Philosophy of Computer Science: Online Resources

Further Readings for Chapter 9:

Computers: A Philosophical Perspective

Note 1: Many of these items are online; links are given where they are known. Other items may also be online; an internet search should help you find them.

Note 2: In general, works are listed in chronological order. (This makes it easier to follow the historical development of ideas.)

§9.1: What Is a Computer?

1. General Readings:
   • Shagrir 1999 is a short, but wide-ranging, paper on the nature of computers, hypercomputation, analog computation, and computation as not being purely syntactic (a topic that we look into in Chapter 16).

   • Shagrir, O. (2006). Why we view the brain as a computer. Synthese, 153:393–416, §1, "The Problem of Physical Computation: What Does Distinguish Computers from Other Physical Systems?".
     • Contains a good survey of various theories of what a computer is.

   • Chalmers, D.J. (2011). A computational foundation for the study of cognition. Journal of Cognitive Science (South Korea), 12(4):323–357, §"What about computers?", pp. 335–336.
     • Suggests that a computer is a device that can implement multiple computations by being programmable.
     • The 2011 version of this essay — originally written in 1993 — was accompanied by commentaries, including:
       • Egan, F. (2012). Metaphysics and computational cognitive science: Let's not let the tail wag the dog. Journal of Cognitive Science (South Korea), 13(1):39–49.

       • Rescorla, M. (2012). How to integrate representation into computational modeling, and why we should. Journal of Cognitive Science (South Korea), 13(1):1–38.

       • Scheutz, M. (2012). What it is not to implement a computation: A critical analysis of Chalmers' notion of implemention. Journal of Cognitive Science (South Korea), 13(1):75–106

       • Shagrir, O. (2012). Can a brain possess two minds? Journal of Cognitive Science (South Korea), 13(2):145–165.

       and a reply:

       • Chalmers, D. J. (2012). The varieties of computation: A reply. Journal of Cognitive Science (South Korea), 13(3):211–248.

   • It is also useful to see what introductory CS texts say:
     • A good (early) example is Ralston 1971.

     • A textbook survey of numerous definitions is in:
       • Harnish, R.M. (2002). Coda: Computation for cognitive science, or what IS a computer, anyway? In Minds, Brains, Computers: An Historical Introduction to the Foundations of Cognitive Science, pages 394–412. Blackwell, Malden, MA.

   • See also:
     • the website Anderson, D.L. (2006). The nature of computers. The Mind Project,

     • and the video Chirimuuta, M., Boone, T., and DeMedonsa, M. (2014). Is your brain a computer?, Instant HPS

     

   • An entirely different view of what computers are can be found in:
     • Smith, Brian Cantwell (1999), "Requiem for the Computational Theory of Mind", in his Indiscrete Affairs, Vol. II (2010):37–58.

     There he says:

     "… the property computational or being a computer does not pick out a natural or scientific kind. It is not a property that will figure in scientific laws, or underwrite any deep or interesting scientific generalisations. Nothing of scientific interest holds of a computer in virtue of its being a computer, or of anything at all in virtue of its being computational.

     "In the end, computers turn out to be rather like cars: objects of inestimable historical and economical and social importance, the existence of which has and will continue to transform our lives. …

     "For 'computational' to be a scientific property—for there to be a theory of computation—[…]there must be something special about computers. It is that 'specialness' that a theory should capture. And […] the reason why 'computational' is not going to survive as a scientific property is that there is not anything sufficiently special. In spite of the press, real-world computers turn out not to be necessarily formal, or necessarily digital, or necessarily abstract, or necessarily context-independent…or necessarily any other property that has been suggested (or that I have been able to find). Rather, what computers are are dynamic, intentional systemssocially constructed intentional artifacts, the best, at any moment in history, that we know how to build." (pp.51f)

   

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2. On virtual machines, see:
   • Popek, G.J. and Goldberg, R.P. (1974). Formal requirements for virtualizable third generation architectures. Communications of the ACM, 17(7):412–421.

   • Pylyshyn 1992

   • Pollock, J.L. (2008). What am I? Virtual machines and the mind/body problem. Philosophy and Phenomenological Research, 76(2):237–309.
     • Argues that we are virtual machines!

   • Sloman, A. (2008). Why virtual machines really matter — for several disciplines.
     • Views virtual machines as mathematical abstractions that can have causal relations with the physical world.

