Teaching Science as Model-Building

Scientific 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.

Large scale 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:

"What 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

Feynman 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:

  1. Simple situations in which there are only few parts and we can predict what will happen and thus verify how our rules work.
  2. Situations 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.
  3. Methods of approximation may yield increasingly closer approaches to the real rules. Feynman remarks that this type of modeling is the most powerful way.

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.

The main 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:

"Scientists 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."

This explanation 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.

The lack 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 literacy.

 

[1] Feynman, Richard. Lectures on Physics: Volume One, Addison-Wesley Pub Co., 1963. (page 2-1)

  ©Copyright 2003 Carnegie Mellon University
This material is based upon work supported by the National Science Foundation under Grant Number 9653194. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.