Advertisement

The European Physical Journal Special Topics

, Volume 226, Issue 2, pp 181–195 | Cite as

Nature as a network of morphological infocomputational processes for cognitive agents

  • Gordana Dodig-CrnkovicEmail author
Open Access
Regular Article
Part of the following topical collections:
  1. Information in Physics and Beyond

Abstract

This paper presents a view of nature as a network of infocomputational agents organized in a dynamical hierarchy of levels. It provides a framework for unification of currently disparate understandings of natural, formal, technical, behavioral and social phenomena based on information as a structure, differences in one system that cause the differences in another system, and computation as its dynamics, i.e. physical process of morphological change in the informational structure. We address some of the frequent misunderstandings regarding the natural/morphological computational models and their relationships to physical systems, especially cognitive systems such as living beings. Natural morphological infocomputation as a conceptual framework necessitates generalization of models of computation beyond the traditional Turing machine model presenting symbol manipulation, and requires agent-based concurrent resource-sensitive models of computation in order to be able to cover the whole range of phenomena from physics to cognition. The central role of agency, particularly material vs. cognitive agency is highlighted.

References

  1. 1.
    NASA, Dark Energy, Dark Matter, 2016Google Scholar
  2. 2.
    J.A. Wheeler, Information, Physics, Quantum: The Search for Links, in Complexity, Entropy, and the Physics of Information, ed. W. Zurek (Addison-Wesley, Redwood City, 1990)Google Scholar
  3. 3.
    G. Bateson, Steps to an Ecology of Mind: Collected Essays in Anthropology, Psychiatry, Evolution, and Epistemology, ed. P. Adriaans, J. van Benthem (University of Chicago Press, Amsterdam, 1972)Google Scholar
  4. 4.
    C. Hewitt, What Is Commitment? Physical, Organizational, and Social, in Coordination, Organizations, Institutions, and Norms in Agent Systems II, ed. P. Noriega, J. Vazquez-Salceda, G. Boella, O. Boissier, V. Dign (Springer-Verlag, Berlin, Heidelberg, 2007), p. 293Google Scholar
  5. 5.
    H. von Baeyer, Information: The New Language of Science (Harvard University Press, Cambridge Mass., 2004)Google Scholar
  6. 6.
    S. Lloyd, Programming the Universe: A Quantum Computer Scientist Takes on the Cosmos (Knopf, New York, 2006)Google Scholar
  7. 7.
    C. Seife, Decoding the Universe: How The New Science of Information is Explaining Everything in the Cosmos, from Our Brains to Black Holes (Viking, New York, 2006)Google Scholar
  8. 8.
    V. Vedral, Decoding Reality: The Universe as Quantum Information (Oxford University Press, Oxford, 2010)Google Scholar
  9. 9.
    P. Davies, N.H. Gregersen, Information and the Nature of Reality from Physics to Metaphysics (Cambridge University Press, 2010)Google Scholar
  10. 10.
    M. Burgin, Theory of Information: Fundamentality, Diversity and Unification (World Scientific Pub Co., Singapore, 2010)Google Scholar
  11. 11.
    M. Burgin, Super-Recursive Algorithms (Springer-Verlag, New York, 2005)Google Scholar
  12. 12.
    C. Hewitt, P. Bishop, P. Steiger, A Universal Modular ACTOR Formalism for Artificial Intelligence, in IJCAI – Proceedings of the 3rd International Joint Conference on Artificial Intelligence, ed. N.J. Nilsson (William Kaufmann, Standford, 1973), p. 235Google Scholar
  13. 13.
    C. Hewitt, What is computation? Actor Model versus Turing's Model, in A Computable Universe, Understanding Computation & Exploring Nature As Computation, ed. H. Zenil (Imperial College Press, 2012)Google Scholar
