Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
Book cover

International Workshop on Self-Organizing Systems

IWSOS 2012: Self-Organizing Systems pp 90–95Cite as

  1. Home
  2. Self-Organizing Systems
  3. Conference paper
A Quantitative Measure, Mechanism and Attractor for Self-Organization in Networked Complex Systems

A Quantitative Measure, Mechanism and Attractor for Self-Organization in Networked Complex Systems

  • Georgi Yordanov Georgiev18 
  • Conference paper
  • 938 Accesses

  • 6 Citations

  • 4 Altmetric

Part of the Lecture Notes in Computer Science book series (LNCCN,volume 7166)

Abstract

Quantity of organization in complex networks here is measured as the inverse of the average sum of physical actions of all elements per unit motion multiplied by the Planck’s constant. The meaning of quantity of organization is the number of quanta of action per one unit motion of an element. This definition can be applied to the organization of any complex system. Systems self-organize to decrease the average action per element per unit motion. This lowest action state is the attractor for the continuous self-organization and evolution of a dynamical complex system. Constraints increase this average action and constraint minimization by the elements is a basic mechanism for action minimization. Increase of quantity of elements in a network, leads to faster constraint minimization through grouping, decrease of average action per element and motion and therefore accelerated rate of self-organization. Progressive development, as self-organization, is a process of minimization of action.

Keywords

  • network
  • self-organization
  • complex system
  • organization
  • quantitative measure
  • principle of least action
  • principle of stationary action
  • attractor
  • progressive development
  • acceleration

Download conference paper PDF

References

  1. Georgiev, G., Georgiev, I.: The least action and the metric of an organized system. Open Syst. Inf. Dyn. 9(4), 371 (2002)

    CrossRef  MathSciNet  Google Scholar 

  2. de Maupertuis, P.: Essai de cosmologie (1750)

    Google Scholar 

  3. Goldstein, H.: Classical Mechanics. Addison Wesley (1980)

    Google Scholar 

  4. Feynman, R.: The Principle of Least Action in Quantum Mechanics. Ph.D. thesis (1942)

    Google Scholar 

  5. Gauss, J.: Über ein neues allgemeines Grundgesetz der Mechanik (1831)

    Google Scholar 

  6. Taylor, J.: Hidden unity in nature’s laws. Cambridge University Press (2001)

    Google Scholar 

  7. Kaku, M.: Quantum Field Theory. Oxford University Press (1993)

    Google Scholar 

  8. Hoskins, D.A.: A least action approach to collective behavior. In: Parker, L.E. (ed.) Proc. SPIE, Microrob. and Micromech. Syst., vol. 2593, pp. 108–120 (1995)

    Google Scholar 

  9. Piller, O., Bremond, B., Poulton, M.: Least Action Principles Appropriate to Pressure Driven Models of Pipe Networks. In: ASCE Conf. Proc. 113 (2003)

    Google Scholar 

  10. Willard, L., Miranker, A.: Neural network wave formalism. Adv. in Appl. Math. 37(1), 19–30 (2006)

    CrossRef  MathSciNet  MATH  Google Scholar 

  11. Wang, J., Zhang, K., Wang, E.: Kinetic paths, time scale, and underlying landscapes: A path integral framework to study global natures of nonequilibrium systems and networks. J. Chem. Phys. 133, 125103 (2010)

    CrossRef  Google Scholar 

  12. Annila, A., Salthe, S.: Physical foundations of evolutionary theory. J. Non-Equilib. Thermodyn., 301–321 (2010)

    Google Scholar 

  13. Devezas, T.C.: Evolutionary theory of technological change: State-of-the-art and new approaches. Tech. Forec. & Soc. Change 72, 1137–1152 (2005)

    CrossRef  Google Scholar 

  14. Chaisson, E.J.: The cosmic Evolution. Harvard (2001)

    Google Scholar 

  15. Bar-Yam, Y.: Dynamics of Complex Systems. Addison Wesley (1997)

    Google Scholar 

  16. Smart, J.M.: Answering the Fermi Paradox. J. of Evol. and Tech. (June 2002)

    Google Scholar 

  17. Vidal, C.: Computational and Biological Analogies for Understanding Fine-Tuned Parameters in Physics. Found. of Sci. 15(4), 375–393 (2010)

    CrossRef  MathSciNet  Google Scholar 

  18. Gershenson, C., Heylighen, F.: When Can We Call a System Self-Organizing? In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds.) ECAL 2003. LNCS (LNAI), vol. 2801, pp. 606–614. Springer, Heidelberg (2003)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Natural Sciences – Physics and Astronomy, Assumption College, 500 Salisbury St, Worcester, MA, 01609, United States of America

    Georgi Yordanov Georgiev

Authors
  1. Georgi Yordanov Georgiev
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, P.O. Box 5031, 2600 GA, Delft, The Netherlands

    Fernando A. Kuipers

  2. Department of Telematics, Norwegian University of Science and Technology, O.S. Bragstads plass 2B, 7491, Trondheim, Norway

    Poul E. Heegaard

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 IFIP International Federation for Information Processing

About this paper

Cite this paper

Georgiev, G.Y. (2012). A Quantitative Measure, Mechanism and Attractor for Self-Organization in Networked Complex Systems. In: Kuipers, F.A., Heegaard, P.E. (eds) Self-Organizing Systems. IWSOS 2012. Lecture Notes in Computer Science, vol 7166. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28583-7_9

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-28583-7_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28582-0

  • Online ISBN: 978-3-642-28583-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

167.114.118.210

Not affiliated

Springer Nature

© 2023 Springer Nature