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animal. We draw on insights from biology, epidemiology, and related disciplines to
identify key components of, and influences on, human and environmental systems.
We use the graphical programming language STELLA to organize these insights
into formal models that can be run on a computer; we then use these models to in-
vestigate the dynamics of pestilence and explore alternative scenarios for outside
intervention into the systems' dynamics. In particular, we look for emergent prop-
erties of the model—those results that we did not expect.
We consider this kind of modeling as a subtle craft, an art form that is intended
to help us understand the future. And because of the complexity of dynamic sys-
tems, the use of formal models and numbers is essential—they help us dispel the
complexity of many real-world processes and force us to be specific. Good dynamic
modeling is an art. It requires modeling experience that draws upon modeling analo-
gies for the creation of new and useful models.
Modeling dynamic systems is central to our understanding of real-world phe-
nomena. We all create dynamic mental models of the world around us, dissecting
our observations into cause and effect. Such mental models enable us, for exam-
ple, to cross a busy street or hit a baseball successfully. But we are not mentally
equipped to go much further. The complexities of social, economic, or ecological
systems and their interactions force us to use aids if we want to understand much of
anything about them.
With the advent of personal computers and graphical programming, everyone
can create more sophisticated models of the phenomena in the world around us. As
Heinz Pagels noted in
Dreams of Reason
in 1988, the computer modeling process is
to the mind what the telescope and the microscope are to the eye. We can model the
macroscopic results of microphenoma, and vice versa. We can simulate the various
possible futures of a dynamic process. We can begin to explain and perhaps even to
predict.
In order to deal with these phenomena, we abstract from details and attempt to
concentrate on the larger picture—a particular set of features of the real world or the
structure that underlies the processes that lead to the observed outcomes. Models are
such abstractions of reality. Models force us to face the results of the structural and
dynamic assumptions that we have made in our abstractions.
The process of model construction can be rather involved. However, it is possible
to identify a set of general procedures that are followed frequently. These general
procedures are shown in simplified circular form (Figure 1.1).
Models help us understand the dynamics of real-world processes by mimicking
with the computer the actual but simplified forces that are assumed to result in a
system's behavior. For example, it may be assumed that the number of people con-
tracting a disease is directly proportional to the size of the infected and susceptible
populations. In a simple version of this epidemic model, we may abstract away
from a variety of factors that impede or stimulate the spread of a disease in addi-
tion to factors directly related to the different population sizes and distance. Such
an abstraction may leave us with a sufficiently good predictor of the known infec-
tion rates, or it may not. If it does not, we reexamine the abstractions, reduce the
assumptions, and retest the model for its new predictions. Models help us in the
