Advertisement

Humanity Is Much More than the Sum of Humans

  • Tommaso BolognesiEmail author
Chapter
Part of the The Frontiers Collection book series (FRONTCOLL)

Abstract

Consider two roughly spherical and coextensive complex systems: the atmosphere and the upper component of the biosphere—humanity. It is well known that, due to a malicious antipodal butterfly, the possibility to accurately forecast the weather is severely limited. Why should it be easier to predict and steer the future of humanity? Here we present various viewpoints on the issue. On a long time scale, we sketch a software-oriented view at the cosmos in all of its components, from spacetime to the biosphere and human societies, borrowing ideas from Wolfram, Chaitin and Tononi; this is also motivated by an attempt to provide some formal foundations to Teilhard de Chardin’s cosmological/metaphysical visions. On a shorter scale, we discuss the possibility of using formal models, agent based software systems, and big data from social computing, for simulating humanity in-silico, in order to anticipate problems and test solutions.

Keywords

Cellular Automaton Cellular Automaton Physical Universe Spacetime Diagram Human Phenomenon 
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.

References

  1. 1.
    Balduzzi, D., Tononi, G.: Integrated information in discrete dynamical systems: Motivation and theoretical framework. PLOS Comput. Biol. 4(6), e1000091 (2008)Google Scholar
  2. 2.
    Bolognesi, T.: Algorithmic causets. In Space, Time, Matter - current issues in quantum mechanics and beyond—Proceedings of DICE 2010. IOP, J. Phys.—Conf. Ser. (2011)Google Scholar
  3. 3.
    Bolognesi, T.: Causal sets from simple models of computation. Int. J. Unconv. Comput. (IJUC), 7, 2011Google Scholar
  4. 4.
    Bombelli, L., Lee, J., Meyer, D., Sorkin, R.D.: Space-time as a causal set. Phys. Rev. Lett. 59(5), 521–524 (1987)Google Scholar
  5. 5.
    Bonabeau, E.: Agent-based modeling: Methods and techniques for simulating human systems. Proc. Nat. Acad. Sci. 99(3), 7280–7287 (2002)Google Scholar
  6. 6.
    Chaitin, G.: Proving Darwin—Making Biology Mathematical. Pantheon Books, New York (2012)Google Scholar
  7. 7.
    de Chardin, T.: Le Phénomène Humain. Ed. du Seuil, Paris (1955)Google Scholar
  8. 8.
    Epstein, J.M., Axtell, R.L.: Growing Artificial Societies: Social Science from the Bottom Up. MIT Press, Cambridge (1996)Google Scholar
  9. 9.
    Kauffman, S.A.: At Home in the Universe: The Search for Laws of Self-organization and Complexity. Oxford University Press, Oxford paperbacks (1995)Google Scholar
  10. 10.
    Li, X., Mao, W., Zeng, D., Wang, F.-Y.: Agent-based social simulation and modeling in social computing. In: Yang, C.C., et al. (eds.) Intelligence and Security Informatics. Lecture Notes in Computer Science, vol. 5075, pp. 401–412. Springer, Berlin Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Lloyd, S.: Universe as quantum computer. Complexity 3(1), 32–35 (1997)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Schelling, T.: Dynamic models of segregation. J. Math. Sociol. 1, 143–186 (1971)CrossRefGoogle Scholar
  13. 13.
    Wolfram, S.: A New Kind of Science. Wolfram Media, Inc. (2002)Google Scholar
  14. 14.
    Zenil, H., Villarreal-Zapata, E.: Sensitivity to peturbation in elementary cellular automata. The Wolfram Demonstrations Project. http://demonstrations.wolfram.com/SensitivityToPeturbationInElementaryCellularAutomata/

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.CNR-ISTIPisaItaly

Personalised recommendations