Philosophy of Computer Science: Online Resources

Further Readings for Chapter 4:

Science

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.)

§4.2: Science and Non-Science

• My favorite introduction to the philosophy of science is:
  • Okasha, S. (2002). Philosophy of Science: A Very Short Introduction. Oxford University Press, Oxford.

• Other (longer!) book-length treatments include:
  • McCain, G. and Segal, E. M. (1969). The Game of Science. Brooks/Cole, Belmont, CA.

  • Giere, R. N. (1984). Understanding Scientific Reasoning, 2nd edition. Holt, Rinehart & Winston, New York.

  • Rosenberg, A. (2000). Philosophy of Science: A Contemporary Introduction. Routledge, London.

• Two good short introductions are:
  • Papineau, D. (2003). Philosophy of science. In Bunnin, N. and Tsui-James, E., editors, The Blackwell Companion to Philosophy, 2nd edition, pages 286–316. Blackwell, Malden, MA.

  • Kolak, D., Hirstein, W., Mandik, P., and Waskan, J. (2006). Cognitive Science: An Introduction to Mind and Brain. Routledge, New York, §4.4.2.

• Other readings include:
  • Hempel, C. G. (1966). Philosophy of Natural Science. Prentice-Hall, Englewood Cliffs, NJ.
    • Discusses the differences between empirical and non-empirical sciences.

  • Chaitin, G. J. (1968). On the difficulty of computations. In Chaitin, G. J., editor, Thinking about Göodel and Turing: Essays on Complexity, 1970–2007, pages 3–17. World Scientific Publishing.
    • §7 discusses "classical problems of the methodology of science" as part of an essay on computational complexity.

  • Dyson, F. (2006, 19 October). Writing nature's greatest book. New York Review of Books, 53(16).
    • An interesting book review that discusses some of the origins of science.

• For a detailed defense of the view that science is (merely?) any systematic study, see:
  • Hoyningen-Huene, P. (2008). Systematicity: The nature of science. Philosophia, 36:167–180.

§4.4.1: Description

• On Ernst Mach, see:
  Cohen, Robert S. (1968), "Ernst Mach: Physics, Perception and the Philosophy of Science", Synthese 18:132–170.

• On physicalism, see:
  • Stoljar, Daniel, Physicalism, The Stanford Encyclopedia of Philosophy (Summer 2021 Edition), Edward N. Zalta (ed.).

• On Kant's theory of noumena and phenomena, see:
  • Stang, Nicholas F., Kant's Transcendental Idealism, The Stanford Encyclopedia of Philosophy (Spring 2022 Edition), Edward N. Zalta (ed.).
    • Especially §6.1, "Phenomena and Noumena"

  • The shortest (but by no means the easiest!) introduction to Kant's philosophy is:
    • Kant, I. (1783). Prolegomena to Any Future Metaphysics. Cambridge University Press, 2004, New York. Trans. by Gary Hatfield.

  • For further discussion, see:
    • Putnam 1987

    • Becker, A. (2018). What Is Real? The Unfinished Quest for the Meaning of Quantum Physics. John Murray, London.

    • Rohlf, Michael, Immanuel Kant, The Stanford Encyclopedia of Philosophy (Fall 2020 Edition), Edward N. Zalta (ed.), esp. §2.2, "Kant's Copernican revolution in philosophy" and §3, "Transcendental idealism".

§4.4.3: Prediction

On Quantum Mechanics, see:
• Gopnik, Adam (2015, 30 November). Spooked. The New Yorker, pages 84–86.
  • A cultural critic's views on what we can learn about the nature of science from the paradox of quantum entanglement in physics.

• Weinberg, S. (2017, 19 January). The trouble with quantum mechanics. New York Review of Books, 64(1):51–53.

• Albert, D. Z. (2018, 19 April). Quantum's leaping lizards. New York Review of Books, 65(7):55–57.

§4.5: Instrumentalism vs. Realism:

• Chakravartty, Anjan, Scientific Realism, The Stanford Encyclopedia of Philosophy (Summer 2017 Edition), Edward N. Zalta (ed.).

§4.6: Scientific Theories:

• On scientific theories in general, see:
  • Winther, Rasmus Grønfeldt, The Structure of Scientific Theories, The Stanford Encyclopedia of Philosophy (Spring 2021 Edition), Edward N. Zalta (ed.).

• On the syntactic vs. semantic views, see:
  • Hesse, Mary B. (1966), "The Function of Models: A Dialogue", in Mary B. Hesse, Models and Analogies in Science (Notre Dame, IN: University of Notre Dame Press): 7–56.

