Philosophy of Computer Science: Online Resources

Further Readings for Chapter 12:

Software and Hardware

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.)

§12.1: The Nature of Computer Programs:

• Gemignani, M. (1981). What is a computer program? American Mathematical Monthly, 88(3):185–188.
  • A good survey of the issues that arise when trying to say what a computer program is.

• Haigh and Priestley 2016 is an interesting history of both programming and the term 'program', arguing that Ada Lovelace was probably not the first computer programmer and that programming computers originally had a lot in common with concert "programming" or radio "programs".

• Lonati, Violetta, et al. (2022), "What We Talk About When We Talk about Programs", ITiCSE-WGR '22: Proceedings of the 2022 Working Group Reports on Innovation and Technology in Computer Science Education:117–164.
  • Written from the perspective of computer science education. Has a very good survey of the nature of computer programs.

§12.2: Programs and Algorithms:

• A nice description — for readers who are not computer scientists — of how algorithms are implemented in computers can be found in:
  • Colburn, T.R. (1999). Software, abstraction, and ontology. The Monist, 82(1):3–19, esp. pp. 6–9.

• Dennett 2017, p. 256, discusses how Universal Turing Machines running software S often "evolve" into "'dedicated' hardware" Turing Machines that only execute S.

§12.3.2: Software, Music, and Art:

• There are other interesting relationships between software and art:
  • Bond, G. W. (2005). Software as art. Communications of the ACM, 48(8):118–124, says:
    "A programming language is a language after all, albeit a hightly constrained one. As such, it is a perfect medium for the poet comfortable with other highly constrained poetic forms like the sonnet or haiku". (p. 123)

  • On the literary value of programs, see:

    • Knuth 1984 on "literate programming".

    • Schneider, D. (2007). Neatness counts: Messy computer code is normally frowned on—but not always. American Scientist, pages 213–214.

    • Chandra, V. (2014). Geek Sublime: The Beauty of Code, the Code of Beauty. Graywolf Press.
      • Reviewed in Gleick, J. (2014, 24 August). A unified theory. New York Times Book Review, page 17

    • Turner, Z. (2015, 30 March). Beautiful code. The New Yorker, pages 21–22.
      • On the first art auction of computer code.

• For some examples of "aesthetic" or "playful" programs, see:
  • "Most Adaptable Programs" (1991, 1996)
    • Two examples of programs that can be compiled and run in several different programming-language systems, as if there were a sentence that was grammatical and meaningful in both English and French — is there such a sentence?

  • "dodsond1"
    • A text-to-"Pig Latin" translator (12th International Obfuscated C Code Contest, 1995) — which must be seen to be believed!

  • For more examples, see The International Obfuscated C Code Contest

§12.3.4: Copyright vs. Patent:

• For general discussion of the issues surrounding legal protection of programs, see:

  • Samuelson, P. (1988). Is copyright law steering the right course? IEEE Software, pages 78–86

  • Samuelson, P. (1991). Digital media and the law. Communications of the ACM, 34(10):23–28.

  • Forester, T. and Morrison, P. (1994). Computer Ethics: Cautionary Tales and Ethical Dilemmas in Computing; Second Edition. MIT Press, Cambridge, MA, Ch. 3, pp. 57–68.

  • Johnson, D.G. (2001). Computer Ethics. Prentice Hall, Upper Saddle River, NJ, Ch. 6, pp. 137–167 ("Property Rights in Computer Software")

  • Samuelson, P. (2003). Unsolicited communications as trespass? Communications of the ACM, 46(10):15–20.

  • Klemens, B. (2006). Math You Can't Use: Patents, Copyright, and Software. Brookings Institution Press.
    • Excerpted in Klemens, Ben (2006), "Private Numbers", Chronicle [of Higher Education] Review 52(22) (3 February): B2.

  • Samuelson, P. (2007). Software patents and the metaphysics of section 271(f). Communications of the ACM, 50(6):15–19.

  • Boyle, J. (2009). What intellectual property law should learn from software. Communications of the ACM, 52(9):71–76.

  

• On copyright protection for the output of large language models such as ChatGPT, see:
  • Samuelson, Pamela (2025, 24 June), "AI Authorship Revisite", Communications of the ACM 68(7):26–28.
  

• For the story of the first software patent, see:
  Sandomir, Richard (2023), "Martin Goetz, Who Received the First Software Patent, Dies at 93", New York Times (21 October).

• For follow-ups to Newell's essay (including links to commentary by Donald Knuth), see "Newell 1986: The Models Are Broken"

• The ontological task is investigated in:

  • Smith 1996

  • Smith, B.C. (2002). The foundations of computing. In Scheutz, M., editor, Computationalism: New Directions, pages 23–58. MIT Press, Cambridge, MA.

  • Eden, A.H. (2007). Three paradigms of computer science. Minds and Machines, 17(2):135–167

  • Turner, R. and Eden, A.H. (2007). Towards a programming language ontology. In Dodig-Crnkovic, G. and Stuart, S., editors, Computation, Information, Cognition: The Nexus and the Liminal, pages 147–159. Cambridge University Press, Cambridge, UK.

• On ontologies more generally, see the work by philosopher Barry Smith and his colleagues at The Buffalo Ontology Site, which includes Duncan's ontology of software vs. hardware:
  • Duncan, W.D. (2017). Ontological distinctions between hardware and software. Applied Ontology, 12(17):5–32.
    • Duncan surveys Moor, Suber, and Colburn, but argues that a formal ontology is needed before a useful distinction between hardware and software can be made, and that, on the Basic Formal Ontology, "a piece of computational hardware is an ontologically independent entity, whereas a software program is an ontologically dependent entity."

