Actual Data

A story that is frequently recounted by Jay Forrester is that a student once told him that he "learned to read the newspaper differently," after taking a class in system dynamics. Although it is possible to interpret the student's comment in a number of ways, one can speculate that, among other things, he began to see the graphs of actual time series data, presented in the media, in a different light after studying system dynamics.

Figures 1 through 10 present graphs of a variety of actual time series data reproduced from books, newspapers, and magazines, which appeared during the last few years. An examination of the figures reveals that it is possible to identify, in actual data, many of the time paths shown in the previous section.

Figure 1, for example, is a graph of actual Medicare costs from 1967 to 1994 and Medicare costs from 1967 to 1994 as projected in 1965. Two things about this graph are noteworthy. The first is that Medicare costs were projected to grow linearly from 1967 to 1994 -- a common mistake among persons not familiar with the dynamics of systems. The second is that actual Medicare costs grew exponentially from 1967 to 1994. This time path is clearly a member of the exponential family of time paths.

Figure 2 is a graph of the number of cases of the measles reported in the United States from 1960 to 1993. The time shape exhibited by these data is clearly exponential decay.

Figure 2: Measle cases from 1960 through 1993

Figure 3 below is a graph of National Basketball Association attendance during the years 1988 to 1993 and Figure 4 is a graph of the average U.S. selling price of a 486 desktop computer. These data are clearly following non-zero goal-seeking time paths.

Figure 5 is a graph of the number of lynx trapped in Canada (in log form) from 1821 to 1933. Although slightly "noisy" or choppy, the time path can certainly be classified as a sustained oscillation.

Figure 6 is a graph of Norwegian pulp inventory, production, and sales, for the years 1957 to 1978. Clearly, the time path of pulp inventory is an exploding oscillation.

Figure 7 is a time series graph of the number of third class mail pieces (in billions) delivered by the United States Postal Service, during the years 1981 to 1991. The path is clearly s-shaped.

Figure 8 is a time series graph of materials consumption in the United States for the years 1900 to 1992. Although a bit noisy, the data exhibit an overshoot and oscillate time path.

Figure 9 presents a time series graph of United States bank, savings and loan, and total financial institution, failures, during the years 1980 to 1992. All three series exhibit an overshoot and collapse time path.

Figure 10 is a graph of grainland in Japan for the years 1950 to 1994. The time path exhibited by this data is clearly a reverse s-shape.

The general conclusion that the reader should draw from these graphs is that real systems often generate clearly identifiable time patterns and that system dynamic models can be built to mimic the patterns.