Foundations of Science

, Volume 22, Issue 4, pp 863–881 | Cite as

Emergence, Computation and the Freedom Degree Loss Information Principle in Complex Systems

Article

Abstract

We consider processes of emergence within the conceptual framework of the Information Loss principle and the concepts of (1) systems conserving information; (2) systems compressing information; and (3) systems amplifying information. We deal with the supposed incompatibility between emergence and computability tout-court. We distinguish between computational emergence, when computation acquires properties, and emergent computation, when computation emerges as a property. The focus is on emergence processes occurring within computational processes. Violations of Turing-computability such as non-explicitness and incompleteness are intended to represent partially the properties of phenomenological emergence, such as logical openness, given by the observer’s cognitive role; structural dynamics where change regards rules rather than only values; and multi-modelling where multiple non-equivalent models are required to model such structural dynamics. In this way, we validate, from an epistemological viewpoint, models and simulations of phenomenological emergence where the sequence of events constitutes the natural, analogical non-Turing computation which a cognitive complex system can reproduce through learning. Reproducibility through learning is different from Turing-like computational iteration. This paper aims to open a new, non-reductionist understanding of the conceptual relationship between emergence and computability.

Keywords

Computational Emergence Information Iteration Uniqueness Violations 

References

  1. Acosta, D., Fernandez de Cordoba, P., Isidro, J. M., & Santander, J. L. G. (2012). An entropic picture of emergent quantum mechanics. International Journal of Geometric Methods in Modern Physics, 9(5), 1250048–1250053.CrossRefGoogle Scholar
  2. Altman, R. B., Dunker, A. K., & Hunter, L. (2014). Biocomputing 2014: Proceedings of the pacific symposium. Singapore: World Scientific.Google Scholar
  3. Anderson, P. W. (1972). More is different: Broken symmetry and the nature of the hierarchical structure of sciences. Science, 177(4047), 393–396.CrossRefGoogle Scholar
  4. Anderson, N. G., & Bhanja, S. (2014). Field-coupled nanocomputing: Paradigms, progress, and perspectives (lecture notes in computer science/theoretical computer science and general issues). New York: Springer.Google Scholar
  5. Aoki, I. (1982). A simulation study on the schooling mechanism in fish. Bulletin of the Japanese Society of Scientific Fisheries, 48, 1081–1088.CrossRefGoogle Scholar
  6. Ballarini, M., Cabibbo, N., Candelier, R., Cavagna, A., Cisbani, E., Giardina, I., et al. (2008). Interaction ruling animal collective behaviour depends on topological rather than metric distance: Evidence from a field study. Proceedings of the National Academy of Science, 105(4), 1232–1237.CrossRefGoogle Scholar
  7. Barabási, A. L. (2011). Bursts: The hidden patterns behind everything we do, from your e-mail to bloody crusades. London: Plume.Google Scholar
  8. Batterman, R. W. (2011). Emergence, singularities, and symmetry breaking. Foundations of Physics, 41(6), 1031–1050.CrossRefGoogle Scholar
  9. Bedau, M. (2011). Weak emergence and computer simulation. In P. Humphreys & C. Imbert (Eds.), Models, simulations, and representations (pp. 91–114). New York: Routledge.Google Scholar
  10. Bell, J. S. (1987). Speakable and unspeakable in quantum mechanics (pp. 52–62). Cambridge: Cambridge University Press.Google Scholar
  11. Bianconi, G., & Barabási, A. (2001). Bose–Einstein condensation in complex networks. Physical Review Letters, 86(24), 5632–5635.CrossRefGoogle Scholar
  12. Blasone, M., Jizba, P., & Vitiello, G. (2001). Dissipation and quantization. Physics Letters A, 287(3), 205–210.CrossRefGoogle Scholar
