MIT Encyclopedia of the Cognitive Sciences - Cryptome

738 Self-Organizing Systems
issue is whether life itself came into existence through a
self-organizing process. Those who have used computers to
explore the mathematically self-ordering properties of complex
adaptive systems have studied this subject under the
rubric of ARTIFICIAL LIFE. Those whose definition of life is
less formal have looked for chemical conditions in the early
history of the planet in which some form of prebiotic selection
might have amplified the autocatalytic properties of
self-organizing protocells into functional, adapted traits.
Such studies begin with the plausible insight that life probably
did not originate solely in the accidental assembly of
nucleic acids, but in the coevolution of proteins and nucleic
acids, with protein evolution perhaps playing the leading
role. Sidney Fox’s pioneering work on proteinoid microspheres
reveals them to be spontaneously self-organized systems
that might have provided the hydrophobic boundaries
within which the subsequent coevolution of protein and
nucleic acids can take place (Fox l984). Harold Morowitz’s
and David Deamer’s notion of abiotically forming vesicular
amphiphile bilayers provides another possible such “cradle”
for the emergence of life (Morowitz, Heinz, and Deamer
l988). Manfred Eigen’s notion of hypercycles provides a
model of how this interaction might have occurred. One can
think of a hypercycle as a system of linked autocatalytic
cycles, in which each member is catalyzed by at least one
other member (Eigen and Schuster l979.)
A second issue is the relationship between natural selection
and self-organization once life is up and running. On
the face of it, self-organization rivals natural selection as the
basis of both individual development and of the larger contours
of phylogenetic order. For the acquisition of functional
traits by self-amplifying feedback is not the same thing as
selection-by-consequences by means of a forcelike “selection
pressure” that operates against an inertial, and indeed
inert, dynamic background, which is how natural selection
is usually conceived. The notion that self-organization and
natural selection are rivals has been defended by several
authors (Goodwin l994; Oyama l985; Salthe l993). A more
integrative, mutually reinforcing approach is, however, possible.
Recognizing that when any system analogous to Boolean
networks is set into motion it can be expected
spontaneously to explore its space of future states and if
mild “fitness” conditions are imposed it can be expected to
reach peaks on “adaptive landscapes.” Kauffman, Holland,
and others who have studied genetic algorithms and EVOLU-
TIONARY COMPUTATION have suggested that “spontaneous
order is available to natural selection for the further selective
crafting of well-wrought designs” (Kauffman l993: l;
Holland l995). One might readily imagine, in accord with
this suggestion, that natural selection has stabilized the selforganized
life cycle of slime molds, and in the process has
conferred an explicitly biological function on a spontaneously
generated pattern. Kauffman himself argues that the
regulatory systems of genetic networks, among whose many
nodes much connectivity and parallel processing are at play,
are self-organizing, functionally decomposable (see FUNC-
TIONAL DECOMPOSITION) systems that have been stabilized
by natural selection in this way. Weber and Depew have
argued that, considered as a natural phenomenon in its own
right, natural selection emerges only in autocatalytic chemi-
cal systems that have managed to internalize information in
macromoleules, the error rate of which provides the fuel of
natural selection (Weber and Depew l996). In this account,
genes have the function of enhancing and stabilizing the
coupling between organism and environment that selforganization
first generates.
The human brain has about l0 l0 neurons, any of which
can have up to l0 4 connections with other such neurons,
stimulated and regulated by a large number of chemical
neurotransmitters. This fact alone brings the study of cognitive
and other psychological phenomena within hailing distance
of the study of self-organizing complex systems. This
distance has been reduced by those advocating DYNAMIC
APPROACHES TO COGNITION, who recognize that learning is
a process that occurs only in systems that are “environmentally
embedded, corporeally embodied, and neurally
entrained” by feedback (see Port and Van Gelder 1995;
Smith and Thelen 1993; Kelso 1995; Cook and Murray
l995). If digital computationalism gives way to more
dynamical studies of connectivity in neural networks, selforganization
can be expected to play a more prominent role
in both the ontogeny and phylogeny of mental functions.
The purely adaptationist stories people like to tell about the
Pleistocene origins of localized mental functions (Barkow,
Cosmides, and Tooby l992), as well as the inclination to
model neurological development closely on Darwinian
mechanisms (Edelman l987), might then give way to a more
nuanced view, in which neural organization is taken to be
governed in part by self-organization working through
intense feedback between organism and environment.
See also ADAPTATION AND ADAPTATIONISM; COMPUTA-
TION AND THE BRAIN; EVOLUTIONARY PSYCHOLOGY
—David Depew and Bruce Weber
References
Barkow, J., L. Cosmides, and J. Toomy. (1992). The Adapted Mind.
New York: Oxford University Press.
Cook, J., and J. D. Murray. (l995). Pattern formation, biological. In
M. A. Arbib, Ed., Handbook of Brain Theory and Neural Networks.
Cambridge, MA: MIT Press, pp. 705–710.
Dyke, C. (1988). The Evolutionary Dynamics of Complex Systems.
Oxford: Oxford University Press.
Edelman, G. (1987). Neural Darwinism. New York: Basic Books.
Eigen, M., and P. Schuster. (1979). The Hypercycle. Berlin:
Springer.
Fox, S. (1984). Proteinoid experiments and evolutionary theory. In
M.-W. Ho and P. T. Saunders, Eds., Beyond Neo-Darwinism.
London: Academic Press, pp. l5–60.
Garkinkel, A. (1987). The slime mold Dictyostelium as a model of
self-organization in social systems. In F. E. Bates, Ed., Self-
Organizing Systems: The Emergence of Order. New York: Plenum
Press.
Goodwin, B. C. (1994). How the Leopard Changed Its Spots: The
Evolution of Complexity. London: Weidenfeld and Nicolson.
Holland, J. A. (1995). Hidden Order: How Adaptation Builds
Complexity. Reading, MA: Addison-Wesley.
Jantsch, E. (1980). The Self-Organizing Universe. Oxford: Pergamon
Press.
Kauffman, S. A. (1993). The Origins of Order: Self-Organization
and Selection in Evolution. New York: Oxford University
Press.