Database Reference
In-Depth Information
Chapter 16
Conclusion
Being able to better understand how diseases and pests are propagated and how
they may be contained requires knowledge of the many factors that influence their
success or failure. The combination of chance events, time delays, and nonlinearities
in complex social and environmental settings make predicting spread of diseases and
pests a daunting task. Yet, some method for sorting through the myriad of factors
and influences and for anticipating future dynamics is required to meet dual goals
of improving the state of human welfare and maintaining healthy ecosystems.
In this topic we provided one particular, and very powerful, way of looking at
the world. We concentrated on the forces that underlie different dynamic systems.
Others, with different educational or cultural backgrounds, may choose a different
approach and may develop different models. The potential diversity of perspec-
tives and approaches in modeling is a challenge for all of us and should be perceived
as an opportunity to engage in cross-disciplinary and cross-cultural dialogue about
the world in which we live.
STELLA, through its use of graphics, is an excellent tool to organize and com-
municate model assumptions, structure, and results among individuals with differ-
ent backgrounds. You will soon find that your models become increasingly detailed.
Frequently, model efforts become large-scale multidisciplinary endeavors. STELLA
is sufficiently versatile to enable development of complex, large-scale dynamic
models. Such models can include a variety of features that are typically not dealt
with by an individual modeler. Through easy incorporation of new modules into
existing dynamic models and flexibility in adjusting models to specific real-world
problems, STELLA fosters dialog and collaboration among modelers. It is a superb
organizing and knowledge-capturing device for model building in an interdiscipli-
nary arena. Individuals can easily integrate their knowledge into a STELLA model
without “losing sight” of, or influence on, their particular part of the model.
Even though the models developed in this topic were guided toward an explana-
tion of real-world phenomena, empirical applications are not the focus of this topic.
Nevertheless, once we developed sufficiently elaborate models, we made intensive
use of real-world data. These and other models illustrate the applicability to real-
world data and, in general, the power of the dynamic modeling approach chosen