  14. 14.
    R. Landauer, Information is Physical, Phys. Today 44, 23 (1991)ADSCrossRefGoogle Scholar
  15. 15.
    R. Pfeifer, F. Iida, Morphological computation: Connecting body, brain and environment, Japanese Sci. Mon. 58, 48 (2005)Google Scholar
  16. 16.
    R. Pfeifer, G. Gomez, Morphological computation – connecting brain, body, and environment, in Creating Brain-like Intelligence: From Basic Principles to Complex Intelligent Systems, ed. K.B. Sendhoff, O. Sporns, E. Körner, H. Ritter, K. Doya (Springer, Berlin, 2009), p. 66Google Scholar
  17. 17.
    G. Dodig-Crnkovic, The info-computational nature of morphological computing, in Philosophy and Theory of Artificial Intelligence Volume 5, ed. V.C. Müller (Springer, Berlin, 2013), p. 59Google Scholar
  18. 18.
    G. Dodig-Crnkovic, Information, Computation, Cognition. Agency-Based Hierarchies of Levels, Fundamental Issues of Artificial Intelligence (Springer International Publishing, Switzerland, 2016), Vol. 376, pp. 141–159Google Scholar
  19. 19.
    J. von Uexküll, A Stroll through the Worlds of Animals and Men, in Instinctive Behavior ed. C. Schiller (International Universities Press, New York, 1957), p. 5Google Scholar
  20. 20.
    S. Palmquist, Kant's System of Perspectives (University Press of America, Lanham, 1993)Google Scholar
  21. 21.
    N. Block, Consciousness, Accessibility and the mesh between psychology and neuroscience, Behav. Brain Sci. 30, 481 (2007)Google Scholar
  22. 22.
    R. Pfeifer, J. Bongard, How the Body Shapes the Way We Think – A New View of Intelligence (MIT Press, 2006)Google Scholar
  23. 23.
    E. Ben-Jacob, Social behavior of bacteria: from physics to complex organization, Eur. Phys. J. B 65, 315 (2008)ADSCrossRefGoogle Scholar
  24. 24.
    E. Ben-Jacob, Bacterial Self-Organization: Co-Enhancement of Complexification and Adaptability in a Dynamic Environment, Phil. Trans. R. Soc. Lond. A 361, 1283 (2003)ADSMathSciNetCrossRefGoogle Scholar
  25. 25.
    S. Schauder, B.L. Bassler, The languages of bacteria, Genes. Dev. 15, 1468 (2001)CrossRefGoogle Scholar
  26. 26.
    M. Minsky, The Society of Mind (Simon and Schuster, New York, 1986)Google Scholar
  27. 27.
    O. Rössler, Endophysics: The World as an Interface (World Scientific, Singapore, New Jersey, London, Hong Kong, 1998)Google Scholar
  28. 28.
    P. Goyal, Information Physics – Towards a New Conception of Physical Reality, Information 3, 567 (2012)Google Scholar
  29. 29.
    C. Fields, If physics is an information science, what is an observer? Information 3, 92 (2012)Google Scholar
  30. 30.
    I. Prigogine, I. Stengers, Order out of Chaos: Man's new dialogue with nature (Flamingo, 1984)Google Scholar
  31. 31.
    I. Prigogine, From Being to Becoming: Time and Complexity in the Physical Sciences (W.H. Freeman, San Francisco, CA, 1980)Google Scholar
  32. 32.
    H. Maturana, F. Varela, Autopoiesis and cognition: The realization of the living (D. Reidel Pub. Co., Dordrecht, Holland, 1980)Google Scholar
  33. 33.
    J. Stewart, Cognition = life: Implications for higher-level cognition, Behav. Process. 35, 311 (1996)CrossRefGoogle Scholar
  34. 34.
    G. Dodig-Crnkovic, Significance of Models of Computation, from Turing Model to Natural Computation, Minds Mach. 21, 301 (2011)CrossRefGoogle Scholar
  35. 35.
    G. Dodig-Crnkovic, Info-Computational Philosophy Of Nature: An Informational Universe With Computational Dynamics, in Festschrift for Søren Brier ed. C. P. and T.T. Sørensen Bent (CBS University Press, 2011), p. 97Google Scholar
  36. 36.
    G. Dodig-Crnkovic, Modeling Life as Cognitive Info-Computation, in Computability in Europe 2014. LNCS, ed. A. Beckmann, E. Csuhaj-Varjú, K. Meer (Springer, Berlin, Heidelberg, 2014), p. 153Google Scholar