  13. Brunner, K. A. (2002). What’s emergent in emergent computing? In R. Trappl (Ed.), Cybernetics and systems 2002: Proceedings of the 16th European meeting on cybernetics and systems research (pp. 189–192). Vienna: Austrian Society for Cybernetics Study.Google Scholar
  14. Buchanan, M. (2000). Ubiquity. London: Wiedenfield & Nicholson.Google Scholar
  15. Burks, A. W. (Ed.). (1970). Essays on cellular automata. Urbana (IL): Illinois University Press.Google Scholar
  16. Butterfield, J. (2011). Emergence, reduction and supervenience: A varied landscape. Foundations of Physics, 41(6), 920–959.CrossRefGoogle Scholar
  17. Cavagna, A., Cimarelli, A., Giardina, I., Parisi, G., Santagati, R., Stefanini, F., et al. (2010). Scale-free correlations in starling flocks. Proceeding of the National Academy of Sciences of the United States of America, 107(26), 11865–11870.CrossRefGoogle Scholar
  18. Chalmers, D. J. (2006). Strong and weak emergence. In P. Davies & P. Clayton (Eds.), The re-emergence of emergence (pp. 244–256). Oxford: Oxford University Press.Google Scholar
  19. Claude, C., & Longo, G. (2016). The deluge of spurious correlations in big data, Found. of Sc., First online http://www.di.ens.fr/users/longo/files/BigData-Calude-LongoAug21.pdf. 07 Mar.
  20. Crutchfield, J. P. (1994). The calculi of emergence: Computation, dynamics and induction. Physica D, 75, 11–54.CrossRefGoogle Scholar
  21. Crutchfield, J. P. (1999). Is anything ever new? Considering emergence. In G. A. Cowan, D. Pines, & D. Meltzer (Eds.), Complexity: Metaphors, models, and reality (pp. 515–537). Cambridge (MA): Perseus Books.Google Scholar
  22. De Finetti, B. (2008). Philosophical lectures on probability english translation of B. de Finetti’s: Filosofia della probabilitá, synthese library. Vol. 340, New York: Springer.Google Scholar
  23. Erl, T., Puttini, R., & Mahmood, Z. (2013). Cloud computing: Concepts, technology and architecture. New York: Prentice Hall.Google Scholar
  24. Faloutsos, C., & Megalooikonomoum, V. (2007). On data mining, compression, and Kolmogorov complexity. Data Mining and Knowledge Discovery, 15(1), 3–20.CrossRefGoogle Scholar
  25. Fokkink, W. (2014). Distributed algorithms: An intuitive approach. Cambridge (MA): MIT Press.Google Scholar
  26. Forrest, S. (1990). Emergent computation. Cambridge (MA): MIT Press.Google Scholar
  27. Gardner, M. (1970). Mathematical games—The fantastic combinations of John Conway’s new solitaire game “life”. Scientific American, 223, 120–123.CrossRefGoogle Scholar
  28. Goldstein, J. (1999). Emergence as a construct: History and issues. Emergence, 1(1), 49–72.CrossRefGoogle Scholar
  29. Gorban, A. N., Smirnova, E. V., & Tyukina, T. A. (2009). General laws of adaptation to environmental factors: From ecological stress to financial crisis. Mathematical Modelling of Natural Phenomena, 4(6), 1–53.CrossRefGoogle Scholar
  30. Gorban, A. N., Smirnova, E. V., & Tyukina, T. A. (2010). Correlations, risk and crisis: From physiology to finance. Physica A, 389(16), 3193–3217.CrossRefGoogle Scholar
  31. Haken, H. (1987). Synergetics: An approach to self-organization. In F. E. Yates (Ed.), Self-organizing systems: The emergence of order (pp. 417–434). New York: Plenum.CrossRefGoogle Scholar
  32. Haken, H. (1988). Information and self-organization. A macroscopic approach to complex systems. Berlin: Springer.CrossRefGoogle Scholar
  33. Hoekstra, A. G., Kroc, J., & Sloot, P. M. A. (2010). Simulating complex systems by cellular automata. Berlin: Springer.Google Scholar
  34. Hosni, H., Fedel, M., & Montagna, F. (2011). A logical characterization of coherence for imprecise probabilities. International Journal of Approximate Reasoning, 52(8), 1147–1170.CrossRefGoogle Scholar
  35. Ishii, H., & Morishita, S. (2010). A learning algorithm for the simulation of pedestrian flow by cellular automata. In S. Bandini & S. Manzoni (Eds.), Cellular automata, lecture notes in computer science (pp. 465–473). Berlin: Springer.Google Scholar
