Hello all,
I disagree with this. Selection implies choice. Dynamic,
state-determined systems, have no choice: they just follow precise
state-determined trajectories. Take a cellular automata or a boolean
network, which are examples of computational systems that observe
self-organizing behavior. What is the process of variation and what is
the process of selection here? Each cell/node is computing a
state-determined transition rule based on inputs from other cells/nodes.
Of course out of families of selforganizing systems one can discuss the
existance of possible trajectories, or better, attractor landscapes, and
envision a process of selection of those, which happens in evolutionary
systems. But this is not a process of self-organization alone, more what
can be referred to as selected self-organization. I discussed this in
http://www.c3.lanl.gov/~rocha/ises.html and
http://www.c3.lanl.gov/~rocha/dissert.html and also
http://www.c3.lanl.gov/~rocha/sa.html
In arguing this it might be useful first to define what a
self-organizing system is. From http://www.c3.lanl.gov/~rocha/sa.html, I
see it as:
"Self-organization is seen as the process by which systems of many
components tend to reach a particular state, a set of cycling
states, or a small volume of their state space (attractor basins), with
no external interference. This attractor behavior is often
recognized at a different level of observation as the spontaneous
formation of well organized structures, patterns, or behaviors, from
random initial conditions (emergent behavior). The systems used to study
this behavior computationally are referred to as dynamical
systems or state-determined systems, since their current state depends
only on their previous state. They possess a large number of
components or variables, and thus high-dimensional state spaces."
As for selection of self-organizing dynamics:
"For a dynamic system to observe genuine emergence of new
classifications, that is, to be able to accumulate useful variations, it
must change its structure (that is, its components characteristics
establishing a particular attractor landscape). One way or another,
this structural change leading to efficient classification (not just
random change), has only been achieved through some external
influence on the self-organizing system. Artificial neural networks
discriminate by changing the structure of their connections
through an external learning procedure. Evolutionary strategies rely on
internal random variation which must ultimately be externally
selected. In other words, the self-organizing system must be
structurally coupled [Maturana and Varela, 1987] to some external
system which acts on structural changes of the first and induces some
form of explicit or implicit selection of its dynamic
representations: selected self-organization [Rocha, 1996, 1997, 1998a]."
>
> Since, as every systems theorist knows, what is is system and what is
> environment is decided by the observer, there is no strict separation
> possible between "Darwinian" and "self-organizing" evolution. External
> selection by the environment becomes internal "self-organization" when you
> shift your point of view from an individual organism to an ecosystem.
> Similarly, the "self-organization" of different tissues during
> morphogenetic development becomes adaptation to an external environment of
> others cells when you look at it from the point of view of a single cell.
We can of course change levels of description and try to describe
evolutionay trajectories in an ecosystem as a self-organizing system,
but at that point, you are not studying the selection process, namely
genetic variation and Darwinian evolution, but more general historical
trajectories. It may be possible in some cases to evaluate evolutionary
trajectories as a self-organizing system, once historical data is
available. But in any case, it is a different system which is being
studied; this does not mean that evolution and self-organization are the
same thing depending on how you look at it, as much as studying
biochemistry is not the same as studying evolutionary biology.
Cheers,
Luis
______________________________________________________
Luis Mateus Rocha
Complex Systems Modeling Team
Computer Research and Applications Group (CIC-3)
Los Alamos National Laboratory, MS B265
Los Alamos, NM 87545, USA
T: 505-665-5328
e-mail: rocha@lanl.gov or rocha@santafe.edu
www: http://www.c3.lanl.gov/~rocha