CHAPTER 14
Summary of Part II

In Part III that follows are examples of dynamic models based on the philosophy and general principles that have just been given in Part II. Before going to Part III, the chapters of Part II will now be briefly summarized.

IN Chapter 4, models are classified. Those that would usefully describe industrial and economic behavior were identified as mathematical, dynamic and nonlinear models. They need to treat both stable and unstable as well as steady- state and transient systems.

Industrial dynamics models, in their purpose and origin, will be more similar to models of engineering systems than to models in the physical, genetic, and agricultural sciences. They are models of information-feedback systems, not models of open-ended systems in which results do not react on causes.

Mathematical models can be constructed from the bottom upward, based on our descriptive knowledge of the elementary parts of the system to be represented (the size of the elements depends on the purpose of the model). A successful dynamic model of nonlinear, noisy, information-feedback systems probably cannot be derived by statistical analysis of the over-all, aggregate, system performance.

Mathematical models make controlled experiments possible and allow us to see the effect of the separate parts of the system. A management laboratory then becomes possible for the design of improved managerial policies. The dynamic model is a tool for the design of policy and organizational form.

The understanding of system dynamics and the tools of electronic computation have now reached the point where systems of hundreds or thousands of linear and nonlinear variables car be readily and economically investigated. The hurdles are not technical nor financial, but in. tellectual. Success will be paced by the rate of advancement in our descriptive knowledge and insight into the industrial, economic, political, educational, and social interactions that surround us. Models must simultaneously deal with all these factors if they are to serve their purpose.

Dynamic models will be based primarily or our descriptive information already available, not on statistical data alone. Observation of and familiarity with a system will reveal actions, motivations, and information sources that cannot be discovered through historically available quantitative measures. The "intangibles" must appear quantitatively in a model to the best ability of the model builder; to omit them is a more serious failing than to have an error in magnitude. Our real-life systems are not highly sensitive to changes in the components of which they are constructed, neither are models having the proper structure. Severalfold changes in values of parameters and major changes in the shapes of assumed statistical distributions of system responses often have but slight influence. Concentration must be on those factors that determine the characteristics of information-feedback systems -- structure, amplifica

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