Computing Mechanisms | Philosophy of Science | Cambridge Core (original) (raw)

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.

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

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

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

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