The Algorithmic Theory of Laws

  • Billy WheelerEmail author
Part of the SpringerBriefs in Philosophy book series (BRIEFSPHILOSOPH)


Chapter 4 brings together the insights of the first three chapters, and argues that the best way to understand ideal laws is to think of them as rules or algorithms for compressing empirical data. Idealization is explained as a form of lossy compression. Lossy compression is tolerated in scientific theories because of predictive redundancy in our theories. Idealizations in scientific theories and their application are accounted for as compression artefacts left over from the lossy compression. A number of possible objections to this explanation are considered and responses given.


  1. Abe, N., R. Khardon, and T. Zuegmann. 2001. Algorithmic learning theory. Washington: Springer.CrossRefGoogle Scholar
  2. Acharya, T., and P. Tsai. 2005. JPEG2000 standard for image compression: concepts, algorithms and VLSI architectures. New Jersey: Wiley-Interscience.CrossRefGoogle Scholar
  3. Armstrong, D. 1983. What is a law of nature?. Cambridge: Cambridge University Press.Google Scholar
  4. Azatoth. 2011. Felis silvestris silvestris small gradual decrease of quality.png. Photo. CC BY 3.0, Oct 3.
  5. Banks, E. 2004. The philosophical roots of Ernst Mach’s economy of thought. Synthese 139 (1): 23–53.CrossRefGoogle Scholar
  6. Barrow, J. 1991. Theories of everything. Oxford: Oxford University Press.Google Scholar
  7. Bogen, J., and J. Woodward. 1988. Saving the phenomena. The Philosophical Review 97 (3): 303–352.CrossRefGoogle Scholar
  8. Braddon-Mitchell, D. 2001. Lossy Laws. Nous 35 (2): 260–277.CrossRefGoogle Scholar
  9. Chaitin, G. 1987. Algorithmic information theory. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  10. Chaitin, G. 2005. Meta maths—The quest for omega. London: Atlanta Books.Google Scholar
  11. Cohen, C., and C. Callender. 2009. A better best system of lawhood. Philosophical Studies 145 (1): 1–34.CrossRefGoogle Scholar
  12. Copeland, J., and D. Proudfoot. 2005. Turing and the computer. In Alan Turing’s automatic computing engine, ed. J. Copeland, 107–148. Oxford: Oxford University Press.CrossRefGoogle Scholar
  13. Davies, P. 1995. Algorithmic compressibility, fundamental and phenomenological laws. In Laws of nature: essays on the philosophical, scientific and historical dimensions, ed. F. Weinert, 248–267. Berlin: Walter de Gruyter & Co.Google Scholar
  14. Earman, J. 1984. Laws of nature: The empiricist challenge. In D.M. Armstrong, ed. R.J. Bogden, 191–223. Dordrecht: Reidel.Google Scholar
  15. Einstein, A. 1933. On the method of theoretical physics—The Herbert Spencer lecture. Performed by Albert Einstein. University of Oxford. June 10.Google Scholar
  16. Feynman, R. 1963. Lectures on physics, vol. 1. Reading: Addison-Wesley.Google Scholar
  17. Floridi, L. 2010. Information: A very short introduction. Oxford: Oxford University Press.CrossRefGoogle Scholar
  18. Gell-Mann, M. 1987. Simplicity and complexity in the description of nature. Performed by M. Gell-Mann. The Caltech Associate, Pasadena. October 1.Google Scholar
  19. Giere, R. 1988. Explaining science: A cognitive approach. Chicago: University of Chicago Press.CrossRefGoogle Scholar
  20. Grunwald, P., and P. Vitanyi. 2008. Algorithmic complexity. In Philosophy of information, ed. P. Adriaans and J. van Benthem, 289–328. Amsterdam: Elsevier.Google Scholar
  21. Harris, T. 2003. Data models and the acquisition and manipulation of data. Philosophy of Science 70: 1508–1517.CrossRefGoogle Scholar
  22. Kolmogorov, A. 1965. Three approaches to the definition of the quantity of information. Problems of Information Transmission 1 (1): 1–7.Google Scholar
  23. Landauer, R. 1996. The physical nature of information. Physics Letters A 217: 188–193.CrossRefGoogle Scholar
  24. Langley, P., G. Bradshaw, H. Simon, and J. Zytow. 1987. Scientific discovery: Computational explorations of the creative Process. Cambridge: MIT Press.Google Scholar