  36. Kitto, K. (2014). A contextualised general systems theory. Systems, 2(4), 541–565.CrossRefGoogle Scholar
  37. Korotkikh, V. (2014). A mathematical structure for emergent computation. Dordrecht: Springer.Google Scholar
  38. Kroger, B. (2014). Hermann Haken: From the laser to synergetics: A scientific biography of the early years. New York: Springer.Google Scholar
  39. Langton, C. G. (1990). Computation at the edge of chaos: Phase transitions and emergent computation. In S. Forrest (Ed.), Emergent computation. Amsterdam: North-Holland.Google Scholar
  40. Laughlin, R. B., Pines, D., Schmalian, J., Stojkovic, B. P., & Wolynes, P. (2000). The middle way. Proceedings of the National Academy of Sciences, 97(1), 32–37. http://www.pnas.org/content/97/1/32.full.pdf.
  41. Li, M., & Vitányi, P. M. B. (2009). An introduction to Kolmogorov complexity and its applications. New York: Springer.Google Scholar
  42. Licata, I. (2006). General system theory, link-quantum semantics and fuzzy sets. In G. Minati, E. Pessa, & M. Abram (Eds.), Systemics of emergence: Research and development (pp. 723–734). New York: Springer.CrossRefGoogle Scholar
  43. Licata, I. (2008a). La logica aperta della mente. Torino: Codice Edizioni.Google Scholar
  44. Licata, I. (2008b). Emergence and computation to the edge of classical and quantum systems. In I. Licata & A. Sakaji (Eds.), Physics of emergence and organization (pp. 1–25). Singapore: World Scientific.CrossRefGoogle Scholar
  45. Licata, I. (2010). Living with radical uncertainty: The exemplary case of folding protein. In I. Licata & A. Sakaji (Eds.), Crossing in complexity. Interdisciplinary application of physics in biological and social systems (pp. 1–10). New York, NY: Nova Publishers.Google Scholar
  46. Licata, I., & Minati, G. (2010). Creativity as cognitive design—The case of mesoscopic variables in meta-structures. In Alessandra M. Corrigan (Ed.), Creativity: Fostering, measuring and contexts (pp. 95–107). New York: Nova Publishers.Google Scholar
  47. Liu, X. F., & Sun, C. P. (2001). Consequences of ‘t Hooft’s equivalence class theory and symmetry by large coarse graining. Journal of Mathematical Physics, 42(8), 3665–3672.CrossRefGoogle Scholar
  48. MacLennan, B. J. (2004). Natural computation and non-Turing models of computation. Theoretical Computer Science, 317(1–3), 115–145.CrossRefGoogle Scholar
  49. MacLennan, B. (2012). Molecular coordination of hierarchical self-assembly. Nano Communication Networks, 3(2), 116–128.CrossRefGoogle Scholar
  50. Minati, G., & Licata, I. (2012). Meta-structural properties in collective behaviours. The International Journal of General Systems, 41(3), 289–311.CrossRefGoogle Scholar
  51. Minati, G., & Licata, I. (2013). Emergence as mesoscopic coherence. Systems, 1(4), 50–65.CrossRefGoogle Scholar
  52. Minati, G., & Pessa, E. (2006). Collective beings. New York: Springer.Google Scholar
  53. Minati, G., Penna, M. P., & Pessa, E. (1998). Thermodynamic and logical openness in general systems. Systems Research and Behavioral Science, 15(3), 131–145.CrossRefGoogle Scholar
  54. Minati, G., Licata, I., & Pessa, E. (2013). Meta-structures: The search of coherence in collective behaviours (without physics). In A. Graudenzi, G. Caravagna, G. Mauri, & M. Antoniotti (Eds.) Wivace 2013Proceedings of the Italian workshop on artificial life and evolutionary computation (pp. 35–42). Electronic proceedings in theoretical computer science. http://rvg.web.cse.unsw.edu.au/eptcs/paper.cgi?Wivace2013.6. Accessed Jan 2016.
  55. Nagatani, T. (2012). Four species CA model for facing pedestrian traffic at rush hour. Applied Mathematical Modelling, 36(2), 702–711.CrossRefGoogle Scholar
  56. Pacheco, P. (2011). An introduction to parallel programming. Burlington (MA): Morgan Kaufmann.Google Scholar
