Advertisement

Information, Computation, Cognition. Agency-Based Hierarchies of Levels

  • Gordana Dodig-CrnkovicEmail author
Chapter
Part of the Synthese Library book series (SYLI, volume 376)

Abstract

This paper connects information with computation and cognition via concept of agents that appear at variety of levels of organization of physical/chemical/cognitive systems – from elementary particles to atoms, molecules, life-like chemical systems, to cognitive systems starting with living cells, up to organisms and ecologies. In order to obtain this generalized framework, concepts of information, computation and cognition are generalized. In this framework, nature can be seen as informational structure with computational dynamics, where an (info-computational) agent is needed for the potential information of the world to actualize. Starting from the definition of information as the difference in one physical system that makes a difference in another physical system – which combines Bateson and Hewitt’s definitions, the argument is advanced for natural computation as a computational model of the dynamics of the physical world, where information processing is constantly going on, on a variety of levels of organization. This setting helps us to elucidate the relationships between computation, information, agency and cognition, within the common conceptual framework, with special relevance for biology and robotics.

Keywords

Information Computation Cognition Natural computation Morphological computing Morphogenesis Embodied computation 

References

  1. Allo, P. (2008). Formalising the “no information without data-representation” principle. In A. Briggle, K. Waelbers, & P. A. E. Brey (Eds.), Proceedings of the 2008 conference on current issues in computing and philosophy (pp. 79–90). Amsterdam: Ios Press.Google Scholar
  2. Bateson, G. (1972). In P. Adriaans & J. Benthem van (Eds.), Steps to an ecology of mind: Collected essays in anthropology, psychiatry, evolution, and epistemology (pp. 448–466). Amsterdam: University Of Chicago Press.Google Scholar
  3. Ben-Jacob, E. (2008). Social behavior of bacteria: From physics to complex organization. The European Physical Journal B, 65(3), 315–322.CrossRefGoogle Scholar
  4. Ben-Jacob, E. (2009). Bacterial complexity: More is different on all levels. In S. Nakanishi, R. Kageyama, & D. Watanabe (Eds.), Systems biology – The challenge of complexity (pp. 25–35). Tokyo/Berlin/Heidelberg/New York: Springer.Google Scholar
  5. Ben-Jacob, E., Shapira, Y., & Tauber, A. I. (2011). Smart bacteria. In L. Margulis, C. A. Asikainen, & W. E. Krumbein (Eds.), Chimera and consciousness. Evolution of the sensory self. Cambridge/Boston: MIT Press.Google Scholar
  6. Ben-Naim, A. (2008). A farewell to entropy: Statistical thermodynamics based on information. Singapore/London/Hong Kong: World Scientific.CrossRefGoogle Scholar
  7. Bonsignorio, F. (2013). Quantifying the evolutionary self-structuring of embodied cognitive networks. Artificial Life, 19(2), 267–289.CrossRefGoogle Scholar
  8. Burgin, M. (2010). Theory of information: Fundamentality, diversity and unification (pp. 1–400). Singapore: World Scientific Pub Co.Google Scholar
  9. Burgin, M., & Dodig-Crnkovic, G. (2011). Information and computation – Omnipresent and pervasive. In Information and computation (pp. vii–xxxii). New York/London/Singapore: World Scientific Pub Co Inc.Google Scholar
  10. Burgin, M., & Dodig-Crnkovic, G. (2013). Typologies of computation and computational models. Arxiv.org, arXiv:1312.Google Scholar
  11. Cantwell Smith, B. (1998). On the origin of objects. Cambridge, MA: MIT Press.Google Scholar
