In this modern era of mathematical modeling, applications have
become increasingly complicated. As the complexity grows, it
becomes more and more difficult to draw meaningful conclusions
about the behavior of theoretical models and their relations to
reality. Alongside methods that emphasize quantitative properties
and the testing of scientific details, there is a need for
approaches that are more qualitative. These techniques attempt to
cover whole families of models in one bold stroke, in a manner that
allows robust conclusions to be drawn about them.
Loop analysis and time averaging provide a means of interpreting
the properties of systems from the network of interactions within
the system. The authors' methodology concentrates on graphical
representation to guide experimental design, to identify sources of
external variability from the statistical pattern of variables, and
to make management decisions.
Although most of the examples are drawn from ecology, the
methods are relevant to all of the pure and applied sciences. This
relevance is enhanced by case studies from such diverse areas as
physiology, resource management, the behavioral sciences, and
social epidemiology. The book will be useful to a broad readership
from the biological and social sciences as well as the physical
sciences and technology. It will interest undergraduate and
graduate students along with researchers active in these
disciplines. Here the reader will find a strong rationale for
maintaining a holistic approach, revealing what insights and
advantages are retained by the broader perspective and, more
explicitly, by the synergistic effects that cannot be discerned by
reducing systems to their smallest parts.
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