Geoscience Reference
In-Depth Information
impacted heavily by human actions both in the form of intentional harvesting
and in the form of unintended by-products of a wide range of other activi-
ties (e.g. agricultural and industrial activities generating marine pollution).
We also know that human enterprises, such as the performance of specific
markets, are impacted heavily by fluctuations in the condition of targeted
fish stocks. But this is not enough to allow for the construction of integrated
models of socio-ecological systems in which anthropogenic and biophysical
forces are operating at the same time.
What is to be done? For starters, it is essential to frame questions that
cannot possibly be answered without input from both natural scientists and
social scientists. Obvious examples include questions pertaining to the future
of coastal communities dependent on harvesting marine living resources or to
projections regarding concentrations of carbon dioxide in the Earth's atmos-
phere in 2020, 2040 or 2060. But beyond this, we are likely to confront
a trade-off between resorting to highly simplified models or working with
models that are not as tractable as we would like in analytic - and especially
mathematical - terms. Simplified models might make use of reaction/process
relationships in which various biophysical and social subsystems react to each
other's behaviour on the basis of well-defined coefficients (Rapoport 1960).
Models of this type require sacrifices in terms of methodological individual-
ism and granularity in order to represent complex systems in crude terms.
Models that sacrifice analytical tractability, on the other hand, produce more
complex systems that are not amenable to mathematical or other forms of
logical analysis. In the end, some combination of these strategies may prove
effective. The simplified models can, and often will, turn up phenomena (e.g.
emergent properties of coupled systems) that call for much more detailed
consideration. These efforts, in turn, may lead to models that can be used
to support computer simulations, even when they do not lend themselves to
more traditional forms of analysis (Meadows et al . 1992). The results, while
not predictive, can turn up numerous insights about the behaviour of coupled
systems that identify issues that are clearly relevant to policy, even though
they do not take the form of predictions in the normal sense (Bolin 1997).
Exploring interactive causal clusters
The causal mechanisms that drive socio-ecological systems do not lend them-
selves to well-defined forms of deductive and inductive reasoning that are
standard in the natural sciences and that are increasingly common in the
social sciences. The problem is twofold: (i) any of a number of combinations
of driving forces can lead to the same outcome (e.g. the degradation of marine
ecosystems, the loss of biological diversity) and (ii) the individual elements in
these clusters of drivers are highly interactive. Situations of this sort are dif-
ficult to model in a parsimonious fashion, a situation that leads either to the
development of tractable models dealing with special cases or to the creation
 
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