Digital Computer Systems

  • George EllisEmail author
Part of the The Frontiers Collection book series (FRONTCOLL)


This chapter considers issues of emergence and causation in the case of digital computers, as a warm-up example before giving a general viewpoint on these topics in the next chapter. It will be shown that top-down causation is central to their functioning.


Virtual Machine Digital Computer Adaptive Selection Register Machine Possibility Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    H. Abelson, G.J. Sussman, J. Sussman, Structure and Interpretation of Computer Programs (MIT Press, Cambridge, 1996)Google Scholar
  2. 2.
    U. Alon, An Introduction to Systems Biology: Design Principles of Biological Circuits (Chapman and Hall/CRC, London, 2007)zbMATHGoogle Scholar
  3. 3.
    A.W. Appel, Modern Compiler Implementation in Java (Cambridge University Press, Cambridge, 2002)CrossRefzbMATHGoogle Scholar
  4. 4.
    W. Ross Ashby, An Introduction to Cybernetics (Chapman and Hall, London, 1957).
  5. 5.
    G. Auletta, G.F.R. Ellis, L. Jaeger, Top-down causation: From a philosophical problem to a scientific research program. J. R. Soc. Interface 5, 1159–1172 (2008). arXiv:0710.4235 CrossRefGoogle Scholar
  6. 6.
    A.V. Aho, M.S. Lam, R. Sethi, J.D. Ullman, Compilers: Principles, Techniques, and Tools Paperback (Pearson, 2013)Google Scholar
  7. 7.
    S. Beer, Brain of the Firm (Wiley, Chichester, 1981)Google Scholar
  8. 8.
    C.H. Bennett, Notes on Landauer’s principle, reversible computation and Maxwell’s demon. Stud. History Philos. Modern Phys. 34, 501–510 (2003)CrossRefzbMATHGoogle Scholar
  9. 9.
    S. Bennett, S. McRobb, R. Farmer, Object-Oriented Systems Analysis and Design (McGraw Hill, Maidenhead, 2010)Google Scholar
  10. 10.
    A. Bérut, A. Arakelyan, A. Petrosyan, S. Ciliberto, R. Dillenschneider, E. Lutz, Experimental verification of Landauer’s principle linking information and thermodynamics. Nature 483, 187–190 (2012)ADSCrossRefGoogle Scholar
  11. 11.
    C.M. Bishop, Neural Networks for Pattern Recognition (Oxford University Press, Oxford, 1999)zbMATHGoogle Scholar
  12. 12.
    G. Booch, Object-Oriented Analysis and Design with Applications (Addison Wesley, New York, 1994)zbMATHGoogle Scholar
  13. 13.
    G. Booch, J. Rumbaugh, I. Jacobson, The Unified Modeling Language User Guide (Addison Wesley, New York, 1998)Google Scholar
  14. 14.
    V. Brattka, Computability Theory (University of Cape Town Notes, 2011)Google Scholar
  15. 15.
    V. Brilhante, Computer modelling hierarchy: the model reflects the hierarchy of the system being modelled. J. Braz. Comp. Soc. 11(2), Campinas (2005)Google Scholar
  16. 16.
    G. Buzsáki, Rhythms of the Brain (Oxford University Press, Oxford, 2006)CrossRefzbMATHGoogle Scholar
  17. 17.
    J.-P. Changeux, A. Connes, Conversations on Mind, Matter, and Mathematics (Princeton University Press, Princeton, 1998)Google Scholar
  18. 18.
    G.J. Chaitin, Computers, paradoxes and the foundations of mathematics. Am. Sci. 90, 164–171 (2002)CrossRefGoogle Scholar
  19. 19.
    P. Churchland, Plato’s camera: how the physical brain captures a landscape of Abstract Universals (Cambridge) (The MIT Press, Cambridge, 2012)Google Scholar
  20. 20.
    B.J. Copeland, The Church–Turing Thesis. The Stanford Encyclopedia of Philosophy, (Fall 2008 edition), ed. by E.N. Zalta (2002).
  21. 21.
    J. Copeland, The Essential Turing (Oxford University Press, Oxford, 2004)zbMATHGoogle Scholar
  22. 22.