  37. 37.
    G. Dodig-Crnkovic, Why we need info-computational constructivism, Constr. Found. 9, 246 (2014)Google Scholar
  38. 38.
    G. Dodig-Crnkovic, Info-computationalism and morphological computing of informational structure, in Integral Biomathics, ed. P.L. Simeonov, L.S. Smith, A.C. Ehresmann (Springer, Berlin, Heidelberg, 2012), p. 97Google Scholar
  39. 39.
    G. Rozenberg, T. Bäck, J.N. Kok, Handbook of Natural Computing (Springer, Berlin, Heidelberg, 2012)Google Scholar
  40. 40.
    P. Denning, Computing is a natural science, Commun. ACM 50, 13 (2007)CrossRefGoogle Scholar
  41. 41.
    G. Dodig-Crnkovic, V. Müller, A Dialogue Concerning Two World Systems: Info-Computational vs. Mechanistic, in Information and Computation, ed. G. Dodig Crnkovic, M. Burgin (World Scientific Pub. Co. Inc., Singapore, 2011), p. 149Google Scholar
  42. 42.
    M. Burgin, G. Dodig-Crnkovic, A Taxonomy of Computation and Information Architecture, in Proceedings of the 2015 European Conference on Software Architecture Workshops (ECSAW ’15), ed. M. Galster (ACM Press, New York, 2015)Google Scholar
  43. 43.
    V. Vedral, Information and Physics, Information 3, 219 (2012)CrossRefGoogle Scholar
  44. 44.
    H. Zenil, Information Theory and Computational Thermodynamics: Lessons for Biology from Physics, Information 3, 739 (2012)CrossRefGoogle Scholar
  45. 45.
    K. Wharton, Quantum States as Ordinary Information, Information 5, 190 (2014)CrossRefGoogle Scholar
  46. 46.
    E. Wigner, The Unreasonable Effectiveness of Mathematics in the Natural Sciences, Commun., Pure Appl. Math. 13 (1960)Google Scholar
  47. 47.
    G. Dodig-Crnkovic, Information and Energy/Matter, Information 3, 751 (2012)CrossRefGoogle Scholar
  48. 48.
    J. Ladyman, D. Ross, D. Spurrett, J. Collier, Everything Must Go: Metaphysics Naturalised (Clarendon Press, Oxford, 2007)Google Scholar
  49. 49.
    D. Deutsch, Quantum theory, the Church-Turing Principle and the universal quantum computer, Proc. R. Soc. Lond. A 400, 97 (1985)ADSMathSciNetCrossRefzbMATHGoogle Scholar
  50. 50.
    D. Deutsch, C. Marletto, Constructor Theory of Information, Proc. R. Soc. A 471, 1 (2015)MathSciNetGoogle Scholar
  51. 51.
    G. Dodig-Crnkovic, Physical Computation as Dynamics of Form that Glues Everything Together, Information 3, 204 (2012)CrossRefGoogle Scholar
  52. 52.
    H. Zenil, A Computable Universe. Understanding Computation & Exploring Nature as Computation, ed. H. Zenil (World Scientific Publishing Company/Imperial College Press, Singapore, 2012)Google Scholar
  53. 53.
    A. Clark, D.J. Chalmers, The Extended Mind, Analysis 58, 7 (1998)CrossRefGoogle Scholar
  54. 54.
    G. Dodig-Crnkovic, R. Giovagnoli, Computing Nature (Springer, Heidelberg, 2013)Google Scholar
  55. 55.
    A. Kurakin, The self-organizing fractal theory as a universal discovery method: The phenomenon of life, Theor. Biol. Med. Model. 8, 1 (2011)CrossRefGoogle Scholar
  56. 56.
    G. Piccinini, Computation in Physical Systems, Stanford Encycl. Philos. (2012)Google Scholar
  57. 57.
    J.A. Wheeler, It from Bit, in At Home in the Universe (Am. Inst. Phys., 1994), p. 295Google Scholar
  58. 58.
    R.P. Feynman, Simulating Physics with Computers, Int. J. Theor. Phys. 21, 467 (1982)MathSciNetCrossRefGoogle Scholar
  59. 59.
    E. Fredkin, Finite Nature, in XXVIIth Rencontre de Moriond (1992)Google Scholar
  60. 60.
    S. Wolfram, A New Kind of Science (Wolfram Media, 2002)Google Scholar
  61. 61.
    G. Chiribella, G.M. D'Ariano, P. Perinotti, Quantum Theory, Namely the Pure and Reversible Theory of Information, Entropy 14, 1877 (2012)ADSMathSciNetCrossRefzbMATHGoogle Scholar