  12. Chaitin, G. (2007). Epistemology as information theory: From Leibniz to Ω. In G. Dodig Crnkovic (Ed.), Computation, information, cognition – The nexus and the liminal (pp. 2–17). Newcastle: Cambridge Scholars Pub.Google Scholar
  13. Chiribella, G., D’Ariano, G. M., & Perinotti, P. (2012). Quantum theory, namely the pure and reversible theory of information. Entropy, 14, 1877–1893.CrossRefGoogle Scholar
  14. Deacon, T. (2011). Incomplete nature. How mind emerged from matter. New York/London: W. W. Norton & Company.Google Scholar
  15. Denning, P. (2007). Computing is a natural science. Communications of the ACM, 50(7), 13–18.CrossRefGoogle Scholar
  16. Dodig-Crnkovic, G. (2006). Investigations into information semantics and ethics of computing (pp. 1–33). Västerås: Mälardalen University Press.Google Scholar
  17. Dodig-Crnkovic, G. (2008). Knowledge generation as natural computation. Journal of Systemics, Cybernetics and Informatics, 6(2), 12–16.Google Scholar
  18. Dodig-Crnkovic, G. (2010). In J. Vallverdú (Ed.), Biological information and natural computation. Hershey: Information Science Reference.Google Scholar
  19. Dodig-Crnkovic, G. (2012a). Info-computationalism and morphological computing of informational structure. In P. L. Simeonov, L. S. Smith, & A. C. Ehresmann (Eds.), Integral biomathics. Tracing the road to reality. Berlin/Heidelberg: Springer.Google Scholar
  20. Dodig-Crnkovic, G. (2012b). Information and energy/matter. Information, 3(4), 751–755.CrossRefGoogle Scholar
  21. Dodig-Crnkovic, G. (2012c). Physical computation as dynamics of form that glues everything together. Information, 3(2), 204–218.CrossRefGoogle Scholar
  22. Dodig-Crnkovic, G. (2012d). The info-computational nature of morphological computing. In V. C. Müller (Ed.), Theory and philosophy of artificial intelligence (SAPERE, pp. 59–68). Berlin: Springer.Google Scholar
  23. Dodig-Crnkovic, G. (2014a). Info-computational constructivism and cognition. Constructivist Foundations, 9(2), 223–231.Google Scholar
  24. Dodig-Crnkovic, G. (2014b). Modeling life as cognitive info-computation. In A. Beckmann, E. Csuhaj-Varjú, & K. Meer (Eds.), Computability in Europe 2014 (LNCS, pp. 153–162). Berlin/Heidelberg: Springer.Google Scholar
  25. Dodig-Crnkovic, G., & Giovagnoli, R. (2013). Computing nature. Berlin/Heidelberg: Springer.CrossRefGoogle Scholar
  26. Dodig-Crnkovic, G., & Hofkirchner, W. (2011). Floridi’s open problems in philosophy of information, ten years after. Information, 2(2), 327–359.CrossRefGoogle Scholar
  27. Dodig-Crnkovic, G., & Müller, V. (2011). A dialogue concerning two world systems: Info-computational vs. mechanistic. In G. Dodig Crnkovic & M. Burgin (Eds.), Information and computation (pp. 149–184). Singapore/Hackensack: World Scientific.CrossRefGoogle Scholar
  28. Fisher, J., & Henzinger, T. A. (2007). Executable cell biology. Nature Biotechnology, 25(11), 1239–1249.CrossRefGoogle Scholar
  29. Fredkin, E. (1992). Finite nature. Proceedings of the XXVIIth Rencotre de Moriond, Les Arcs, Savoie, France.Google Scholar
  30. Goyal, P. (2012). Information physics – Towards a new conception of physical reality. Information, 3, 567–594.CrossRefGoogle Scholar
  31. Hawkins, J., & Blakeslee, S. (2005). On intelligence. New York: Times Books, Henry Holt and Co.Google Scholar
  32. Hewitt, C. (2007). What is commitment? Physical, organizational, and social. In P. Noriega, J. Vazquez-Salceda, G. Boella, O. Boissier, & V. Dign (Eds.), Coordination, organizations, institutions, and norms in agent systems II (pp. 293–307). Berlin/Heidelberg: Springer.CrossRefGoogle Scholar
