative Function 85:3–28.Google Scholar
Church, A. (1940), “On the Concept of a Random Sequence”, On the Concept of a Random Sequence 46:130–135.Google Scholar
Collins, J., Hall, N., and Paul, L. A. (eds.) (2004), Causation and Counterfactuals. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Copeland, B. J. (1996), “What Is Computation?”, What Is Computation? 108:224–359.Google Scholar
Copeland, B. J. (2000), “Narrow versus Wide Mechanism: Including a Re-examination of Turing’s Views on the Mind-Machine Issue”, Narrow versus Wide Mechanism: Including a Re-examination of Turing’s Views on the Mind-Machine Issue 96:5–32.Google Scholar
Copeland, B. J. (2002), “Hypercomputation”, Hypercomputation 12:461–502.Google Scholar
Corcoran, J., Frank, W., and Maloney, M. (1974), “String Theory”, String Theory 39:625–637.Google Scholar
Cotogno, P. (2003), “Hypercomputation and the Physical Church-Turing Thesis”, Hypercomputation and the Physical Church-Turing Thesis 54:181–223.Google Scholar
Craver, C. (2001), “Role Functions, Mechanisms, and Hierarchy”, Role Functions, Mechanisms, and Hierarchy 68:53–74.Google Scholar
Craver, C. (forthcoming), Explaining the Brain. Oxford: Oxford University Press.CrossRefGoogle Scholar
Cummins, R. (1977), “Programs in the Explanation of Behavior”, Programs in the Explanation of Behavior 44:269–287.Google Scholar
Cummins, R. (1983), The Nature of Psychological Explanation. Cambridge, MA: MIT Press.Google Scholar
Davis, M., Sigal, R., and Weyuker, E. J. (1994), Computability, Complexity, and Languages. Boston: Academic Press.Google Scholar
Dretske, F. (1986), “Misrepresentation”, in Bogdan, R. (ed.), Belief: Form, Content and Function. New York: Oxford University Press, 17–36.Google Scholar
Dreyfus, H. L. (1979), What Computers Can’t Do. New York: Harper & Row.Google Scholar
Fodor, J. A. (1968), “The Appeal to Tacit Knowledge in Psychological Explanation”, The Appeal to Tacit Knowledge in Psychological Explanation 65:627–640.Google Scholar
Fodor, J. A. (1975), The Language of Thought. Cambridge, MA: Harvard University PressGoogle Scholar
Fodor, J. A. (1998), Concepts. Oxford: Clarendon Press.CrossRefGoogle Scholar
Glennan, S. (2002), “Rethinking Mechanistic Explanation”, Rethinking Mechanistic Explanation 64:S342–S353.Google Scholar
Goldstine, H., and Neumann, J. von (1946), “On the Principles of Large Scale Computing Machines”, Princeton, NJ: Institute for Advanced Studies.Google Scholar
Hopfield, J. (1982), “Neural Networks and Physical Systems with Emergent Collective Computational Abilities”, Neural Networks and Physical Systems with Emergent Collective Computational Abilities 79:2554–2558.Google ScholarPubMed
Lewis, D. (1986), “Postscript to ‘Causation’”, in Philosophical Papers, Vol. 2. New York: Oxford University Press, 172–213.Google Scholar
Machamer, P. K., Darden, L., and Craver, C. (2000), “Thinking about Mechanisms”, Thinking about Mechanisms 67:1–25.Google Scholar
Minsky, M. L., and Papert, S. A. (1988), Perceptrons: An Introduction to Computational Geometry. Cambridge, MA: MIT Press.Google Scholar
Patterson, D. A., and Hennessy, J. L. (1998), Computer Organization and Design: The Hardware/Software Interface. San Francisco: Morgan Kauffman.Google Scholar
Piccinini, G. (2003), “Alan Turing and the Mathematical Objection”, Alan Turing and the Mathematical Objection 13:23–48.Google Scholar
Piccinini, G. (2004a), “Functionalism, Computationalism, and Mental Contents”, Functionalism, Computationalism, and Mental Contents 34:375–410.Google Scholar
Piccinini, G. (2004b), “Functionalism, Computationalism, and Mental States”, Functionalism, Computationalism, and Mental States 35:811–833.Google Scholar
