Foundations of Physics

, Volume 49, Issue 11, pp 1253–1277 | Cite as

How Downwards Causation Occurs in Digital Computers

  • George EllisEmail author
  • Barbara Drossel


Digital computers carry out algorithms coded in high level programs. These abstract entities determine what happens at the physical level: they control whether electrons flow through specific transistors at specific times or not, entailing downward causation in both the logical and implementation hierarchies. This paper explores how this is possible in the light of the alleged causal completeness of physics at the bottom level, and highlights the mechanism that enables strong emergence (the manifest causal effectiveness of application programs) to occur. Although synchronic emergence of higher levels from lower levels is manifestly true, diachronic emergence is generically not the case; indeed we give specific examples where it cannot occur because of the causal effectiveness of higher level variables.


Emergence Downward causation Digital computers Logical control Transistors 



We thank Steven Simon and Oxford University Press for permission to reproduce Figs. 2 and 3 from [25]. This project was completed while both authors were visiting the Quantum Research Group at the University of KwaZulu Natal (UKZN), and we thank Francesco Petruccione for his hospitality at UKZN and support from his research grant number 64812: National Research Foundation (South African Research Chair). We thank an anonymous referee for very helpful comments on a previous version of this paper.


  1. 1.
    Anderson, P.W.: More is different. Science 177, 393–396 (1972)ADSCrossRefGoogle Scholar
  2. 2.
    Tanenbaum, A.S.: Structured Computer Organisation, 5th edn. Prentice Hall, Englewood Cliffs (2006)Google Scholar
  3. 3.
    Leggett, A.J.: On the nature of research in condensed-state physics. Found. Phys. 22, 221–233 (1992)ADSMathSciNetCrossRefGoogle Scholar
  4. 4.
    Hohwy, J., Kallestrup, J. (eds.): Being Reduced. Oxford University Press, Oxford (2008)Google Scholar
  5. 5.
    Humphreys, P.: Emergence: A Philosophical Account. Oxford University Press, Oxford (2016)CrossRefGoogle Scholar
  6. 6.
    Gibb, S., Hendry, R.F., Lancaster, T. (eds.): The Routledge Handbook of Emergence. Routledge, Abingdon (2019)Google Scholar
  7. 7.
    Ellis, G.F.R., Noble, D., O’Connor, T.: Downward causation: an integrating theme within and across the sciences? Interface Focus 2, 19 (2011)Google Scholar
  8. 8.
    Ellis, G.: How can Physics Underlie the Mind: Downward Causation in the Human Context. Berlin, Springer (2016)CrossRefGoogle Scholar
  9. 9.
    Noble, D.: A theory of biological relativity: no privileged level of causation. Interface Focus 2, 55–64 (2011)CrossRefGoogle Scholar
  10. 10.
    MacCormick, J.: Nine Algorithms that Changed the Future: The Ingenious Ideas that Drive Today’s Computers. Princeton University Press, Princeton (2011)CrossRefGoogle Scholar
  11. 11.
    Menzies, P.: Counterfactual theories of causation. In: Zalta, E.N. (eds.) The Stanford Encyclopedia of Philosophy. (2017)
  12. 12.
    Simon, H.A.: The Sciences of the Artificial. MIT Press, Cambridge (1996)Google Scholar
  13. 13.
    Mellisinos, A.C.: Principles of Modern Technology. Cambridge University Press, Cambridge (1990)CrossRefGoogle Scholar
  14. 14.
    Ellis, G., Kopel, J.: The dynamical emergence of biology from physics: branching causation via biomolecules. Front. Physiol. (2019).
  15. 15.
    Blachowicz, J.: The constraint interpretation of physical emergence. J. Gen. Philos. Sci. 44, 21–40 (2013)CrossRefGoogle Scholar
  16. 16.
    Booch, G.: Object Oriented Analysis and Design with Application. Pearson Education India, New Delhi (2006)zbMATHGoogle Scholar
  17. 17.
