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Measuring Self-Organization via Observers

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Advances in Artificial Life (ECAL 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2801))

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Abstract

We introduce organization information, an information-theoretic characterization for the phenomenon of self-organization. This notion, which requires the specification of an observer, is discussed in the paradigmatic context of the Self-Organizing Map and its behaviour is compared to that of other information-theoretic measures. We show that it is sensitive to the presence and absence of “self-organization” (in the intuitive sense) in cases where conventional measures fail.

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Author information

Authors and Affiliations

  1. Adaptive Systems Research Group, Department of Computer Science, University of Hertfordshire,  

    Daniel Polani

Authors
  1. Daniel Polani
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Memorial University of Newfoundland, Canada

    Wolfgang Banzhaf

  2. Department of Computer Science, University of Dortmund, 11, Joseph-von-Fraunhofer-Str. 20, 44227, Dortmund, Germany

    Jens Ziegler

  3. Fraunhofer IAIS, Sankt Augustin

    Thomas Christaller

  4. Bio Systems Analysis Group, Jena Centre for Bioinformatics and Friedrich Schiller University Jena, Ernst-Abbe-Platz 1–4, D-07743, Jena, Germany

    Peter Dittrich

  5. School of Computing Sciences, University of East Anglia, NR4 7TJ, Norwich, United Kingdom

    Jan T. Kim

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© 2003 Springer-Verlag Berlin Heidelberg

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Polani, D. (2003). Measuring Self-Organization via Observers. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds) Advances in Artificial Life. ECAL 2003. Lecture Notes in Computer Science(), vol 2801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39432-7_72

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  • DOI: https://doi.org/10.1007/978-3-540-39432-7_72

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20057-4

  • Online ISBN: 978-3-540-39432-7

  • eBook Packages: Springer Book Archive

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