§ 12.4.1: Levels of Understanding:

• For more on the intentional stance, see:

  • Dennett, D.C. (1987). The Intentional Stance. MIT Press, Cambridge, MA.

  • Block, N. (1995). The mind as software of the brain. In Smith, E.E. and Osherson, D.N., editors, An Invitation to Cognitive Science, 2nd Edition; Vol. 3: Thinking, pages 377–425. MIT Press, Cambridge, MA, §11.5.

  • Miller, C.A., editor (2004). Human-Computer Etiquette: Managing Expectations with Intentional Agents. Special section of Communications of the ACM, 47(4) (April): 30–61.
    • Contains some interesting follow-ups from the perspective of CS to Dennett's theory of different "stances".

  • Dennett, D. C. (2009). Intentional systems theory. In McLaughlin, B., Beckermann, A., and Walter, S., editors, The Oxford Handbook of Philosophy of Mind, pages 339–350. Oxford University Press, Oxford

  • Dennett 2013, Ch. 18 (esp. §18.3.3) and Ch. 21.

• If you are uneasy about the intentional stance's use of psychological terms to describe computers, you might treat the terms 'desired', 'believed', and 'formed the intention' as metaphorical; see:

  • Lakoff, G. and Johnson, M. (1980). Conceptual metaphor in everyday language. Journal of Philosophy, 77(8):453–486.

  • Newell, A. (1980). Physical symbol systems. Cognitive Science, 4:135–183,§6.
    • Newell holds that "these levels of description do not exist just in the eye of the beholder, but have a reality in … the real world" (p. 173)

  • Figdor, C. (2017). On the proper domain of psychological predicates. Synthese, 194:4289‐4310.
    • Argues that (some of) these uses are literally true.
    • But:

      • Dennett, D.C. (1981). Three kinds of intentional psychology. In Healey, R., editor, Reduction, Time and Reality, pages 37–61. Cambridge University Press, Cambridge, UK,

      disagrees:

      "Intentional system theory deals just with the performance specifications of believers while remaining silent on how the systems are to be implemented."

      See also p. 59, where he talks about "realizations or embodiments of a Turing machine".

§12.4.4: "Following Instructions":

• On center embedding:

  A simpler example of center embedding is the sentence

  Mice cats chase eat cheese.

  where the sentence 'Cats chase' is embedded in the center of the sentence 'Mice eat cheese', qualifying the kind of mice that eat cheese, namely, the ones that cats chase.

  If we replace these words with others, we get a center-embedding sentence that is harder to understand:

  Dogs dogs dog dog dogs.

  Here, the sentence 'Dogs dog' (meaning "Dogs follow closely") is embedded in the similar sentence 'Dogs dog dogs' (meaning "Dogs follow other dogs closely").

  Finally, if we replace these words with still others, we get the following perfectly grammatical and meaningful sentence of English:

  Buffalo buffalo buffalo buffalo buffalo.

  Instead of cats chasing and dogs dogging, we have buffalo buffaloing (that is, bison intimidating; note that the plural of 'buffalo' is also 'buffalo', just as the plural of 'sheep' is also 'sheep'). Embedding 'Buffalo buffalo' into 'Buffalo buffalo buffalo' (meaning "Some bison intimidate other bison"), we get the sentence above, which means "Bison that other bison intimidate intimidate in turn still other bison").

  • See A History of the Sentence "Buffalo buffalo buffalo buffalo buffalo."

• On following instructions:
  • Cole, D. (1984). Thought and thought experiments. Philosophical Studies, 45:431–444, § III

  • Dennett 1995, pp. 372–373
    • Discusses "two profoundly different ways of building dams: the way beavers do and the way we do." Recast in the present terminology, beavers merely behave in accordance with a genetic algorithm, whereas humans follow explicit engineering procedures.

  • The distinction between "following instructions" and "behaving in accordance with instructions" is sometimes described in terms of rule-following vs. being rule-governed. On this distinction, see:
    • Miller, Alexander and Olivia Sultanescu, "Rule-Following and Intentionality", in The Stanford Encyclopedia of Philosophy (Summer 2022 Edition), Edward N. Zalta (ed.).

• On doing one thing by doing another:

  A distinction is sometimes made between a "basic action" (think of a primitive operation of a programming language) and a "non-basic action" (think of a new operation defined by procedural abstraction, as discussed in §16.10.3). Non-basic actions are done "by doing" certain basic actions.

  (This is also related to the distinction made in §16.5 between an algorithm (A) and its purpose or goal (G), as in the formulation: In order to accomplish G, do A. Here, A is like a basic action, and G is like a non-basic action.)

  For more on the basic/non-basic distinction, see:

  • Danto, A.C. (1965), "Basic Actions", American Philosophical Quarterly 2(2):141–148.

  • Kelley, Mikayla (2022), "How to Perform a Nonbasic Action", Noûs.

• On the Question for the Reader concerning what a Universal Turing Machine does, see the correspondence between John Case and me, in which he argues that a Universal Turing Machine like the one in the Questions does add, but not necessarily the way that its virtual Addition Turing Machine does.

§12.5: Suber: Software is Pattern:

"Does it help to know that any large number, however random, hides a masterpiece? All you need is the right player."

—Richard Powers, Orfeo: A Novel (New York: W.W. Norton, 2014), p. 199.

• Sacks, O. (1999). Migraine: Revised and Expanded. Random House/Vintage Books, New York, pp. 175–176

• Benenson, Y. (2013). Recombinatorial logic. Science, 340(6132):554–555.
  • On implementations of logic gates by bacteria!

• Pennisi, E. (2013). Nervous system may have evolved twice. Science, 339(6118):391.