Piccinini, G. (2007a), “Computation without Representation”, forthcoming in Philosophical Studies.Google Scholar
Piccinini, G. (2007b), “Computational Modeling vs. Computational Explanation: Is Everything a Turing Machine, and Does It Matter to the Philosophy of Mind?”, Computational Modeling vs. Computational Explanation: Is Everything a Turing Machine, and Does It Matter to the Philosophy of Mind? 85:93–115.Google Scholar
Piccinini, G. (2007c), “Connectionist Computation”, forthcoming in Proceedings of the 2007 International Joint Conference on Neural Networks.CrossRefGoogle Scholar
Piccinini, G. (nd), “Computers”. St. Louis: University of Missouri.Google Scholar
Pour-El, M. B. (1974), “Abstract Computability and Its Relation to the General Purpose Analog Computer (Some Connections between Logic, Differential Equations and Analog Computers)”, Abstract Computability and Its Relation to the General Purpose Analog Computer (Some Connections between Logic, Differential Equations and Analog Computers) 199:1–28.Google Scholar
Preston, B. (1998), “Why Is a Wing like a Spoon? A Pluralist Theory of Function”, Why Is a Wing like a Spoon? A Pluralist Theory of Function 95:215–254.Google Scholar
Putnam, H. (1960), “Minds and Machines”, in Hook, S. (ed.), Dimensions of Mind: A Symposium. New York: Collier, 138–164.Google Scholar
Putnam, H. (1967), “Psychological Predicates”, in Art, Philosophy, and Religion. Pittsburgh: University of Pittsburgh Press.Google Scholar
Putnam, H. (1988), Representation and Reality. Cambridge, MA: MIT Press.Google Scholar
Pylyshyn, Z. W. (1984), Computation and Cognition. Cambridge, MA: MIT Press.Google Scholar
Rumelhart, D. E., and McClelland, J. M. (1986), Parallel Distributed Processing. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Scheutz, M. (1999), “When Physical Systems Realize Functions …”, When Physical Systems Realize Functions … 9:161–196.Google Scholar
Schlosser, G. (1998), “Self-Re-production and Functionality: A Systems-Theoretical Approach to Teleological Explanation”, Self-Re-production and Functionality: A Systems-Theoretical Approach to Teleological Explanation 116:303–354.Google Scholar
Searle, J. R. (1980), “Minds, Brains, and Programs”, Minds, Brains, and Programs 3:417–457.Google Scholar
Searle, J. R. (1992), The Rediscovery of the Mind. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Shagrir, O. (2006), “Why We View the Brain as a Computer”, Why We View the Brain as a Computer 153:393–416.Google Scholar
Sieg, W., and Byrnes, J. (1996), “K-Graph Machines: Generalizing Turing’s Machines and Arguments”, in Hájek, P. (ed.), Gödel '96. Berlin: Springer-Verlag, 98–119.Google Scholar
Siegelmann, H. T. (1999), Neural Networks and Analog Computation: Beyond the Turing Limit. Boston: Birkhäuser.CrossRefGoogle Scholar
Tabery, J. (2004), “Synthesizing Activities and Interactions in the Concept of a Mechanism”, Synthesizing Activities and Interactions in the Concept of a Mechanism 71:1–15.Google Scholar
Turing, A. M. (1936–37 [1965]), “On Computable Numbers, with an Application to the Entscheidungsproblem”, in Davis, M. (ed.), The Undecidable. Ewlett, NY: Raven Press, 116–154.Google Scholar
Turing, A. M. (1939), “Systems of Logic Based on Ordinals”, Proceedings of the London Mathematical Society, series 2, 45:161–228.CrossRefGoogle Scholar
Wimsatt, W. C. (2002), “Functional Organization, Analogy, and Inference”, in Ariew, A., Cummins, R., and Perlman, M. (eds.), Functions: New Essays in the Philosophy of Psychology and Biology. Oxford: Oxford University Press, 173–221.Google Scholar