   • Chalmers, D.J. (2017). The virtual and the real. Disputatio, 9(46):309–351.

   • Sloman, A. (2019). What is it like to be a rock?.
     "virtual machines … are implemented in, but not equivalent (or reducible) to any underlying physical machine, in part because the terms used to describe the properties and functions of the virtual machine (e.g. the internet-based email system now used all over our planet) are not definable in the language of physics, and the virtual machine that runs for an extended time is not equivalent to or reducible to the collection of physical machinery that happens to implement the email system at any time. For example, parts of the physical network can be temporarily unavailable causing messages to be routed differently, and over time parts of the physical network are replaced using new physical and software technology that was unknown a few years earlier, providing cheaper, faster and more reliable hardware running the same virtual machine."

§9.3.2: Stored Program vs. Programmable:

• Haigh, T. (2013). 'Stored program concept' considered harmful: History and historiography. In Bonizzoni, P., Brattka, V., and Löwe, B., editors, CiE 2013, pages 241–251. Springer-Verlag Lecture Notes in Computer Science 7921, Berlin.
  • Argues that the phrase 'stored-program computer' is ambiguous between several different readings and often conflated with the notion of a Universal Turing Machine, hence that it would be better to refrain from using it.

• Haigh, T. and Priestley, M. (2016). Where code comes from: Architectures of automatic control from Babbage to Algol. Communications of the ACM, 59(1):39–44.

• Randell 1994 argues (p. 12) that the analogy with Universal Turing Machines is more central (p. 13), and that "EDVAC does not qualify as a stored-program computer" because the "representations [of data and instructions] were quite distinct, and no means were provided for converting data items into instructions" or vice versa (p. 13).

• According to Daylight 2013, p. XX, note 3, the phrase 'stored program' was not commonly used in the early 1950s. Furthermore, according to Daylight 2013, §3, p. VII, storing data and instructions together "was based on practical concerns, not theoretical reasoning" of the sort that might have been inspired by Turing's notion of a Universal Turing Machine.

• For a related argument on pancomputationalism, see:
  • Putnam, H. (1988). Representation and Reality. MIT Press, Cambridge, MA.

• For more detailed critiques and other relevant commentary, see:
  • Piccinini, G. (2006). Computational explanation in neuroscience. Synthese, 153(3):343–353.
    • §§1–4 are a good summary of issues related to the nature of computationalism, observer-dependence (as opposed to what Searle calls "intrinsic" computationalism), and universal realizability (or "pancomputationalism")

  • Piccinini, G. (2007). Computational modelling vs. computational explanation: Is everything a Turing machine, and does it matter to the philosophy of mind? Australasian Journal of Philosophy, 85(1):93–115.

  • Rapaport, W.J. (2007). Searle on brains as computers. American Philosophical Association Newsletter on Philosophy and Computers, 6(2):4–9.

  • Piccinini, G. (2010). The mind as neural software? Understanding functionalism, computationalism, and computational functionalism. Philosophy and Phenomenological Research, 81(2):269–311.

  • Shagrir 2016

  • Sprevak, M. (2018). Triviality arguments about computational implementation. In Sprevak, M. and Matteo, C., editors, The Routledge Handbook of the Computational Mind, pages 175–191. Routledge, London.

  • Piccinini, Gualtiero and Corey Maley, Computation in Physical Systems, The Stanford Encyclopedia of Philosophy (Summer 2021 Edition), Edward N. Zalta (ed.)

  • Shagrir 2022

  • Anderson, Neal G.; & Piccinini, Gualtiero (2024), The Physical Signature of Computation: A Robust Mapping Account (Oxford University Press).

§9.4.4: Being a Computer Is Not an Intrinsic Property of Physical Objects:

• For more discussion on what 'intrinsic' means, see:
  • Lewis, D. (1983). Extrinsic properties. Philosophical Studies, 44(2):197–200.

  • Putnam 1987, p. 36.

  • Langton, R. and Lewis, D. (1998). Defining 'intrinsic'. Philosophy and Phenomenological Researc, 58(2):333–345.

  • Lewis, D. (2001). Redefining 'intrinsic'. Philosophy and Phenomenological Research, 63(2):381–398.

  • Skow, B. (2007). Are shapes intrinsic? Philosophical Studies, 133:111–130.

