Forecasts don't work out. The population suddenly starts to grow or moves to another part of the country or starts buying less of a product because of some unforeseen reason, such as a gas crisis. Attempts to make predictions suffer a chaotic fate. The predictions fail because the models can't take in the whole of how the elements in sensitive dynamical systems interact. Systems Dynamics' answer to this modeling dilemma was to make the essence of the model nonlinear and to shift the emphasis away from prediction.
Nonlinear models differ from linear ones in a number of ways.
Thus the development of the systems model exemplifies the shift that the science of chaos and change is making from quantitative reductionism to a qualitative holistic appreciation of dynamics. When they work in complex organizations such as corporations, System Dynamics modelers try to identify the written and mental concepts the people in an organization are using when they do their work, the organization's rules and policies, the actual behavior of people in the organizational setting, the organizational structure, its purpose, and numerical data such as how many people are working and when they work. The goal is to see what kinds of loops these elements form. The process of making a nonlinear feedback model is itself a nonlinear feedback process. Their first position is always cynicism.