complexitystudies

This is a very nice paper, especially well suited for a first introduction:

Heylighen F. (2001): “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)

@INCOLLECTION {Heylighen2001,
title = {The science of self-organization and adaptivity},
author = {Heylighen, Francis},
editor = {Kiel, L. D. },
booktitle = {Knowledge Management, Organizational Intelligence and Learning, and Complexity},
series = {The Encyclopedia of Life Support Systems},
year = {2001},
publisher = {EOLSS Publishers},
address = {Oxford},
}

I would like to let the abstract speak for itself:

The theory of self-organization and adaptivity has grown out of a variety of disciplines,
including thermodynamics, cybernetics and computer modelling. The present article reviews its
most important concepts and principles. It starts with an intuitive overview, illustrated by the
examples of magnetization and B{\’e}nard convection, and concludes with the basics of mathematical
modelling. Self-organization can be defined as the spontaneous creation of a globally coherent
pattern out of local interactions. Because of its distributed character, this organization tends to be
robust, resisting perturbations. The dynamics of a self-organizing system is typically non-linear,
because of circular or feedback relations between the components. Positive feedback leads to an
explosive growth, which ends when all components have been absorbed into the new
configuration, leaving the system in a stable, negative feedback state. Non-linear systems have in
general several stable states, and this number tends to increase (bifurcate) as an increasing input of
energy pushes the system farther from its thermodynamic equilibrium. To adapt to a changing
environment, the system needs a variety of stable states that is large enough to react to all
perturbations but not so large as to make its evolution uncontrollably chaotic. The most adequate
states are selected according to their fitness, either directly by the environment, or by subsystems
that have adapted to the environment at an earlier stage. Formally, the basic mechanism
underlying self-organization is the (often noise-driven) variation which explores different regions in
the system’s state space until it enters an attractor. This precludes further variation outside the
attractor, and thus restricts the freedom of the system’s components to behave independently. This
is equivalent to the increase of coherence, or decrease of statistical entropy, that defines selforganization.

The author’s homepage.

Technorati Tags: complexity, paper of the week, phd

TrackBack Url for this entry:
http://www.complexitystudies.org/2007/02/16/introduction-to-complexity-concepts/trackback/