  • Bader, R. M. (2013). Towards a hyperintensional theory of intrinsicality. Journal of Philosophy, 110(10):525–563.
    • A highly technical essay, but it contains useful references to the literature on "intrinsic properties"

  • Marshall, D. (2016). The varieties of intrinsicality. Philosophy and Phenomenological Research, 92(2):237–263

  • Marshall, Dan and Brian Weatherson, Intrinsic vs. Extrinsic Properties, The Stanford Encyclopedia of Philosophy (Spring 2018 Edition), Edward N. Zalta (ed.)

• On the intrinsicness of mass in this connection, see Shagrir 2016, p. 31.

• For more detailed objections to Searle from the nature of the measurement of mass and length, see:
  • Dresner, E. (2010). Measurement-theoretic representation and computation-theoretic realization. Journal of Philosophy, 107(6):272–292.

  • Matthews, R. J. and Dresner, E. (2017). Measurement and computational skepticism. Noûs, 51(4):832–854.

§9.4.6: Everything Is a Computer:

• Although not directly related to the question of whether everything is a computer, the discussion of intendend interpretations and mathematical structuralism mentioned in this section is the topic of the following paper:
  • Grabmayr, Balthasar (2025), "Structure and Computation", Noûs
    From the abstract: "It is a truism of mathematics that differences between isomorphic number systems are irrelevant to arithmetic. This truism … underlies most strands of arithmetical structuralism, the view that arithmetic is about some abstract number structure. In this paper, I challenge this truism by showing that isomorphic systems can differ with regard to important computational features of numbers." In particular, Grabmayr argues that two isomorphic models of the natural numbers can differ in that, in one of them, primality is decidable, whereas in another, it is not.

For more on computers as switch-setting devices, see the discussions of how train switches can implement computations, in:
• Stewart 1994
• B. Hayes 2007

Both of these are also examples of Turing Machines implemented in very different media than silicon (namely, trains)!

§9.6: Gualtiero Piccinini: Computers as Digital String Manipulators

• For a critique of Piccinini's theory, see:
  • Maley, Corey J. (2023), "Medium Independence and the Failure of the Mechanistic Account of Computation", Ergo: An Open Access Journal of Philosophy 10 (Art. 28).

• 
  For an updated version of Piccinini's theory, see Anderson & Piccinini 2024.
  

• On metaphors for the brain (and mind), see:
  • Fritz Kahn's artwork, likening the brain and nervous system to an electronic communications network.

  • On analogies between telephone systems and computers themselves, see:
    • Lewis, W.D. (1953). Electronic computers and telephone switching. Proceedings of the Institute of Radio Engineers, 41(10):1242–1244.

  • Squires, R. (1970). On one’s mind. Philosophical Quarterly, 20(81):347–356.

  • Sternberg, R. J. (1990). Metaphors of Mind: Conceptions of the Nature of Intelligence. Cambridge University Press, New York.

  • Gigerenzer, G. and Goldstein, D. G. (1996). Mind as computer: Birth of a metaphor. Creativity Research Journal, 9(2–3):131–144.

  • Guernsey, L. (2009). Computers track the elusive metaphor. Chronicle of Higher Education, page A11.

  • Angier, N. (2010, 2 February). Abstract thoughts? The body takes them literally. New York Times, page D2.

  • Pasanek, B. (2015). The mind is a metaphor.

• On computationalism, see:
  • Piccinini, G. (2009). Computationalism in the philosophy of mind. Philosophy Compass, 4(3):515–532.

  • Rapaport, W.J. (2012). Semiotic systems, computers, and the mind: How cognition could be computing. International Journal of Signs and Semiotic Systems, 2(1):32–71.
    • Revised version: Rapaport, W.J. (2018). Syntactic semantics and the proper treatment of computationalism. In Danesi, M., editor, Empirical Research on Semiotics and Visual Rhetoric, pages 128–176. IGI Global, Hershey, PA. References on pp. 273–307.

  • Rescorla, Michael, The Computational Theory of Mind, The Stanford Encyclopedia of Philosophy (Fall 2020 Edition), Edward N. Zalta (ed.)