  • Hesse, Mary B. (2017), "Models and Analogies", in W.H. Newton-Smith (ed.), A Companion to the Philosophy of Science (Blackwell):299–307

  and the debate in:

  • Halvorson, H. (2012). What scientific theories could not be. Philosophy of Science, 79(2):183–206.

  • Glymour, C. (2013). Theoretical equivalence and the semantic view of theories. Philosophy of Science, 80(2):286–297.

  • Halvorson, H. (2013). The semantic view, if plausible, is syntactic. Philosophy of Science, 80:475–478.

• For a wonderful discussion of the nature of science with respect to evolution, see the judge's opinion in the celebrated 2005 legal case of Kitzmiller v. Dover Area School District (esp. §4, "Whether ID [Intelligent Design] Is Science").

• Willingham, D. T. (2011, May). Trust me, I'm a scientist. Scientific American.
  • Discusses "Why so many people choose not to believe what scientists say".

• Gopnik, Adam (2015). The evolution catechism. The New Yorker.
  • Discusses the nature of 'theory' as it is used in science and in ordinary language.

§4.7: "The" Scientific Method:

• A good general introduction is:
  • Hepburn, Brian and Hanne Andersen, Scientific Method, The Stanford Encyclopedia of Philosophy (Summer 2021 Edition), Edward N. Zalta (ed.),
    • Esp. §5.2 on "computer methods".

• 
  On the nature of scientific thinking in practice, see:
  • Nersessian, Nancy J. (2024), "How Do Scientists Think? Contributions Toward a Cognitive Science of Science", Topics in Cognitive Science
  

• Gabrielle-Émilie Le Tonnelier de Breteuil, Marquise du Châtelet (1706–1749) was a French physicist and philosopher. In a review of a recent biography of her, Adam Gopnik writes that …
  … she insisted that, though no number of positive affirmations can establish a theory, one falsification can disprove it. "A single experiment is not enough to confirm a hypothesis, but one alone is sufficient to reject it," she wrote, two centuries before Karl Popper made the idea a commonplace of twentieth-century science. (p. 62 of Gopnik's "Does the Enlightenment's Great Female Intellect Need Rescuing?", The New Yorker (4 November 2024), pp. 60–65)

• On pseudo-science, see:
  • Pigliucci, M. and Boudry, M., editors (2013a). Philosophy of Pseudoscience: Reconsidering the Demarcation Problem. University of Chicago Press, Chicago.

  • Pigliucci, M. and Boudry, M. (2013b, 10 October). The dangers of pseudoscience. New York Times.

  • Curd, M. (2014). Review of Pigliucci and Boudry 2013a. Notre Dame Philosophical Reviews.

  • Hansson, Sven Ove, Science and Pseudo-Science, The Stanford Encyclopedia of Philosophy (Fall 2021 Edition), Edward N. Zalta (ed.)

• On astrology, see:
  • Thagard, P. (1978). Why astrology is a pseudoscience. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association, 1:223–234.

• On Marxist economics, see:
  • Hudelson, R. (1980). Popper's critique of Marx. Philosophical Studies, 37(3):259–270.

• On Freudian theories, see:
  • Grünbaum, A. (1984). The Foundations of Psychoanalysis. University of California Press, Berkeley,

  • Prof. Adolf Grünbaum's Publications on Psychoanalysis and Philosophy of Medicine

  On homeopathy — and a very good general survey of the nature of pseudoscience — see:

  • Mukerji, N.; Ernst, E. (2022), Why homoeopathy is pseudoscience. Synthese 200:394.

§4.8.2: The Logic of Falsifiability:

• On rules of logic such as Modus Tollens and DeMorgan's Law, see:
  • Any introductory logic text; my favorite is:
    • Schagrin, M.L., Rapaport, W.J., and Dipert, R.R. (1985). Logic: A Computer Approach. McGraw-Hill, New York.

  • Rapaport, W.J. (1992). Logic, propositional. In Shapiro, Stuart C., editor, Encyclopedia of Artificial Intelligence, 2nd edition, pages 891–897. John Wiley, New York.

  • Propositional Calculus (Wikipedia)

• On quantum logic, see:
  • Wilce, Alexander, Quantum Logic and Probability Theory, The Stanford Encyclopedia of Philosophy (Fall 2021 Edition), Edward N. Zalta (ed.).

• Papineau 2003, §1.8, pp. 300–301.

• Ladyman, James, Structural Realism, The Stanford Encyclopedia of Philosophy (Winter 2020 Edition), Edward N. Zalta (ed.).

