inquiry is mostly presented as great men discovering the "truths"
of nature rather than as a meandering course of curiosity and observation,
and the organization of observations into patterns. For any environmental
concern, these patterns are complex and contain layers of interacting
units, as described earlier. Data and calculations, even those based on
well-understood scientific truths, acquire large uncertainties when put
into this complex framework. Institutional and personal biases select
the questions to be answered and the ways in which the scientific uncertainties
are interpreted. It is well known and acknowledged that large-scale technological
systems and projects create mistakes and disasters, partly due to of biased
interpretation or denial of scientific or empirical uncertainty, or the
overlooking of interactions in complex systems.
environmental issues such as global climate change are studied by bringing
together large working models of the atmosphere, of climate, and of the
distribution and dispersion of releases of materials from human activity.
While only a specialist can understand the details of this modeling, every
student of the environment should recognize the complexity and inherent
uncertainty of results emerging from such models and what these imply
for decision making.
It is important
therefore to present science as a work in progress--a model of natural
phenomena that is refined and rebuilt continuously. Even in physics, a
science that is thought to be rigorous, this sense is important. As described
by Richard Feynman, one of the greatest physicists:
do we mean by 'understanding something'? We can imagine that this complicated
array of moving things which constitutes 'the world' is something like
a great chess game being played by the gods, and we are observers of
the game. We do not know what the rules of the game are: all we are
allowed to do is to watch the playing. Of course, if we watch long enough
we may eventually catch on to a few of the rules. The rules of the game
are what we mean by fundamental physics. Even if we knew every rule,
however, we might not be able to understand why a particular move is
made in the game, merely because it is too complicated..."1
describes observation, reason, and experiment as the basis of the scientific
method. He speaks of three situations in which we can check if we are
guessing the "rules of the game" correctly:
situations in which there are only few parts and we can predict what
will happen and thus verify how our rules work.
where we may not know the details but can figure out a rule that works
in an overall way. After several applications of this rule, a pattern
may emerge and show us the rule.
of approximation may yield increasingly closer approaches to the real
rules. Feynman remarks that this type of modeling is the most powerful
It is useful
to introduce these situations (that is, the nature of scientific models)
along with the scientific knowledge so the student understands the inherent
scientific uncertainty. For example, a simple model of the sun-earth system
involving only gravitation and the influx of the sun's radiation onto
the planet without bringing in questions of climate, is of the first type.
Some of the simple phenomena as the seasons and day-night cycles are described
adequately by this picture. The water cycle is an example of Feynman's
second situation. Even if we do not know all the details of all molecular
motions, models of the evaporation and condensation of water in cycles
goes a long way in describing the role of water on the planet and the
evolution of life. Detailed models involving movements of air masses and
other compounds introduced into the atmosphere are examples of the third
type. While these may have greater sophistication, they are complicated
and may still have large uncertainties in them.
success of traditional science has been its ability to relate cause and
effect. Many marvels of technology have arisen from this ability. Newton's
laws are essentially the basis of space travel so far. However, predicting
natural phenomena may be much harder than travel to the moon. In the article
"Chaos: Does God Play Dice?" from the 1990 Yearbook of Science
and the Future, Encyclopedia Britannica, Ian Stewart says:
can predict the tides, so why do they have so much difficulty predicting
the weather? ...the two systems are different. The weather is extremely
complex; it involves dozens of such quantities as air pressure, humidity,
wind speed, and cloud cover. Tides are much simpler. Or are they? In
reality, the system that gives rise to the tides involves just as many
variables--the shape of the coastline, the temperature of the sea, the
salinity, it's pressure, the waves on its surface, the position of the
Sun and Moon, and so on--as that which gives rise to the weather. Somehow,
however, those variables interact in a regular and predictable fashion.
The tides are a phenomenon of order. Weather, on the other hand, is
not. There the variables interact in an irregular and impredictable
way. Weather is, in a word, chaos."
conveys the fact that the model-building roles of science vary depending
upon the type of system in question. While we will not deal with the details
of complex systems--such as that of climate and weather-- in this text,
it is important to realize that there are intricacies, and that not understanding
these intricacies has led to behaviors that degrade the environment.
of understanding of these complexities has also led to arguments over
model predictions among parties with different vested interests. Thus
arguments about the uncertainties in the model when ozone depletion was
first predicted by scientists Molina and Rowland delayed the reduction
of chlorofluorocarbon use. Arguments over global climate change modeling
and predictions are still holding up actions by various governments to
control emissions. Understanding that science is ultimately model-building
with definite capabilities and shortcomings is critical to environmental
Feynman, Richard. Lectures on Physics: Volume One, Addison-Wesley
Pub Co., 1963. (page 2-1)