“A model is a qualitative or quantitative representation of a process or endeavor that shows the effects of those factors which are significant for the purposes being considered. A model may be pictorial, descriptive, qualitative, or generally approximate in nature; or it may be mathematical and quantitative in nature and reasonably precise. It is important that effective means for modeling be understood such as analog, stochastic, procedural, scheduling, flow chart, schematic, and block diagrams.”

—  Harold Chestnut

Source: Systems Engineering Tools, (1965), p. 108; As cited in: Alberto Ortiz (1992) Systems engineering concept demonstration, process model http://www.dtic.mil/dtic/tr/fulltext/u2/a265469.pdf. p. 12

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Harold Chestnut27
American engineer 1917–2001

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“A model may be pictorial, descriptive, qualitative, or generally approximate in nature, or it may be mathematical and quantitative in nature and reasonably precise.”

Harold Chestnut (1917–2001) American engineer

Source: Systems Engineering Tools, (1965), p. 108

“As is used in connection with systems engineering, a model is a qualitative or quantitative representation of a process or endeavor that shows the effects of those factors which are significant for the purposes being considered. Modeling is the process of making a model. Although the model may not represent the actual phenomenon in all respects, it does describe the essential inputs, outputs, and internal characteristics, as well as provide an indication of environmental conditions similar to those of actual equipment.”

Harold Chestnut (1917–2001) American engineer

Source: Systems Engineering Tools, (1965), p. 108; As cited in: Alberto Ortiz (1992, p. 13)

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“The problem is not the math itself, but the blind acceptance and even idolatry we have applied to the quantitative models. These predictive models leave citizens befuddled and unable to defend or criticize model-based decisions. We argue that we should accept the fact that we live in a qualitative world when it comes to natural processes. We must rely on qualitative models that predict only direction, trends, or magnitudes of natural phenomena, and accept the possibility of being imprecise or wrong to some degree. We should demand that when models are used, the assumptions and model simplifications are clearly stated. A better method in many cases will be adaptive management, where a flexible approach is used, where we admit there are uncertainties down the road and we watch and adapt as nature rolls on.”

Orrin H. Pilkey (1934) American ecologist

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“In the quantitative models that appear in leading economics journals and textbooks, nature is taken to be a fixed, indestructible factor of production. The problem with the assumption is that it is wrong: nature consists of degradable resources.”

Partha Dasgupta (1942) British economist

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“In an unrestricted science such as biology or geology, the effects under study are so complex that designed experiments with controls are often not possible. Quantitative models are more vulnerable and the chance of unknown factors dominating the observations is much greater.”

Peter Checkland (1930) British management scientist

Source: Systems thinking, systems practice: includes a 30-year retrospective, 1999, p. 65

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“If the system exhibits a structure which can be represented by a mathematical equivalent, called a mathematical model, and if the objective can be also so quantified, then some computational method may be evolved for choosing the best schedule of actions among alternatives. Such use of mathematical models is termed mathematical programming.”

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Source: Linear programming and extensions (1963), p. 2

“… it is most important – particularly in an immature field like information science - to accept that all modes of study and all analytical methods can make useful contributions, and not to denigrate [qualitative] models as “just description””

Brian Campbell Vickery (1918–2009) British information theorist

Source: Information Systems (1973), p. 331; As cited in: Lyn Robinson and David Bawden (2011).

“Simulation is the use of models and/or the actual conditions of either the thing being modeled or the environment in which it operates, with the models or conditions in physical, mathematical, or some other form. The purpose of simulation is to explore the various results which might be obtained from the real system by subjecting the model to representative environments which are equivalent to, or in some way representative of, the situations it is desired to understand or investigate. Simulation may involve system hardware and the actual physical environment, or it may involve mathematical models subjected to mathematical forcing or disturbance functions representative of the systems conditions to be studied.”

Harold Chestnut (1917–2001) American engineer

Source: Systems Engineering Tools, (1965), p. 111 as cited in

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“As opposed to [megatechnics], an organic system directs itself to qualitative richness, amplitude, spaciousness, free from quantitative pressure and crowding, since self-regulation, self-correction, and self-propulsion are as much an integral property of organisms as nutrition, reproduction, growth, and repair. Balance, wholeness, completeness, continuous interplay between inner and outer, the subjective and the objective aspects of existence are identifying characteristics of the organic model; and the general name for an economy based on such a model is an economy of plenitude.”

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“We should not, perhaps, take it for granted that a description in terms of a formalised model, which has certain properties lacking in those derived from models of another kind, will necessarily be the best description for all of the very diverse purposes for which the descriptions of languages are needed. In assessing the value of a description, it is reasonable to ask whether it has proved useful for the purposes for which it was intended.”

Michael Halliday (1925–2018) Australian linguist

Source: 1950s–1960s, The Linguistic Sciences and Language Teaching, 1964, p. 13. cited in: David Brazil (1995) A Grammar of Speech. p. 9.

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