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Digital Computer Systems

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Book cover How Can Physics Underlie the Mind?

Part of the book series: The Frontiers Collection ((FRONTCOLL))

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Abstract

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.

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Notes

  1. 1.

    For a hardcore reductionist, it is illegitimate to regard these levels as real: they are epiphenomena arising from the underlying physics. This viewpoint provides no useful understanding of the causation in action.

  2. 2.

    The Hotbits random number generator uses this technique: see http://www.fourmilab.ch/hotbits/.

  3. 3.

    I am aware that some present day feedback control systems use principles of adaptive control. I believe they should be labeled as such, to distinguish them from the basic cybernetic processes identified by Wiener, in which the goal is fixed.

  4. 4.

    This is what Penrose identifies as bottom-up organisation [53, p. 18], but this is incorrect, because he fails to recognise the top-down nature of the decision process via higher level selection criteria.

  5. 5.

    I thank Vasco Brattke for these characterisations.

  6. 6.

    I am indebted to Vasco Brattke (private communication) for the following comments.

  7. 7.

    I only consider classical computers here, where quantum uncertainty in the underlying physics has no effect on microcomputer operations because they have been carefully designed so that this will be the case. Quantum computing raises many further possibilities I do not engage with in this text.

References

  1. H. Abelson, G.J. Sussman, J. Sussman, Structure and Interpretation of Computer Programs (MIT Press, Cambridge, 1996)

    Google Scholar 

  2. U. Alon, An Introduction to Systems Biology: Design Principles of Biological Circuits (Chapman and Hall/CRC, London, 2007)

    MATH  Google Scholar 

  3. A.W. Appel, Modern Compiler Implementation in Java (Cambridge University Press, Cambridge, 2002)

    Book  MATH  Google Scholar 

  4. W. Ross Ashby, An Introduction to Cybernetics (Chapman and Hall, London, 1957). http://pcp.lanl.gov/books/IntroCyb.pdf

  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

    Article  Google Scholar 

  6. A.V. Aho, M.S. Lam, R. Sethi, J.D. Ullman, Compilers: Principles, Techniques, and Tools Paperback (Pearson, 2013)

    Google Scholar 

  7. S. Beer, Brain of the Firm (Wiley, Chichester, 1981)

    Google Scholar 

  8. C.H. Bennett, Notes on Landauer’s principle, reversible computation and Maxwell’s demon. Stud. History Philos. Modern Phys. 34, 501–510 (2003)

    Article  MATH  Google Scholar 

  9. S. Bennett, S. McRobb, R. Farmer, Object-Oriented Systems Analysis and Design (McGraw Hill, Maidenhead, 2010)

    Google Scholar 

  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)

    Article  ADS  Google Scholar 

  11. C.M. Bishop, Neural Networks for Pattern Recognition (Oxford University Press, Oxford, 1999)

    MATH  Google Scholar 

  12. G. Booch, Object-Oriented Analysis and Design with Applications (Addison Wesley, New York, 1994)

    MATH  Google Scholar 

  13. G. Booch, J. Rumbaugh, I. Jacobson, The Unified Modeling Language User Guide (Addison Wesley, New York, 1998)

    Google Scholar 

  14. V. Brattka, Computability Theory (University of Cape Town Notes, 2011)

    Google Scholar 

  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. G. Buzsáki, Rhythms of the Brain (Oxford University Press, Oxford, 2006)

    Book  MATH  Google Scholar 

  17. J.-P. Changeux, A. Connes, Conversations on Mind, Matter, and Mathematics (Princeton University Press, Princeton, 1998)

    Google Scholar 

  18. G.J. Chaitin, Computers, paradoxes and the foundations of mathematics. Am. Sci. 90, 164–171 (2002)

    Article  Google Scholar 

  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. B.J. Copeland, The Church–Turing Thesis. The Stanford Encyclopedia of Philosophy, (Fall 2008 edition), ed. by E.N. Zalta (2002). http://plato.stanford.edu/archives/fall2008/entries/church-turing/

  21. J. Copeland, The Essential Turing (Oxford University Press, Oxford, 2004)

    MATH  Google Scholar 

  22. T. Deacon, The Symbolic Species: The Co-Evolution of Language and the Human Brain (Penguin, London, 1997)

    Google Scholar 

  23. K.A. De Jong, Evolutionary Computation: A Unified Approach (MIT Press, Cambridge, 2006)

    MATH  Google Scholar 

  24. G. Dyson, Darwin Among the Machines (Penguin, London, 1997)

    Google Scholar 

  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. 607–636

    Google Scholar 

  26. G.F.R. Ellis, On the nature of causation in complex systems. Trans. R. Soc. S. Africa 63, 69–84 (2008)

    Article  Google Scholar 

  27. G.F.R. Ellis, Top-down causation and emergence: some comments on mechanisms. J. R. Soc. Interface Focus 2, 126–140 (2012)

    Article  Google Scholar 

  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. 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. R.L. Flood, E.R. Carson, Dealing with Complexity: An Introduction to the Theory and Application of Systems Science (Plenum Press, London, 1990)