  25. Lewis, D. 1973. Counterfactuals. Oxford: Blackwell.Google Scholar
  26. Lewis, D. 1983. New work for a theory of universals. Australian Journal of Philosophy 61 (4): 343–377.CrossRefGoogle Scholar
  27. Lewis, D. 1986. Causation. In Philosophical papers, vol. II, ed. D. Lews, 159–213. Oxford: Oxford University Press.Google Scholar
  28. Lewis, D. 1994. Humean supervenience debugged. Mind 103 (412): 473–490.CrossRefGoogle Scholar
  29. Lieb, E., and J. Yngvason. 1999. The physics and the mathematics of the second law of thermodynamics. Physics Reports 310: 1–96.CrossRefGoogle Scholar
  30. Lloyd, S. 2006. Programming the universe: A quantum computer scientist takes on the cosmos. New York: Alfred Knopf.Google Scholar
  31. Loewer, B. 1996. Humean supervenience. Philosophical Topics 24 (1): 101–127.CrossRefGoogle Scholar
  32. Loewer, B. 2007. Laws and natural properties. Philosophical Topics 35 (1 and 2): 313–328.Google Scholar
  33. Lynch, M. 1988. The externalized retina: Selection and mathematization in the visual documentation of objects in the life sciences. Human Studies 11 (2/3): 201–234.Google Scholar
  34. Mach, E. 1883 (1960). Science of mechanics. Illinois: Open Court.Google Scholar
  35. Mach, E. 1894 (1943). Popular scientific lectures. Illinois: Open Court.Google Scholar
  36. McAllister, J. 1997. Phenomena and patterns in data sets. Erkenntnis 47 (2): 217–228.CrossRefGoogle Scholar
  37. McAllister, J. 2003. Algorithmic randomness in empirical data. Studies in the History and Philosophy of Science 34: 633–646.CrossRefGoogle Scholar
  38. McAllister, J. 2010. The ontology of patterns in empirical data. Philosophy of Science 77 (5): 804–814.CrossRefGoogle Scholar
  39. Nelson, M. 1991. The data compression book. New York: Henry and Holt.Google Scholar
  40. Pennebaker, W., and J. Mitchell. 1993. JPEG: Still image data compression standard. New York: Van Nostrand Reinhold.Google Scholar
  41. Reichenbach, H. 1947. Elements of Symbolic Logic. New York: The Macmillan Company.Google Scholar
  42. Roberts, J. 2008. The law-governed universe. Oxford: Oxford University Press.CrossRefGoogle Scholar
  43. Salomon, D. 2008. A concise introduction to data compression. London: Springer.CrossRefGoogle Scholar
  44. Solomonoff, R. 1964a. A formal theory of inductive inference: Part I. Information and Control 7 (1): 1–22.CrossRefGoogle Scholar
  45. Solomonoff, R. 1964b. A formal theory of inductive inference: Part II. Information and Control 7 (2): 224–254.CrossRefGoogle Scholar
  46. Suppes, P. 1962. Models of data. In Logic, methodology and philosophy of science: Proceedings of the 1960 international congress, ed. E. Nagel, P. Suppes, and A. Tarski, 252–261. Stanford: Stanford University Press.Google Scholar
  47. Taylor, B. 1993. On natural properties in metaphysics. Mind 102 (405): 81–100.CrossRefGoogle Scholar
  48. Teller, P. 2010. Saving the phenomena today. Philosophy of Science 77 (5): 815–826.CrossRefGoogle Scholar
  49. Tomkow, T. 2013. “The Computational Theory of the Laws of Nature.” September 1.
  50. Twardy, C., S. Gardner, and D. Dowe. 2005. Empirical data sets are algorithmically compressible: Reply to McAllister? Studies in the History and Philosophy of Science 36: 391–402.CrossRefGoogle Scholar
  51. van Fraassen, B. 1989. Laws and symmetry. Oxford: Clarenden Press.CrossRefGoogle Scholar
  52. Wheeler, B. 2016. Simplicity, language-dependency and the best system account of laws. Theoria: An International Journal for Theory, History and Foundations of Science 31 (2): 189–206.Google Scholar
  53. Woodward, J. 1989. Data and phenomena. Synthese 79 (3): 393–472.CrossRefGoogle Scholar
  54. Woodward, J. 2014. Simplicity in the best systems account of laws of nature. The British Journal for the Philosophy of Science 65 (1): 91–123.CrossRefGoogle Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Switzerland AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of PhilosophySun Yat-Sen UniversityZhuhaiChina

Personalised recommendations