Advertisement

A Mathematical Model of Information Theory: The Superiority of Collective Knowledge and Intelligence

  • Pedro G. GuillénEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10385)

Abstract

The mathematical model described here is an evolution of the one constructed within the studies [1, 2, 3] by De Santos and Villa, among others. This paper aims to formalize the concept of knowledge and its properties as a basis for creating generalized economic value functions [4], focusing on the business models of the current technological sector as the application environment. The main conclusions reached are focused on the improvement of the value of information and knowledge under the assumption of collective cooperation amongst information system agents, as well as the properties of the knowledge space derived from the model.

Keywords

Knowledge Collective Intelligence Wisdom Structure Value Economy Swarm Topology 

References

  1. 1.
    Guillén, P.G.: Topological proof of the computability of the algorithm based on the morphosyntactic distance. In: Conference Series of the 9th Annual International Conference on Computer Science and Information Systems, COM2013-0595 (2013)Google Scholar
  2. 2.
    Serradilla, F., Villa, E., De Santos, A., Guillén, P.G.: Semantic construction of an univocal language. ITHEA: Inf. Theor. Appl. 19(3), 211 (2012)Google Scholar
  3. 3.
    De Santos, A., Villa, E., Serradilla, F., Guillén, P.G.: Construction of morphosyntactic distance on semantic structures. ITHEA: Inf. Theor. Appl. 19(4), 336 (2012)Google Scholar
  4. 4.
    Menger, C.: Principles of Economics. Ludwig von Mises Institute, Auburn (1976). 120 p.Google Scholar
  5. 5.
    Frankland, P.W., Bontempi, B.: The organization of recent and remote memories. Nat. Rev. Neurosci. 6(2), 119–130 (2005)CrossRefGoogle Scholar
  6. 6.
    Rugg, M., Yonelinas, A.P.: Human recognition memory: a cognitive neuroscience perspective. Trends Cogn. Sci. 7(7), 313–319 (2003)CrossRefGoogle Scholar
  7. 7.
    Shannon, C.E., Weaver, W.: The Mathematical Theory of Communication. University of Illinois Press, Urbana (1964). 125 p.zbMATHGoogle Scholar
  8. 8.
    Moreiro González, J.A.: Aplicaciones al análisis automático del contenido provenientes de la teoría matemática de la información. Anales de documentación 5, 273–286 (2002)Google Scholar
  9. 9.
    Chung, F.: Graph theory in the information age. Not. AMS 57(6), 726–732 (2010)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Bourbaki, N.: Integration I. Chap. III–IV. Springer, Heidelberg (2004)Google Scholar
  11. 11.
    Narici, L., Beckenstein, E.: The Hahn-Banach theorem: the life and times. Topol. Appl. 77, 193–211 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Foreman, M., Wehrung, F.: The Hahn-Banach theorem implies the existence of a non-Lebesgue measurable set. Fundamenta Mathematicae 138, 13–19 (1991)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Herrlich, H.: Axiom of Choice. Springer, Heidelberg (2006). 120 p.zbMATHGoogle Scholar
  14. 14.
    Bosyk, G.M.: Más allá de Heisenberg. Relaciones de incerteza tipo Landau-Pollak y tipo entrópicas (2014). 113 p.Google Scholar
  15. 15.
    Hawkins, T.: The Lebesgue’s Theory of Integration. University of Wisconsin Press, Madison (1970)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Knowdle Foundation & Research InstituteMálagaSpain

Personalised recommendations