  33. Hewitt, C. (2010). Actor model for discretionary, adaptive concurrency. CoRR, abs/1008.1. Retrieved from http://arxiv.org/abs/1008.1459
  34. Hewitt, C. (2012). What is computation? Actor model versus Turing’s model. In H. Zeni (Ed.), A computable universe, understanding computation & exploring nature as computation. Singapore: World Scientific Publishing Company/Imperial College Press.Google Scholar
  35. Hewitt, C., Bishop, P., & Steiger, P. (1973). A universal modular ACTOR formalism for artificial intelligence. In N. J. Nilsson (Ed.), IJCAI – Proceedings of the 3rd International Joint Conference on Artificial Intelligence (pp. 235–245). Standford: William Kaufmann.Google Scholar
  36. Hinton, G. (2006). To recognize shapes, first learn to generate images, UTML TR 2006–004.Google Scholar
  37. Hinton, G., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18, 1527–1554.CrossRefGoogle Scholar
  38. Kampis, G. (1991). Self-modifying systems in biology and cognitive science: A new framework for dynamics, information, and complexity (pp. 1–564). Amsterdam: Pergamon Press.CrossRefGoogle Scholar
  39. Kauffman, S. (1993). Origins of order: Self-organization and selection in evolution. New York: Oxford University Press.Google Scholar
  40. Kauffman, S. (1995). At home in the universe: The search for laws of self-organization and complexity. New York: Oxford University Press.Google Scholar
  41. Kauffman, S. (2000). Investigations. New York/London: Oxford University Press.Google Scholar
  42. Kauffman, S., Logan, R., Este, R., Goebel, R., Hobill, D., & Shmulevich, I. (2008). Propagating organization: An enquiry. Biology and Philosophy, 23(1), 27–45.CrossRefGoogle Scholar
  43. Landauer, R. (1991). Information is physical. Physics Today, 44, 23–29.CrossRefGoogle Scholar
  44. Lloyd, S. (2006). Programming the universe: A quantum computer scientist takes on the cosmos. New York: Knopf.Google Scholar
  45. Lungarella, M., & Sporns, O. (2005). Information self-structuring: Key principle for learning and development. In Proceedings of 2005 4th IEEE Int. Conference on Development and Learning (pp. 25–30).Google Scholar
  46. MacLennan, B. J. (2010). Morphogenesis as a model for nano communication. Nano Communication Networks, 1(3), 199–208.CrossRefGoogle Scholar
  47. MacLennan, B. J. (2011). Artificial morphogenesis as an example of embodied computation. International Journal of Unconventional Computing, 7(1–2), 3–23.Google Scholar
  48. Maldonado, C. E., & Gómez Cruz, A. N. (2014). Biological hypercomputation: A new research problem in complexity theory. Complexity, wileyonline (1099–0526). doi: 10.1002/cplx.21535.Google Scholar
  49. Matsuno, K., & Salthe, S. (2011). Chemical affinity as material agency for naturalizing contextual meaning. Information, 3(1), 21–35.Google Scholar
  50. Maturana, H., & Varela, F. (1980). Autopoiesis and cognition: The realization of the living. Dordrecht/Holland: D. Reidel Pub. Co.CrossRefGoogle Scholar
  51. Maturana, H., & Varela, F. (1992). The tree of knowledge. Boston: Shambala.Google Scholar
  52. Nunes de Castro, L., Silveira Xavier, R., Pasti, R., Dourado Maia, R., Szabo, A., & Ferrari, D. G. (2011). The grand challenges in natural computing research: The quest for a new science. International Journal of Natural Computing Research (IJNCR), 2(4), 17–30.CrossRefGoogle Scholar
  53. Pfeifer, R., & Bongard, J. (2006). How the body shapes the way we think – A new view of intelligence. Cambridge, MA: MIT Press.Google Scholar