    T. Deacon, The Symbolic Species: The Co-Evolution of Language and the Human Brain (Penguin, London, 1997)Google Scholar
  23. 23.
    K.A. De Jong, Evolutionary Computation: A Unified Approach (MIT Press, Cambridge, 2006)zbMATHGoogle Scholar
  24. 24.
    G. Dyson, Darwin Among the Machines (Penguin, London, 1997)Google Scholar
  25. 25.
    G.F.R. Ellis, True complexity and its associated ontology, in Science and Ultimate Reality: Quantum Theory, Cosmology and Complexity, ed. by J.D. Barrow, P.C.W. Davies, C.L. Harper (Cambridge University Press, Cambridge, 2004), pp. 607–636Google Scholar
  26. 26.
    G.F.R. Ellis, On the nature of causation in complex systems. Trans. R. Soc. S. Africa 63, 69–84 (2008)CrossRefGoogle Scholar
  27. 27.
    G.F.R. Ellis, Top-down causation and emergence: some comments on mechanisms. J. R. Soc. Interface Focus 2, 126–140 (2012)CrossRefGoogle Scholar
  28. 28.
    G.F.R. Ellis, D. Noble, T. O’Connor (eds.), Top-down causation: An integrating theme within and across the sciences? R. Interface Focus Spec. Issue 2, 1–140 (2012)Google Scholar
  29. 29.
    K. Fatahalian, T.J. Knight, M. Houston, M. Erez, D.R. Horn, L. Leem, J.Y. Park, M. Ren, A. Aiken, W.J. Dally, P. Hanrahan, Sequoia: Programming the memory hierarchy, in SC 2006 Conference, Proceedings of the ACM/IEEE (2006)Google Scholar
  30. 30.
    R.L. Flood, E.R. Carson, Dealing with Complexity: An Introduction to the Theory and Application of Systems Science (Plenum Press, London, 1990)zbMATHGoogle Scholar
  31. 31.
    E. Gamma, R. Helm, R. Johnson, J. Vlissides, Design Patterns: Elements of Reusable Object Oriented Software (Addison Wesley, New York, 1995)Google Scholar
  32. 32.
    P. Gray, Psychology (Worth Publishers, New York, 2011)Google Scholar
  33. 33.
    J. Hawkins, On Intelligence (Holt Paperbacks, New York, 2004)Google Scholar
  34. 34.
    D. Hofstadter, Godel, Escher, Bach: An Eternal Golden Braid (Penguin, London, 1980)zbMATHGoogle Scholar
  35. 35.
    D. Hilbert, On the infinite, in Philosophy of Mathematics, ed. by P. Benacerraf, H. Putnam (Prentice Hall, Englewood Cliff, 1964), p. 134Google Scholar
  36. 36.
    J.H. Holland, Adaptation in Natural and Artificial Systems (MIT Press, Cambridge, 1992)Google Scholar
  37. 37.
    B. Jacobs, S.W. Ng, D.T. Wang, Memory Systems: Cache, DRAM, Disk (Elsevier, Burlington, 2008)Google Scholar
  38. 38.
    N.L. Kamorova, M.A. Nowak, Language, learning, and evolution, in Language Evolution, ed. by M.H Christensen, S. Kirby (Oxford University Press, Oxford, 2005), pp. 317–337Google Scholar
  39. 39.
    R.M. Keller, Computer Science: Abstraction to Implementation.
  40. 40.
    J.F. Kurose, K.W. Ross, Computer Networking: A Top-Down approach (Addison-Wesley, New York, 2012)Google Scholar
  41. 41.
    J. Ladyman, S. Presnell, A.J. Short, B. Groisman, The connection between logical and thermodynamic irreversibility (2006).
  42. 42.
    R. Lafore, Data Structures and Algorithms in Java (SAMS, Indianapolis, 2002)Google Scholar
  43. 43.
    R. Landauer, Irreversibility and heat generation in the computing process. IBM J. Res. Dev. 5, 183–191 (1961)MathSciNetCrossRefzbMATHGoogle Scholar
  44. 44.
    S. Lloyd, Computational capacity of the universe (2001). arXiv:quant-phy/0110141
  45. 45.
