"The Machine Age’s commitment to cause and effect was the source of many dilemmas, including the one involving free will. At the turn of the century the American philosopher E.A. Singer, Jr., showed that science had, in effect, been cheating. It was using two different relationships but calling both cause and effect. He pointed out, for example, that acorns do not cause oaks because they are not sufficient, even though they are necessary, for oaks. An acorn thrown into the ocean, or planted in the desert or an Arctic ice cap does not yield an oak. To call the relationship between an acorn and an oak ‘probabilistic’ or ‘non deterministic causality,’ as many scientists did, was cheating because it is not possible to have a probability other than 1.0 associated with a cause; a cause completely determines its effect. Therefore, Singer chose to call this relationship ‘producer-product’ and to differentiate it from cause-effect."

“Scientific models have all these connotations. They are representations of states, objects, and events. They are idealized in the sense that they are less complicated than reality and hence easier to use for research purposes. These models are easier to manipulate and "carry" than the real thing. The simplicity of models, compared with reality, lies in the fact that only the relevant properties of reality are represented.”

“A good deal of the corporate planning I have observed is like a ritual rain dance; it has no effect on the weather that follows, but those who engage in it think it does. Moreover, it seems to me that much of the advice and instruction related to corporate planning is directed at improving the dancing, not the weather.”

“Over time, every way of thinking generates important problems that it cannot solve.”

“Managers need all the information they want. Most MIS designers "determine" what information is needed by asking managers what information they would like to have. This is based on the assumption that managers know what information they need and want.”

“Analysis of a system reveals its structure and how it works. It provides the knowledge required to make it work efficiently and to repair it when it stops working. Its product is know-how, knowledge, not understanding. To enable a system to perform effectively we must understand it—we must be able to explain its behavior—and this requires being aware of its functions in the larger systems of which it is a part.”

“Analysis of a system reveals how it works; it provides know-how, knowledge, not understanding; that is, explanations of why it works the way it does. This requires synthetic thinking... Analysis is the way scientists conduct research. Synthetic thinking is exemplified by design.”