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Fig. 3.7 Kristen Nygaard
with C++ and others during the late 1980s. These developments opened consider-
able new options in ecological modelling, since object orientation allowed a
convenient linkage of structural and functional dynamics, which had been difficult
to bring together. In both domains, changes could be synchronously represented -
by changing the object structure together with the values of the variables stored in
the objects (Fig. 3.8 ). One of the first, who understood the importance for ecology
was Heinrich Kaiser in Aachen (Germany). He used the approach for the develop-
ment of individual-based models (Kaiser 1976, 1979). A Pioneer in the field was
also Paulien Hogeweg in Utrecht (The Netherlands, see Hogeweg and Hesper 1979,
1983). The individual-based approach offered far more options to synchronously
represent variability in physiological states of organisms, usage of their behavioural
repertoire and responses to time variant structural environmental pattern (Huston
et al. 1988; Judson 1994).
The Self-Organization Paradigm
The self-organization paradigm became influential in ecology at about the same
time as the individual-based modelling approach. It emerged in the context of
physics (Haken 1977), thermodynamics (Glansdorff and Prigogine 1971), and
systems theory. The proponents of the self-organization approach argued the
following: Science as a whole is grounded on the principle of causality. Any change
that occurs has an antecedent cause. Same settings, same causes always produce the
same results. In this way, causes and effects were traditionally segregated. What
happens if they interact in large numbers and complex networks? With the avail-
ability of automated calculation potential, non-trivial effects were discovered in
loop-structures where interaction results served as a new input (the effect becomes a
new cause that feeds back in iterative cycles). In feedback structures, non-trivial states
can emerge that cannot be reduced to plain external impact. Systems can self-generate
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