With the arrival of easy-to-use computers and software, we can all build on the ex-
isting descriptions of a system and carry them further. The world is not a static or
comparative static process. The models treating it in that way may be misleading and
become obsolete. We can now investigate in great detail and with great precision the
system’s behavior over time, including its movement toward or away from equilib-
rium positions, rather than restricting the analysis to the equilibrium itself. “Theoret-
ical Economics will have virtually abandoned classical equilibrium theory in the
next decade; the metaphor in the short term future will be evolution, not equilib-
rium.”
1
To this we add our own prediction that the study of economics will evolve
over the long run into dynamic computer simulation, based to a significant extent on
game theory and experimental approaches to understanding economic processes.
Throughout the social, biological, and physical sciences, researchers examine
complex and changing interrelationships among factors in a variety of disci-
plines. What are the impacts of a change in the El Niño winds, not only on
weather patterns but also on the cost of beef in the United States? How does the
value of the Mexican peso affect the rate of oil exploration in Alaska? Every day,
scientists ask questions like these that involve dissimilar frames of reference and
even disciplines. This is why understanding the dynamics and complex interrela-
tionships among diverse systems in our increasingly complicated world is impor-
tant. A good set of questions is the start—and often the conclusion—of a good
model. Such questions help the researcher remain focused on the model without
becoming distracted by the myriad of random details that surround it.
Through computer modeling we can study processes in the real world by sketch-
ing simplified versions of the forces assumed to underlie them. As an example, you
might hypothesize that cities drew workers from farmlands as both became greater
users of technology, causing a surplus of jobs in the city and a surplus of labor in the
countryside. Another factor could be the feasibility of moving from an agricultural
area to the city. A basic version of this model might abstract away from many of the
factors that encourage or discourage such migration, in addition to those directly re-
lated to job location and the feasibility of relocation. This model could leave behind
a sufficiently good predictor of migration rates, or it might not. If the model does
not appear to be a good predictor, you can reexamine it. Did your abstractions elim-
inate any important factors? Were all your assumptions valid? You can revise your
model, based on the answers to these questions. Then you can test the revised
model for its predictions. You should now have an improved model of the system
you are studying. Even better, your understanding of that system will have grown.
You can better determine whether you asked the right questions, included all the
important factors in that system, and represented those factors properly.
Elementary to modeling is the idea that a model should be kept simple, even
simpler than the cause-and-effect relationship it studies. Add complexities to the
model only when it does not produce the real effects. Models are sketches of real
systems and are not designed to show all of the system’s many facets. Models aid
us in understanding complicated systems by simplifying them.
4 1. Introduction to Dynamic Modeling
1. Anderson 1995, 1617.