• On the brain as a computer in general, see:
  • Gerard, Ralph W., et al. (1951), Some of the Problems Concerning Digital Notions in the Central Nervous System, Cybernetics: Circular Causal and Feedback Mechanisms in Biological and Social Systems. Transactions of the Seventh Conference (New York: Macy Foundation): 11–57.
    • Highly recommended! A wonderful, round-table discussion among some of the major figures in the field — including McCulloch, Pitts, von Neumann, Wiener, and others — on whether the brain is a computer and on the analog-digital distinction.

  • Ralston 1971, p. 4.

  • Fitch, W.T. (2005). Computation and cognition: Four distinctions and their implications. In Cutler, A., editor, Twenty-First Century Psycholinguistics: Four Cornerstones, pages 381–400. Lawrence Erlbaum Associates, Mahway, NJ.
    • A good discussion by a cognitive biologist of "how the brain computes the mind" (from the Introduction).

  • Naur 2007 says that "the nervous system … has no similarity whatever to a computer" (p. 85).

  • Shagrir 2006

  • Zenil, H. and Hernández-Quiroz, F. (2007). On the possible computational power of the human mind. In Gershenson, C., Aerts, D., and Edmonds, B., editors, Worldviews, Science and Us: Philosophy and Complexity, pages 315–337. World Scientific Publishing, Singapore.
    • Investigates the computational power of the brain and whether the brain might be a "hypercomputer".

  • Schulman, A.N. (2009). Why minds are not like computers. The New Atlantis, pages 46–68.

  • Hutchins, E. (2010). Cognitive ecology. Topics in Cognitive Science, 2:705–715, esp. p. 707.

  • Shagrir, O. (2018). The brain as an input-output model of the world. Minds & Machines, 28(1):53–75.

  • Maley, C.J. (2022), How (and Why) to Think that the Brain Is Literally a Computer. Frontiers in Computer Science, Section: Theoretical Computer Science

  • Polger, Thomas W.; & Shapiro, Lawrence A. (2023), "The Puzzling Resilience of Multiple Realization", Minds and Machines 33:321–3435.

  • Anderson & Piccinini 2024.
    

• On the brain as a Turing Machine in particular, see:
  • It is one thing to argue that brains are (or are not) computers of some kind. It is quite another to argue that they are Turing Machines, in particular. The earliest suggestion to that effect is:
    • McCulloch, W.S. and Pitts, W.H. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 7:114–133. Reprinted in Warren S. McCulloch, Embodiments of Mind (Cambridge, MA: MIT Press, 1965): 19–39.

  • For critical and historical reviews of that classic paper, see:
    • Piccinini 2004

    • Aizawa 2010

    • Piccinini, G. (2020). Neurocognitive Mechanisms: Explaining Biological Cognition. Oxford University Press, Oxford.

    • Piccinini, G. (2022, 11 October), Alan Turing and Neural Computation, The Brains Blog
      • A good short argument for why neural computation might be neither digital nor analog.

    • Piccinini, G. (2023, 2 January), "Why Neuroscience Refutes the Language of Thought", The Brains Blog

  • More recently, the cognitive neuroscientist Stanislas Dehaene and his colleagues have made similar arguments; see:
    • Sackur, J., and Dehaene, S. (2009). The cognitive architecture for chaining of two mental operations. Cognition, 111:187–211.

    • Zylberberg, A., Dehaene, S., Roelfsema, P. R., and Sigman, M. (2011). The human Turing machine: A neural framework for mental programs. Trends in Cognitive Science, 15(7):293–300.

• See also:
  • Shagrir, O. (2010). Computation San Diego style. Philosophy of Science, 77(5):862–874.

  • Piccinini, G. and Shagrir, O. (2014). Foundations of computational neuroscience. Current Opinion in Neurobiology, 25:25–30.

  • Chirimuuta, M. (2021). Your brain is like a computer: Function, analogy, simplification. In Calzavarini, F. and Viola, M., editors, Neural Mechanisms: New Challenges in the Philosophy of Neuroscience, pages 235*–261. Springer, Cham, Switzerland.

§9.7.2: Is the Universe a Computer?

• For a different take on the question of whether the solar system computes Kepler's laws of motion, in the context of "pancomputationalsim" (the view that "every deterministic physical system computes some function"), see:
  • Campbell, D.I. and Yang, Y. (2021). Does the solar system compute the laws of motion? Synthese, 198:3203–3220.

• On cellular automata in general, see:
  • "Cellular Automaton" (Wikipedia)

  • The relatively informal presentation in Bernhardt 2016, Ch. 5.