    Auletta, G., Ellis, G.F., Jaeger, L.: Top-down causation by information control: from a philosophical problem to a scientific research programme. J. R. Soc. Interface 5, 1159–1172 (2008)CrossRefGoogle Scholar
  18. 18.
    Anthony, L.M.: Multiple realisation: keeping it real. In: Hohwy, J., Kallestrup, J. (eds.) Being Reduced, pp. 164–175. Oxford University Press, Oxford (2008)CrossRefGoogle Scholar
  19. 19.
    Bickle, J.: Multiple realizability. In: Zalta, E.N. (ed.) The Stanford Encyclopedia of Philosophy. (2019)
  20. 20.
    Lindholm, T., Yellin, F., Bracha, G., Buckley, A.: The Java Virtual Machine Specification. Pearson Education, London (2014)Google Scholar
  21. 21.
    Lafore, R.: Data Structures and Algorithms in Java. SAMS, Indianapolis (2002)Google Scholar
  22. 22.
    Ross Ashby, W.: Design for a Brain. Chapman and Hall, London (1952)Google Scholar
  23. 23.
    Aho, A.V., Lam, M.S., Sethi, R., Ullman, J.D.: Compilers, Principles, Techniques, and Tools. Addison Wesley, Boston (2006)zbMATHGoogle Scholar
  24. 24.
    Knuth, D.E.: The Art of Computer Programming, Vol. 1: Fundamental Algorithms. Addison-Wesley, Reading (1973)zbMATHGoogle Scholar
  25. 25.
    Simon, S.H.: The Oxford Solid State Basics. Oxford University Press, Oxford (2013)zbMATHGoogle Scholar
  26. 26.
    Phillips, P.: Advanced Solid State Physics. Cambridge University Press, Cambridge (2012)CrossRefGoogle Scholar
  27. 27.
    Schwabl, F.: Quantum Mechanics. Springer, Berlin (2007)zbMATHGoogle Scholar
  28. 28.
    Solyom, J.: fundamentals of the Physics of Solids Volume II: Electronic Properties. Springer, Berlin (2009)CrossRefGoogle Scholar
  29. 29.
    Primas, H.: Emergence in exact natural science. Acta Polytech. Scand. 91, 83–98 (1998)MathSciNetGoogle Scholar
  30. 30.
    Chibbaro, S., Rondoni, L., Vulpiani, A.: Reductionism, Emergence, and Levels of Reality. Springer, Berlin (2014)CrossRefGoogle Scholar
  31. 31.
    Drossel, B., Ellis, G.: Contextual wavefunction collapse: an integrated theory of quantum measurement. New J. Phys. 20, 113025 (2018)ADSCrossRefGoogle Scholar
  32. 32.
    Abelson, H., Sussman, J.S.: Structure and Interpretation of Computer Programs. MIT Press, Cambridge (1990)zbMATHGoogle Scholar
  33. 33.
    Bogacz, R.: A tutorial on the free-energy framework for modelling perception and learning. J. Math. Psychol. 76, 198–211 (2017)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge, MA (1996)zbMATHGoogle Scholar
  35. 35.
    Ghirardi, G.: Sneaking a Look at God’s Cards: Unraveling the Mysteries of Quantum Mechanics. Princeton University Press, Princeton (2007)zbMATHGoogle Scholar
  36. 36.
    O’Gorman, T.J., et al.: Field testing for cosmic ray soft errors in semiconductor memories. IBM J. Res. Dev. 40, 41–50 (1996)CrossRefGoogle Scholar
  37. 37.
    Robb, D., Heil, J.: Mental causation. In: Zalta, E.N. (ed.) The Stanford Encyclopedia of Philosophy (Summer 2019 Edition).
  38. 38.
    Ellis, G.F.R.: Physics, complexity and causality. Nature 435, 743 (2005)ADSCrossRefGoogle Scholar
  39. 39.
    Watson, J.D.: Molecular Biology of the Gene. Pearson Education India, New Delhi (2004)Google Scholar
  40. 40.
    Berridge, M.: Cell Signalling Biology. Portland Press, London (2014)Google Scholar
  41. 41.
    Karplus, M.: Development of multiscale models for complex chemical systems: from H+ H2 to biomolecules. Angew. Chem. Int. Ed. 53, 9992–10005 (2014)CrossRefGoogle Scholar
  42. 42.
    Thompson, C.: Coders: Who They are, What They Think, and How They are Changing the World. Picador, London (2019)Google Scholar
  43. 43.
    Bissell, T.: ZUCKED: Waking Up to the Facebook Catastrophe. Penguin Random House, New York (2019)Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of MathematicsUniversity of Cape TownCape TownSouth Africa
  2. 2.Institute of Condensed Matter PhysicsTechnische Universität DarmstadtDarmstadtGermany

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