  62. 62.
    G. Dodig-Crnkovic, R. Giovagnoli, Natural/Unconventional Computing and Its Philosophical Significance, Entropy 14, 2408 (2012)ADSMathSciNetCrossRefzbMATHGoogle Scholar
  63. 63.
    H. Zenil, G. Dodig-Crnkovic, Special Issue “Physics of Information”, Information 5 (2014)Google Scholar
  64. 64.
    M. Yoshitake, Y. Saruwatari, Extensional Information Articulation from the Universe, Information 3, 644 (2012)CrossRefGoogle Scholar
  65. 65.
    K. Matsuno, S. Salthe, Chemical Affinity as Material Agency for Naturalizing Contextual Meaning, Information 3, 21 (2011)CrossRefGoogle Scholar
  66. 66.
    S.L. Kim, C.D. Fiorillo, Describing realistic states of knowledge with exact probabilities, in AIP Conference Proceedings 1757. 060008 (2016)Google Scholar
  67. 67.
    W.A. Phillips, Self-Organized Complexity and Coherent Infomax from the Viewpoint of Jaynes's Probability Theory, Information 3, 1 (2012)CrossRefGoogle Scholar
  68. 68.
    C.D. Fiorillo, Beyond Bayes: On the Need for a Unified and Jaynesian Definition of Probability and Information within Neuroscience, Information 3, 175 (2012)CrossRefGoogle Scholar
  69. 69.
    K. Zuse, Calculating Space. Translation of “Rechnender Raum” (MIT Technical Translation, 1970)Google Scholar
  70. 70.
    G. Chaitin, Epistemology as Information Theory: From Leibniz to Ω, in Computation, Information, Cognition – The Nexus and The Liminal, ed. G. Dodig Crnkovic (Cambridge Scholars Pub., Newcastle, UK, 2007), p. 2Google Scholar
  71. 71.
    E. Fredkin, Digital Mechanics: An Information Process Based on Reversible Universal Cellular Automata, Physica D 45, 254 (1990)ADSMathSciNetCrossRefzbMATHGoogle Scholar
  72. 72.
    G. Dodig-Crnkovic, Significance of Models of Computation from Turing Model to Natural Computation, Minds Mach. 21, 301 (2011)CrossRefGoogle Scholar
  73. 73.
    G. Rozenberg, L. Kari, The many facets of natural computing, Commun. ACM 51, 72 (2008)Google Scholar
  74. 74.
    R. Pfeifer, F. Iida, G. Gomez, Morphological Computation for Adaptive Behavior and Cognition, Int. Congr. Ser. 1291, 22 (2006)CrossRefGoogle Scholar
  75. 75.
    G. Dodig-Crnkovic, Investigations into Information Semantics and Ethics of Computing (Mälardalen University Press, Västerås, Sweden, 2006)Google Scholar
  76. 76.
    G. Dodig-Crnkovic, Where Do New Ideas Come From? How Do They Emerge? Epistemology as Computation (Information Processing), ed. C. Calude (World Scientific, 2007), p. 1Google Scholar
  77. 77.
    S.B. Cooper, B. Löwe, A. Sorbi, New Computational Paradigms. Changing Conceptions of What is Computable, Springer Mathematics of Computing Series, XIII, ed. S.B. Cooper, B. Löwe, A. Sorbi (Springer, 2008)Google Scholar
  78. 78.
    A. Sloman, Beyond Turing Equivalence, in Machines and Thought: The Legacy of Alan Turing (vol I), ed. A. Clark, P.J.R. Millican, (OUP, The Clarendon Press, 1996), p. 179Google Scholar
  79. 79.
    J. Collier, Hierarchical dynamical information system with a focus on biology, Entropy 5, 102 (2003)ADSCrossRefGoogle Scholar
  80. 80.
    S. Abramsky, Information, Processes and Games, in Philosophy of Information, ed. J. van Benthem, P. Adriaans (North Holland, Amsterdam, The Netherlands, 2008), p. 483Google Scholar
  81. 81.
    F. Heylighen, Evolutionary Transitions: How do levels of complexity emerge? Complexity 6, 53 (2000)Google Scholar

Copyright information

© The Author(s) 2017

Open Access This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0), which permits use, duplication, adaptation, distribution, and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Chalmers University of Technology412 96 GothenburgSweden

Personalised recommendations