  57. Pastor-Satorras, R., & Vespignani, A. (2004). Evolution and structure of the internet: A statistical physics approach. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  58. Pavlov, Y. P., & Andreev, R. D. (2013). Decision control, management, and support in adaptive and complex systems: Quantitative models. Hershey (PA): IGI global.CrossRefGoogle Scholar
  59. Pessa, E. (2008). Phase transitions in biological matter. In I. Licata & A. Sakaji (Eds.), Physics of emergence and organization (pp. 165–228). Singapore: World Scientific.CrossRefGoogle Scholar
  60. Pinto, S. E., Lopes, S. R., & Viana, R. L. (2002). Collective behavior in a chain of van der Pol oscillators with power-law coupling. Physica A, 303(3), 339–356.CrossRefGoogle Scholar
  61. Reynolds, C. W. (1987). Flocks, herds, and schools: A distributed behavioral model. Computer Graphics, 21(4), 25–34.CrossRefGoogle Scholar
  62. Ronald, E. M. A., Sipper, M., & Capcarrère, M. S. (1999). Design, observation, surprise! A test for emergence. Artificial Life, 5(3), 225–239.CrossRefGoogle Scholar
  63. Ryan, A. J. (2006). Emergence is coupled to scope, not level. Complexity, 67(2), 67–77.Google Scholar
  64. Scheffer, M., Carpenter, S. R., Lenton, T. M., Bascompte, J., Brock, W., Dakos, V., et al. (2012). Anticipating critical transitions. Science, 338(6105), 344–348.CrossRefGoogle Scholar
  65. Schmidt, D., Stal, M., Rohnert, H., & Buschmann, F. (2000). Pattern-oriented software architecture volume 2: Patterns for concurrent and networked objects. New York: Wiley.Google Scholar
  66. Sethna, J. P. (2006). Entropy, order parameters and complexity. Oxford: Oxford University Press.Google Scholar
  67. Shafee, F. (2010). Organization and complexity in a nested hierarchical spin-glass like social space. Electronic Journal of Theoretical Physics (EJTP), 7(24), 93–130.Google Scholar
  68. Simon, M. (2005). Emergent computation: Emphasizing bioinformatics. New York: Springer.Google Scholar
  69. Soare, R. I. (2009). Turing oracle machines, online computing, and three displacements in computability theory. Annals of Pure and Applied Logic, 160(3), 368–399.CrossRefGoogle Scholar
  70. Sornette, D. (2006). Critical phenomena in natural sciences: Chaos, fractals, self-organization and disorder: Concepts and tools. Heidelberg: Springer.Google Scholar
  71. Syropoulos, A. (2008). Hypercomputation. Computing beyond the Church–Turing barrier. New York: Springer.CrossRefGoogle Scholar
  72. ‘t Hooft, G. (1993). Dimensional reduction in quantum gravity. In A. Ali, J. Ellis, & S. Randjbar-Daemi (Eds.) Salamfestschrift: A collection of talks. Series in 20th century physics, Vol. 4 (pp. 284–296). Singapore: World Scientific.Google Scholar
  73. ‘t Hooft, G. (2015). The cellular automaton interpretation of quantum mechanics. https://arxiv.org/pdf/1405.1548v3.pdf [quant-ph].
  74. Takagi, T., Moritomi, Y., Iwata, J., Nakamine, H., & Sannomiya, N. (2004). Mathematical model of fish schooling behaviour in a set-net. ICES Journal of Marine Science, 61(7), 1214–1223.CrossRefGoogle Scholar
  75. Toby, O. (2006). Hypercomputation: Computing more than the Turing machine. Applied Mathematics and Computation, 178, 143–153.CrossRefGoogle Scholar
  76. Vicsek, T., & Zafeiris, A. (2012). Collective motion. Physics Reports, 517(3–4), 71–140.CrossRefGoogle Scholar
  77. Vitiello, G. (2001). My double unveiled. Amsterdam: Benjamins.CrossRefGoogle Scholar
  78. Von Foerster, H. (1984). Observing systems. Seaside (CA): Intersystems Publications.Google Scholar
  79. Waldner, J. B. (2010). Nanocomputers and swarm intelligence. Hoboken (NJ): Wiley.Google Scholar
  80. Wolfram, S. (2002). A new kind of science. Champaign (IL): Wolfram Media Inc.Google Scholar
  81. Zhang, W.-B. (1991). The Haken slaving principle and time scale in economic analysis. Springer Series in Synergetics, 53, 193–212.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Institute for Scientific Methodology (ISEM)PalermoItaly
  2. 2.Italian Systems Society (AIRS)MilanItaly

Personalised recommendations