  54. Pfeifer, R., Lungarella, M., & Iida, F. (2007). Self-organization, embodiment, and biologically inspired robotics. Science, 318, 1088–1093.CrossRefGoogle Scholar
  55. Pombo, O., Torres, J. M., & Symons J, R. S. (Eds.). (2012). Special sciences and the unity of science (Logic, Epi.). Berlin/Heidelberg: Springer.Google Scholar
  56. Rössler, O. (1998). Endophysics: The world as an interface. Singapore/London/Hong Kong: World Scientific.CrossRefGoogle Scholar
  57. Rozenberg, G., Bäck, T., & Kok, J. N. (Eds.). (2012). Handbook of natural computing. Berlin/Heidelberg: Springer.Google Scholar
  58. Salthe, S. (2012a). Hierarchical structures. Axiomathes, 22(3), 355–383.CrossRefGoogle Scholar
  59. Salthe, S. (2012b). Information and the regulation of a lower hierarchical level by a higher one. Information, 3, 595–600.CrossRefGoogle Scholar
  60. Shapiro, J. A. (2011). Evolution: A view from the 21st century. New Jersey: FT Press Science.Google Scholar
  61. Sloman, A. (2013a). Meta-morphogenesis. Retrieved from http://www.cs.bham.ac.uk/research/projects/cogaff/misc/meta-morphogenesis.html
  62. Sloman, A. (2013b). Meta-morphogenesis: Evolution and development of information-processing machinery. In S. B. Cooper & J. van Leeuwen (Eds.), Alan Turing: His work and impact (p. 849). Amsterdam: Elsevier.Google Scholar
  63. Smolensky, P. (1986). Information processing in dynamical systems: Foundations of harmony theory. In D. E. Rumelhart, J. L. McClelland, & PDP Research Group (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition (pp. 194–281). Cambridge, MA: MIT Press.Google Scholar
  64. Stepney, S. (2008). The neglected pillar of material computation. Physica D: Nonlinear Phenomena, 237(9), 1157–1164.CrossRefGoogle Scholar
  65. Turing, A. M. (1952). The chemical basis of morphogenesis. Philosophical Transactions of the Royal Society of London, 237(641), 37–72.CrossRefGoogle Scholar
  66. Ulanowicz, R. E. (2009). A third window: Natural life beyond Newton and Darwin. West Conshohocken: Templeton Foundation Press.Google Scholar
  67. Valiant, L. (2013). Probably approximately correct: Nature’s algorithms for learning and prospering in a complex world. New York: Basic Books.Google Scholar
  68. van Benthem, J., & Adriaans, P. (2008). Philosophy of information. Amsterdam: North Holland.Google Scholar
  69. Vedral, V. (2010). Decoding reality: The universe as quantum information (pp. 1–240). Oxford: Oxford University Press.Google Scholar
  70. von Baeyer, H. (2004). Information: The new language of science. Cambridge, MA: Harvard University Press.Google Scholar
  71. Wheeler, J. A. (1990). Information, physics, quantum: The search for links. In W. Zurek (Ed.), Complexity, entropy, and the physics of information. Redwood City: Addison-Wesley.Google Scholar
  72. Wolfram, S. (2002). A new kind of science. Wolfram Media. Retrieved from http://www.wolframscience.com/
  73. Xavier, R. S., Omar, N., & de Castro, L. N. (2011). Bacterial colony: Information processing and computational behavior. In Nature and biologically inspired computing (NaBIC), 2011 Third World Congress on, pp. 439–443, 19–21 Oct 2011. doi:  10.1109/NaBIC.2011.6089627. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6089627&isnumber=6089255
  74. Zeilinger, A. (2005). The message of the quantum. Nature, 438(7069), 743.CrossRefGoogle Scholar
  75. Zuse, K. (1970). Calculating space. Translation of “Rechnender Raum”. Cambridge, MA: MIT Technical Translation.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Chalmers University of Technology & University of GothenburgGothenburgSweden
  2. 2.Mälardalen UniversityVästeråsSweden

Personalised recommendations