By Charles Hall, Dr. John W. Day

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Friedland < previous page page_115 If you like this book, buy it! html9/3/2008 8:46:58 AM next page > page_116 < previous page page_116 next page > Page 116 By their very nature models are incomplete and often unrealistic. They are created that way in order to be useful. By design and of necessity they systematically ignore, disregard, or otherwise exclude from consideration features that actually are a part of the systems whose behavior is of interest. Because of the limitations of our knowledge, funding, computer capacity, and interests, we can include in the model only those features of the real system that we consider significant,whether we intend to deal primarily with inanimate objects, biological systems, or the extremely complex behavior patterns of human beings as they interact with one another and the natural environment.

Historically, of course, the problem of determining the particular evaluation metric has been the subject of the most intensive inquiry. Depending on whether our concern is with measuring "good" and "bad" as it may pertain to society, or mankind, or some particular individual or group of individuals, or even if we are concerned with the consequences of our actions upon ourselves alone, or upon our planet, or whatever entity commands our loyalty and dedication, the solution of this problem has been taken to be the province of philosophers, theologians, moralists, economists, ecologists, and all manner of thinkers.

Models of the former type are most familiar in science and, in accordance with the usual notions of scientific endeavor, the model builder's purpose and thus his modeling choices may be presumed to reflect a primary interest in the discovery of truth. The significance of the variables employed in such models depends first and foremost on their explanatory or predictive power. A scientific model is successful insofar as it accurately portrays the relationships among those factors that cause the phenomena of interest to occur and to the degree that it makes predictions that agree with later observations.

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