An Introduction to General Systems Thinking (Silver Anniversary Edition)

An Introduction to General Systems Thinking (Silver Anniversary Edition)

Gerald M. Weinberg

Language: English

Pages: 320

ISBN: 0932633498

Format: PDF / Kindle (mobi) / ePub


For more than twenty-five years, An Introduction to General Systems Thinking has been hailed as an innovative introduction to systems theory, with applications in computer science and beyond. Used in university courses and professional seminars all over the world, the text has proven its ability to open minds and sharpen thinking.

Originally published in 1975 and reprinted more than twenty times over a quarter century -- and now available for the first time from Dorset House Publishing -- the text uses clear writing and basic algebraic principles to explore new approaches to projects, products, organizations, and virtually any kind of system.

Scientists, engineers, organization leaders, managers, doctors, students, and thinkers of all disciplines can use this book to dispel the mental fog that clouds problem-solving. As author Gerald M. Weinberg writes in the new preface to the Silver Anniversary Edition, "I haven’t changed my conviction that most people don’t think nearly as well as they could had they been taught some principles of thinking.”

Now an award-winning author of nearly forty books spanning the entire software development life cycle, Weinberg had already acquired extensive experience as a programmer, manager, university professor, and consultant when this book was originally published.

With helpful illustrations, numerous end-of-chapter exercises, and an appendix on a mathematical notation used in problem-solving, An Introduction to General Systems Thinking may be your most powerful tool in working with problems, systems, and solutions.

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the measurements as altogether erroneous. Which choice we make depends on how strongly we believe T = f(a) in the first place, and whether our science of observation is "sufficiently advanced" to make it possible to refine our measurements of a. The other side of the coin is overcompleteness. T may not depend on a at all. We begin to suspect as much when we make a series of observations such as a = 0 T = 10 a = 7.5 T = 10 a = -578 T = 10 a = 0.0003 T = 10 If, no matter how much we vary a,

arose on the subatomic scale, as physicists reached the last level of reduction they were capable of performing. So as to avoid involvement in the physical details, we shall explain the situation on a larger, more familiar, scale. Suppose we were taking a traffic safety survey to study how cars accelerated coming out of a toll booth. We would like to know each car's exact position and velocity. Now suppose that we set up an automatic camera to photograph each car, after which we shall determine

the pupil, the catechumen. At the end of the session, through application of the principles of the cabal, the disciple has been brought to a new caste, and receives a new Roman numeral." "You mean he gets the teacher's the, uh, mystagogue's—caste?" "No, not necessarily. You see, it's all determined by the rules of multiplication. For example, if the master's caste is IX and the novice's caste is VII, then the new caste is III." "I'm afraid I don't follow." "Arithmetic! Simple arithmetic! All

put us out of our state-determined misery by waking us up entirely. The closed-system fiction is thus a useful heuristic device. If we see noncyclic behavior, we look for an input. If, on the other hand, we asserted that the system is closed, but "random," we would be saying there is no use looking for any additional input. Many scientists are reluctant to admit a system is open, so sometimes it does pay to talk about randomness as a labor-saving, or face-saving, device. That way we do not have

program like this: 3. Unless j is one of (12, 13, 37, 82, 94), dj = last digit of t. Such a simulation would behave in essentially the same manner as the undifferentiated system under the peculiarly partitioned input. From considerations such as these, we may derive an important principle that rests only upon the most general grounds—the Principle of Indeterminability: We cannot with certainty attribute observed constraint either to system or environment. In specific cases, we might do worse

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