Biology Reference
In-Depth Information
behavioral traits of individual organisms which are important in ecology
and evolution deserve attention. In the autecologist's view, populations
are temporary and dynamic aggregations of individuals in a stochastically
fluctuating environment, they are not abstractions of ecologists in the
sense used by demographic ecologists. Autoecology, therefore, is not
local like demographic ecology, and its focus is not on balancing numbers
across populations, as in metapopulation ecology.
Clearly, Ramensky, Gleason, Andrewartha, and Birch, among others,
are autecologists in the sense of Hengeveld and Walter, whereas the
majority of ecologists past and present have been demographic ecologists.
Of very great importance are the studies by Brown, Gillooly, Allen and
collaborators. Their aim is to explain patterns by the first principles of
body size, temperature, and stoichiometry (Allen et al.
2002
; Gillooly et al.
2002
; Brown et al.
2004
). This ''metabolic theory of ecology'' does not
rely on equilibrium assumptions, and can be expected to become increas-
ingly important in the future (see pp. 184-185).
The approach of NKS (''New Kind of Science'', Wolfram
2002
), and
in particular the use of cellular automata as applied by Wolfram (
1986
,
2002
), is also potentially very significant. The basic idea of NKS is to
run many computer programs based on different ''rules'' and to see how
they behave. A cellular automaton consists of rows of cells; each cell has a
state associated with it, for example, either black or white. Arbitrary rules
specify how the automaton develops, i.e., how a cell evolves from one
computational stage to the next, based on its previous state and that of its
neighbors. Extensive studies have shown that very simple rules
(as measured by the number of instructions) lead to simple repetitive
patterns, but that a slight increase in a rule's complexity may lead to very
complex, apparently random (or better pseudo-random) patterns.
Increasing the complexity of rules above a certain threshold does not
lead to a further increase in the complexity of patterns.
Other systems, such as mobile automata, tag systems, cyclic tag systems,
Turing machines, substitution systems, sequential substitution systems, regis-
termachinesandsymbolicsystemssuchasMathematica
TM
(for definitions
see Wolfram
2002
) follow the same principle, i.e., complex patterns can be
created by rules whose complexity lies between a lower and upper threshold.
Traditional science usually considers systems that satisfy certain con-
straints. Cellular automata can be adapted to the use of such constraints.
For example, a constraint can specify that every white cell should have a
certain kind of neighbor. Many of such rules lead to simple repetitive or
nested patterns, but some lead to complex and pseudo-random patterns.