    MATH  Google Scholar 

  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. P. Gray, Psychology (Worth Publishers, New York, 2011)

    Google Scholar 

  33. J. Hawkins, On Intelligence (Holt Paperbacks, New York, 2004)

    Google Scholar 

  34. D. Hofstadter, Godel, Escher, Bach: An Eternal Golden Braid (Penguin, London, 1980)

    MATH  Google Scholar 

  35. D. Hilbert, On the infinite, in Philosophy of Mathematics, ed. by P. Benacerraf, H. Putnam (Prentice Hall, Englewood Cliff, 1964), p. 134

    Google Scholar 

  36. J.H. Holland, Adaptation in Natural and Artificial Systems (MIT Press, Cambridge, 1992)

    Google Scholar 

  37. B. Jacobs, S.W. Ng, D.T. Wang, Memory Systems: Cache, DRAM, Disk (Elsevier, Burlington, 2008)

    Google Scholar 

  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. 317–337

    Google Scholar 

  39. R.M. Keller, Computer Science: Abstraction to Implementation. http://www.cs.hmc.edu/~keller/cs60book/

  40. J.F. Kurose, K.W. Ross, Computer Networking: A Top-Down approach (Addison-Wesley, New York, 2012)

    Google Scholar 

  41. J. Ladyman, S. Presnell, A.J. Short, B. Groisman, The connection between logical and thermodynamic irreversibility (2006). http://philsci-archive.pitt.edu/id/eprint/2689

  42. R. Lafore, Data Structures and Algorithms in Java (SAMS, Indianapolis, 2002)

    Google Scholar 

  43. R. Landauer, Irreversibility and heat generation in the computing process. IBM J. Res. Dev. 5, 183–191 (1961)

    Article  MathSciNet  MATH  Google Scholar 

  44. S. Lloyd, Computational capacity of the universe (2001). arXiv:quant-phy/0110141

  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. 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. M.M. Mano, C.R. Kime, Logic and Computer Design Fundamentals (Pearson/Prentice Hall, 2008)

    Google Scholar 

  48. J. McCarthy, Artificial intelligence, logic and formalizing common sense (1990). http://www-formal.stanford.edu/jmc/

  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. D. Noble, A theory of biological relativity: no privileged level of causation. Interface Focus 2, 55–64 (2012)

    Article  Google Scholar 

  51. Object Management Group (OMG), OMG Unified Modeling Language (OMG UML) Superstructure Version 2.2. http://www.omg.org/spec/UML/2.4.1/

  52. R. Penrose, The Emperor’s New Mind: Concerning Computers, Minds and the Laws of Physics (Oxford University Press, New York, 1989)

    MATH  Google Scholar 

  53. R. Penrose, Shadows of the Mind: A Search for the Missing Science of Consciousness (Oxford University Press, Oxford, 1994)

    MATH  Google Scholar 

  54. R. Penrose, The Large, the Small and the Human Mind (Cambridge University Press, Cambridge, 1997)

    MATH  Google Scholar 

  55. J. Porway, Q.C. Wang, S.C. Zhu, A hierarchical and contextual model for aerial image parsing. http://vcla.stat.ucla.edu/Aerial_Image_Parsing/index.html

  56. S Russell and P Norvig (2009) Artificial Intelligence: A Modern Approach (Prentice Hall)

    Google Scholar 

  57. W. Savitch, Absolute Java (Pearson, Boston, 2010)

    Google Scholar 

  58. S.C. Shapiro, Artifical intelligence, in Encyclopaedia of Artificial Intelligence, ed. by S.C. Shapiro (Wiley, New York, 1992), pp. 54–57

    Google Scholar 

  59. J.R. Searle, Is the brain a digital computer? https://mywebspace.wisc.edu/lshapiro/web/Phil554_files/SEARLE-BDC.HTM

  60. A. Silberschatz, P.B. Galvin, G. Gagne, Operating System Concepts (Wiley, New York, 2010)

    Google Scholar 

  61. H.A. Simon, The Sciences of the Artificial (MIT Press, Cambridge, 1992)

    Google Scholar 

  62. A.S. Tanenbaum, Structured Computer Organisation (Prentice Hall, Englewood Cliffs, 2006)

    Google Scholar 

  63. R.L. Trask, Language and Linguistics: The Key Concepts (Routledge, Abingdon, 2007)

    Google Scholar 

  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. 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. 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. D.A. Watt, D.F. Brown, Programming Language Processors in Java: Compilers and Interpreters (Prentice Hall, Harlow, 2000)

    Google Scholar 

  68. M.A. Weiss, Data Structure and Algorithm Analysis in Java (Addison Wesley/Longman, 1999)

    Google Scholar 

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Correspondence to George Ellis .

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Ellis, G. (2016). Digital Computer Systems. In: How Can Physics Underlie the Mind?. The Frontiers Collection. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49809-5_2

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  • DOI: https://doi.org/10.1007/978-3-662-49809-5_2

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