  • The more formal presentation in:
    • Burks, A.W., editor (1970). Essays on Cellular Automata. University of Illinois Press, Urbana, IL.
      • Philosopher and mathematician Arthur Burks was one of the people involved in the construction of ENIAC and EDVAC.

• On cellular automata that are Turing Machine-equivalent, see these Wikipedia articles:
  • "Turing Completeness"
  • "Rule 110"
  • "Conway's Game of Life"

• On Wolfram and his theories, see his homepage and:
  • Wolfram, S. (2002). Introduction to A New Kind of Science

  • Bernhardt 2016, Chs. 5, 6

  • For a critical review, see:
    • Weinberg, S. (2002, 24 October). Is the universe a computer? New York Review of Books, 49(16).
    • Shepard, H. and Weinberg, S. (2003, 16 January). Is the universe a computer? (letters to the editor). New York Review of Books, 50(1).

  • Aaronson, Scott (2002), "Book Review on A New Kind of Science", Quantum Information and Computation 2(5):410–423.

  • Aaronson, S. (2011, 9 December). Quantum computing promises new insights, not just supermachines. New York Times, page D5.
    • Claims that quantum computing has "overthrown" views like those of
      • Wolfram, S. (2002). A New Kind of Science. Wolfram Media
      that "the universe itself is basically a giant computer … by showing that if [it is, then] it's a vastly more powerful kind of computer than any yet constructed by humankind."

• On Lloyd and his theories, see:
  • Lloyd, S. (2000). Ultimate physical limits to computation. Nature, 406:1047–1054.
    • Investigates "quantitative bounds to the computational power of an 'ultimate laptop' with a mass of one kilogram confined to a volume of one litre."

  • Lloyd, S. (2002). Computational capacity of the universe. Physical Review Letters, 88(23):237901–1 — 237901–4.
    • Argues that
      "All physical systems register and process information. The laws of physics determine the amount of information that a physical system can register (number of bits) and the number of elementary logic operations that a system can perform (number of ops). The Universe is a physical system. The amount of information that the Universe can register and the number of elementary operations that it can have performed over its history are calculated. The Universe can have performed 10^120 ops on 10^90 bits 10^120 bits including gravitational degrees of freedom)."

  • Lloyd, S. (2006). Programming the Universe: A Quantum Computer Scientist Takes on the Cosmos. Alfred A. Knopf, New York,

  • Two reviews of Lloyd's work:
    • Schmidhuber, J. (2006, July-August). The computational universe. American Scientist, 94:364ff.

    • Powell, C.S. (2006, 2 April). Welcome to the machine. New York Times Book Review, page 19
      • Offers an overview and summary that is worth reading independently of the book being reviewed.

• Konrad Zuse also argued that the universe is a computer; see:
  • Schmidhuber, J. (2002). Zuse's thesis: The universe is a computer

  • Copeland, B.J., Shagrir,O., and Sprevak, M. (2018). Zuse's thesis, Gandy's thesis, and Penrose's thesis. In Cuffaro, M.E. and Fletcher, S.C., editors, Physical Perspectives on Computation, Computational Perspectives on Physics, pages 39–59. Cambridge University Press, Cambridge, UK.

• For more on the universe as a computer, see:
  • Chaitin 2006

  • Copeland, B.J., Sprevak, M., and Shagrir, O. (2017). Is the whole universe a computer? In Copeland, B.J., Bowen, J., Sprevak, M., and Wilson, R., editors, The Turing Guide, pages 445–462. Oxford University Press, Oxford.

  • Piccinini and Anderson 2020

  • 
    Anderson & Piccinini 2024.
    

• Related to Lloyd is Bostrom's work on whether we are living in a computer simulation:
  • Bostrom, N. (2003). Are you living in a computer simulation? Philosophical Quarterly, 53(211):243–255.

  If Lloyd is right, then the universe is a computer, and we are data structures in its program, brought to life as it were by its execution.

  If Bostrom is right, then we are data structures in someone else's program. If theists (computational theists?) are right, then we are data structures in God's program.

  For an argument that simulation theories are not scientific, see Dunning, B. (2018, 8 May). Are you living in a simulation?

  For more on Bostrom's ideas, see the Further Readings for Ch. 19.

§9.8: Conclusion

• For a critique of Piccinini's Definition P3, see Maley 2023.