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Guided Self-Organization: Inception pp 53–66Cite as

Generating Functionals for Guided Self-Organization

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Part of the book series: Emergence, Complexity and Computation ((ECC,volume 9))

Abstract

One may take it as a running joke, that complex systems are complex since they are complex. It is however important to realize, this being said, that complex systems come in a large varieties, and in many complexity classes, ranging from relatively simple to extraordinary complex. One may distinguish in this context between classical and modern complex system theory. In the classical approach one would typically study a standardized model, like the Lorentz model or the logistic map, being described usually by maximally a handful of variables and parameters (Gros 2008). Many real-world systems are however characterized by a very large number of variables and control parameters, especially when it comes to biological and cognitive systems. It has been noted, in this context, that scientific progress may generically be dealing with complexity barriers of various severities, in far reaching areas like medicine and meteorology (Gros 2012b), when researching real-world natural or biological complex systems.

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Authors and Affiliations

  1. Institute for Theoretical Physics, Goethe University Frankfurt, Frankfurt am Main, Germany

    Claudius Gros

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  1. Claudius Gros
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Correspondence to Claudius Gros .

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  1. CSIRO ICT Centre, Epping, Australia

    Mikhail Prokopenko

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Gros, C. (2014). Generating Functionals for Guided Self-Organization. In: Prokopenko, M. (eds) Guided Self-Organization: Inception. Emergence, Complexity and Computation, vol 9. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53734-9_3

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  • DOI: https://doi.org/10.1007/978-3-642-53734-9_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-53733-2

  • Online ISBN: 978-3-642-53734-9

  • eBook Packages: EngineeringEngineering (R0)

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