[1]Principia Cybernetica Web

                                   Self-organization

   Self-organization is a [2]process where the organization (constraint, redundancy)
   of a system spontaneously increases, i.e. without this increase being controlled
   by the environment or an encompassing or otherwise external system
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   Self-organization is a basically a process of [3]evolution where the effect of
   the environment is minimal, i.e. where the development of new, complex structures
   takes place primarily in and through the system itself. As argued in the section
   on [4]evolutionary theory, self-organization can be understood on the basis of
   the same variation and natural selection processes as other, environmentally
   driven processes of evolution. Self-organization is normally triggered by
   internal variation processes, which are usually called "fluctuations" or "noise".
   The fact that these processes produce a selective retained ordered configuration
   has been called the "[5]order from noise" principle by [6]Heinz von Foerster, and
   the "order through fluctuations" mechanism by Ilya [externallink.GIF]
   [7]Prigogine. Both are special cases of what I have proposed to call the
   [8]principle of selective The increase in organization can be measured more
   objective as a decrease of [9]statistical entropy (see the [10]Principle of
   Asymmetric Transitions). This is again equivalent to an increase in
   [11]redundancy, [12]information or [13]constraint: after the self-organization
   process there is less ambiguity about which state the system is in. A
   self-organizing system which also decreases its [14]thermodynamical entropy must
   necessarily (because of the [15]second law of thermodynamics) export
   ("dissipate") such entropy to its surroundings, as noted by von Foerster and
   Prigogine. Prigogine called systems which continuously export entropy in order to
   maintain their organization [16]dissipative structures. Self-organization is
   usually associated with more complex, non-linear phenomena, rather than with the
   relatively simple processes of structure maintenance of diffusion. All the
   intricacies (limit cycles, [17]chaos, sensitivity to initial conditions,
   dissipative structuration, ...) associated with non-linearity can simply be
   understood through the interplay of positive and negative [18]feedback cycles:
   some variations tend to reinforce themselves (see [19]Autocatalytic Growth),
   others tend to reduce themselves. Both types of feedback fuel natural selection:
   positive feedback because it increases the number of configurations (up to the
   point where resources become insufficient), negative feedback because it
   stabilizes configurations. Either of them provides the configuration with a
   selective advantage over competing configurations. The interaction between them
   (variations can be reinforced in some directions while being reduced in others)
   may create intricate and unpredictable patterns ([20]chaos), which can develop
   very quickly until they reach a stable configuration ([21]attractor).

   See also:
     * [22]Self-organization and complexity in the natural sciences
     * [23]Links on Complexity, Self-organization and Artificial Life
     * Dictionary: [24]PRINCIPLE OF SELF-ORGANIZATION
     * Dictionary: [25]SELF-ORGANIZATION
     * Dictionary: [26]SELF-ORGANIZING
     * Dictionary: [27]SELF-ORGANIZING SYSTEM

Further Reading:

     * Heylighen F. (2009): [externallink.GIF] [28]Complexity and Self-organization,
       in: Encyclopedia of Library and Information Sciences, eds. M. J. Bates & M.
       N. Maack (CRC Press).
     * Heylighen F. (2001): " [externallink.GIF] [29]The Science of
       Self-organization and Adaptivity", in: L. D. Kiel, (ed.) Knowledge
       Management, Organizational Intelligence and Learning, and Complexity, in: The
       Encyclopedia of Life Support Systems ((EOLSS), (Eolss Publishers, Oxford).
       [http://www.eolss.net]
     * Heylighen F. & C. Gershenson (2003): " [externallink.GIF] [30]The Meaning of
       Self-organization in Computing", IEEE Intelligent Systems 18:4, p. 72-75.
     * Gershenson C. & F. Heylighen (2003). " [externallink.GIF] [31]When Can we
       Call a System Self-organizing?", In Banzhaf, W, T. Christaller, P. Dittrich,
       J. T. Kim, and J. Ziegler (eds.), Advances in Artificial Life, 7th European
       Conference, ECAL 2003, (Springer, LNAI 2801.), p. 606-614

   .
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   [32]Copyright© 2009 Principia Cybernetica - [33]Referencing this page

   Author
   F. [34]Heylighen,

   Date
   Sep 22, 2009 (modified)
   Jan 27, 1997 (created)

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                           [36]Metasystem Transition Theory
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   [40]Magnetization and Bénard Rolls: examples of self-organization

   [41]Entropy and the Laws of Thermodynamics

   [42]Attractors

   [43]Deterministic Chaos

   [44]Kauffman's NK Boolean networks

   [45]Fitness Landscapes

   [46]Order from noise
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                                    [47]Discussion
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References

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  47. http://pespmc1.vub.ac.be/MAKANNOT.html
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