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Virtual
Ecosystems -- Models And Modeling
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"The greatest
impediment to scientific innovation is usually a
conceptual lock, and not a factual lock."
Stephen J Gould |
Our very existence depends on our ability to abstract or
translate the world outside our bodies into useful
models. We create models to describe, to predict, to
test for abnormalities, and to define confidence levels.
Our body is constantly creating abstractions of the
physical things we see in a format that is understood by
our body. With this process we are able to understand
objects and predict events around us.
For example, our optical senses are never directly
connected to objects. Instead the eye, the optical
nerve, and the brain serve as intermediaries that model
objects through image transformation. To our body, a
tree outside our bedroom window is really a series of
energized nerve and brain cells. Sunlight shining on the
tree is composed of many different wavelengths each
representing a color we humans have defined. That light
shines on the tree and its surrounding background. The
tree absorbs some of the light's wavelengths and
reflects other wavelengths. The reflected wavelengths
are concentrated in the lens of our eyes and absorbed by
rods and cones in the back of our eyes. Our optic nerves
are stimulated and transmit messages to the brain that
portrays the sensitivity of the eye's rods and cones to
the wavelengths of the light it receives. Our brain
converts the optic information. It then creates a
perception by interpreting and comparing the new
information with life experiences and with things we
have been taught earlier.
Our bodies both model our perception of reality and then
verify perception through the use of other models. We
are capable of sensing the same subject through
different points of view - hearing, seeing, touching, or
smelling. As each model is used, a greater perspective
is gained. Modeling an object through our eyes can be
generalized into five steps:
- Data Collection -- We receive the light waves in
our eyes.
- Data Transformation -- Light energy is transformed
to nerve pulses and nerve pulses are transformed to
information storage.
- Data Comparison - Similarities are sought when the
newly stored information is compared with older
information.
- Data Verification - Other modeling methods (touch,
smell, taste) are used to validate our optic model
and to get different perspectives.
- Data Abstraction - We bring new and old data
together in our mind to form some basic conclusions.
If any one of these five modeling steps is faulty, we
might get a different picture of reality. If we are
viewing a tree with little or no light, our data might
be erroneous. If we are color blind, we might do an
ineffective job at data transformation. If a tree killed
my father years ago, my abilities to view similarities
might be emotionally biased. If I have numb fingers, I
am hampered in data verification by feel. All sorts of
feelings stored in a human brain might skew the data
abstraction process.
There are many man-made modeling processes that we take
for granted but depend upon continually. The clock, our
calendar, our numbering system, and mathematics itself
are all models.
Rarely is a model a perfect representation of reality
because it is usually unnecessary to model every
complexity of a system in order to understand or use the
system. Assumptions are used even when we know the truth
because assumptions permit us to simplify a model and
make it more manageable in some way. The assumption that
a day can be divided into exactly 24 hours is not
precisely accurate. But the assumption is convenient for
our clock model because 24 is easily divisible into a
360-degree circle. The inaccuracies of the assumption
are easily overcome with an occasional one-second
adjustment to our time standards and using leap years in
our calendars.
Ecosystem simulation models provide a powerful way for
informing decision makers. Just as a pilot gains
understanding of the consequences of his or her
decisions by hours spent on flight simulators, so too
can ecosystem and resource managers gain insights
through exploring simulation models that approximate the
systems under study.
The aim of research models is not perfection, but
understanding. Not exact descriptions, but useful
insight. Modeling doesn't just catalog patterns, but
helps find principles that explain the patterns. And,
models shed light on the real problem at hand. They can
tell you what to look for before spending money going
into the field. |
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