    J. MacCormack, Nine Algorithms that Changed the Future: The Ingenious Ideas that Drive Today’s Computers (Princeton University Press, Princeton, 2012)Google Scholar
  46. 46.
    J.L. McClelland, The case for interactionism in language processing. Technical Report AIP-2 (Department of Psychology, Carnegie-Mellon University Pittsburgh, PA 15213 USA, 1987)Google Scholar
  47. 47.
    M.M. Mano, C.R. Kime, Logic and Computer Design Fundamentals (Pearson/Prentice Hall, 2008)Google Scholar
  48. 48.
    J. McCarthy, Artificial intelligence, logic and formalizing common sense (1990).
  49. 49.
    J.H. Miller, S.E. Page, Complex Adaptive Systems: An Introduction to Computational Models of Social Life (Princeton University Press, Princeton, 2007)Google Scholar
  50. 50.
    D. Noble, A theory of biological relativity: no privileged level of causation. Interface Focus 2, 55–64 (2012)CrossRefGoogle Scholar
  51. 51.
    Object Management Group (OMG), OMG Unified Modeling Language (OMG UML) Superstructure Version 2.2.
  52. 52.
    R. Penrose, The Emperor’s New Mind: Concerning Computers, Minds and the Laws of Physics (Oxford University Press, New York, 1989)zbMATHGoogle Scholar
  53. 53.
    R. Penrose, Shadows of the Mind: A Search for the Missing Science of Consciousness (Oxford University Press, Oxford, 1994)zbMATHGoogle Scholar
  54. 54.
    R. Penrose, The Large, the Small and the Human Mind (Cambridge University Press, Cambridge, 1997)zbMATHGoogle Scholar
  55. 55.
    J. Porway, Q.C. Wang, S.C. Zhu, A hierarchical and contextual model for aerial image parsing.
  56. 56.
    S Russell and P Norvig (2009) Artificial Intelligence: A Modern Approach (Prentice Hall)Google Scholar
  57. 57.
    W. Savitch, Absolute Java (Pearson, Boston, 2010)Google Scholar
  58. 58.
    S.C. Shapiro, Artifical intelligence, in Encyclopaedia of Artificial Intelligence, ed. by S.C. Shapiro (Wiley, New York, 1992), pp. 54–57Google Scholar
  59. 59.
    J.R. Searle, Is the brain a digital computer?
  60. 60.
    A. Silberschatz, P.B. Galvin, G. Gagne, Operating System Concepts (Wiley, New York, 2010)Google Scholar
  61. 61.
    H.A. Simon, The Sciences of the Artificial (MIT Press, Cambridge, 1992)Google Scholar
  62. 62.
    A.S. Tanenbaum, Structured Computer Organisation (Prentice Hall, Englewood Cliffs, 2006)Google Scholar
  63. 63.
    R.L. Trask, Language and Linguistics: The Key Concepts (Routledge, Abingdon, 2007)Google Scholar
  64. 64.
    A.M. Turing, On computable numbers, with an application to the Entscheidungsproblem, in Proceedings of the London Mathematical Society, vol. 42, pp. 230–265 (1936) (Reprinted in J. Copeland, The Essential Turing (Oxford University Press, Oxford, 2004), p. 58)Google Scholar
  65. 65.
    A.M. Turing, Lecture on the automatic computing engine (1947) (Reprinted in J. Copeland, The Essential Turing (Oxford University Press, Oxford, 2004), p. 378)Google Scholar
  66. 66.
    A.M. Turing, Computing machinery and intelligence. Mind 59, 433–460 (1950) (Reprinted in J. Copeland, The Essential Turing (Oxford University Press, Oxford, 2004), p. 433)Google Scholar
  67. 67.
    D.A. Watt, D.F. Brown, Programming Language Processors in Java: Compilers and Interpreters (Prentice Hall, Harlow, 2000)Google Scholar
  68. 68.
    M.A. Weiss, Data Structure and Algorithm Analysis in Java (Addison Wesley/Longman, 1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Mathematics and Applied MathematicsUniversity of Cape TownRondeboschSouth Africa

Personalised recommendations