• "Pessimistic Induction" (Wikipedia)

§4.9: Scientific Revolutions:

• Corcoran, J. (2007). Scientific revolutions. In Lachs, J. and Talisse, R., editors, Encyclopedia of American Philosophy. Routledge, New York.

• Weinberger, D. (2012). Shift happens. The Chronicle [of Higher Education] Review, pages B6–B9.

• Papineau, D. (2017). Is philosophy simply harder than science? Times Literary Supplement Online.

• Dhar, Vasant (2024), "The Paradigm Shifts in Artificial Intelligence", Communications of the ACM, Online First
  

§4.10: Other Alternatives:

The AI researcher and sociologist M. Ross Quillian made remarks similar to those of Feyerabend in:

• Quillian, M.R. (1994). A content-independent explanation of science's effectiveness. Philosophy of Science, 61(3):429–448, esp. §2.2.

• On "the unreasonable effectiveness of mathematics in science", see:
  • Wigner, E. (1960). The unreasonable effectiveness of mathematics in the natural sciences. Communications in Pure and Applied Mathematics, 13(1).

  • Hamming, R. (1980). The unreasonable effectiveness of mathematics. American Mathematical Monthly, 87(2).
    • A response by Turing Award-winner Richard Hamming.

  • Waxman, D. (2021). Is mathematics unreasonably effective? Australasian Journal of Philosophy, 99(1):83–99
    • Discusses how aesthetics in mathematics might help respond to Wigner.

  • Wilson, E. and Frenkel, E. (2013). Two views: How much math do scientists need? Notices of the AMS, 60(7):837–838
    • Discusses a related question about whether scientists need to study mathematics at all.

• Mark Steedman, a computational linguist, has some interesting things to say on the differences between a discipline such as physics and a discipline such as CS (in general) and computational linguistics (in particular):
  • Steedman, M. (2008). On becoming a discipline. Computational Linguistics, 34(1):137–144, especially §1, "The Public Image of a Science".

• Tedre, M. (2011). Computing as a science: A survey of competing viewpoints. Minds and Machines, 21(3):361 387.
  • Surveys ways in which computing is a science.

  Tedre and Moisseinen 2014 is a survey of the nature of experiments in science, and whether CS is experimental in nature.

• On the relationship between scientific theories and algorithmic information theory, see:
  • Chaitin, G.J. (2005). Epistemology as information theory: From Leibniz to Ω, pp.~5–10.

• In light of 2024's Nobel prizes in physics and in chemistry that went to computer scientists, in particular, to AI researchers (as opposed to physicists or chemists), there has been a flurry of opinions on the nature of CS (and AI) as a science. Here are some articles to consider:
  • Burdick, Alan; & Miller, Katrina (2024), "A Shift in the World of Science", New York Times (13 October)

  • Fortnow, Lance (2024), "Computing and the Nobels", Computational Complexity (blog, 16 October)

    Gasarch, Bill (2024), "Contrast an Episode of Columbo with the recent Nobel Prizes", Computational Complexity (blog, 20 October)

  • Haigh, Thomas (2024), "Between the Booms: AI in Winter", Communications of the ACM 67(11):18–23.

  • Ioannidis, Yannis (2024), "The 5th Paradigm: AI-Driven Scientific Discovery" Communications of the ACM (15 November)

  • Marcus, Gary (2024), "Two Nobel Prizes for AI, and Two Paths Forward", Marcus on AI (blog, 8 October)

  • Metz, Cade (2024), "How Does It Feel to Win a Nobel Prize? Ask the 'Godfather of A.I.', New York Times (8 October)

• The final paragraph of this section is rather confusing. I must admit that I'm not really sure what I was trying to say. Clearly, neural network/machine learning approaches are not "logical" in the way that GOFAI approaches can be (though not all of them are logical, either; here I'm thinking of Roger Schank's approaches, which are GOFAI, but not mathematically logical).

  Given that the passage is in a closing section of the chapter on the nature of science, offering further considerations on whether CS is a science or not, I suspect that what I wanted to say was that AI might be considered to be a non-empirical science. But even that is not quite right, because neural-network approaches are certainly more empirical than GOFAI approaches.

  Exercise for the reader: Can you figure out what I might have had in mind when I wrote that paragraph? :-)
  

• For more on how GOFAI and artificial neural networks can be integrated, especially in light of "large language models" such as ChatGPT, see:
  • Wolfram, Stephen (2023), What is ChatGPT and Why Does It Work? (Wolfram Media)
    • Online here and here, as well as the updated implementation here

  • Lenat, Doug; & Marcus, Gary (2023), "Getting from Generative AI to Trustworthy AI: What LLMs Might Learn from Cyc"