“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

Interview with Orrin Pilkey & Linda Jarvis-Pilkey https://web.archive.org/web/20080105132439/http://www.columbia.edu/cu/cup/publicity/pilkeyinterview.html. <br class="br">Useless Arithmetic: Why Environmental Scientists Can’t Predict the Future (2007)

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Orrin H. Pilkey4

American ecologist 1934

Harold Chestnut (1917–2001) American engineer

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

“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

“If we are to use effectively these abstract models and this descriptive material, we must have a comparable exploration of the criteria for determining what abstract model it is best to use for particular kinds of problems, what entities in the abstract model are to be identified with what observable entities, and what features of the problem or of the circumstances have the greatest effect on the accuracy of the predictions yielded by a particular model or theory.”

Milton Friedman (1912–2006) American economist, statistician, and writer

"The Methodology of Positive Economics" (1953)

“The critical element is inclusiveness. It is only when the model we adopt and implement is one all can accept, can the prospect of genuine and lasting reconciliation be real.”

Joni Madraiwiwi (1957–2016) Fijian politician

Opening address, Fiji Week celebrations, 7 October 2005.

“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)

“We should continue to use simple models where they capture enough of the core underlying structure and incentives that they usefully predict outcomes. When the world we are trying to explain and improve, however, is not well described by a simple model, we must continue to improve our frameworks and theories so as to be able to understand complexity and not simply reject it.”

Elinor Ostrom (1933–2012) American political economist

Elinor Ostrom (2009) "Nobel Prize Lecture", December 8.

“For more than twenty-five years we have monitored beach nourishment projects around the United States. In order to secure federal funding and justify the enormous costs of these projects, anyone undertaking one must make a prediction of how long the sand will last on the replenished beach. The predictions are based on mathematical models that are said to be sophisticated and state of the art, and yet are consistently, dramatically wrong—always in an optimistic direction. In the rare instances when communities questioned the models after the predictions of a long healthy replenished beach clearly failed, the answer typically was that an unusual and unexpected storm caused the error. Well, the occurrence of storms at any beach is neither unusual nor unexpected. Eventually we became interested in how models were used in other fields. When you start looking into it, you find that a lot of global and local decisions are made based on modeling the environment. There are some fascinating (and discouraging) stories of model misuse and misplaced trust in models in the book.”

Orrin H. Pilkey (1934) American ecologist

“Cellular automata are now being used to model varied physical phenomena normally modelled by wave equations, fluid dynamics, Ising models, etc. We hypothesize that there will be found a single cellular automaton rule that models all of microscopic physics; and models it exactly. We call this field DM, for digital mechanics.”

Edward Fredkin (1934) American physicist and computer scientist, a pioneer of digital physics

[An informational process based on reversible universal cellular automata, Physica D: Nonlinear Phenomena, 45, 1–3, September 1990, 254–270, https://www.sciencedirect.com/science/article/pii/016727899090186S, 10.1016/0167-2789(90)90186-S]

“…we are in a logic of simulation, which no longer has anything to do with a logic of facts and an order of reason. Simulation is characterized by a precession of the model, of all the models based on the merest fact - the models come first, their circulation, orbital like that of the bomb, constitutes the genuine magnetic field of the event. The facts no longer have a specific trajectory, they are born at the intersection of models, a single fact can be engendered by all the models at once.”

Jean Baudrillard (1929–2007) French sociologist and philosopher

"The Precession of Simulcra,MÖBIUS - SPIRALING NEGATIVETY
1980s, Simulacra and Simulation (1981)

“Two of the most widely adopted models of human resource management are the hard and soft versions. These are based on opposing views of human nature and managerial control strategies. The hard model is based on notions of tight strategic control, and an economic model of man according to Theory X, while the soft model is based on control through commitment and Theory Y. We argue that because these assumptions are so divergent, they cannot both properly be incorporated within a single model of human resource management.”

Lynda Gratton (1953) Business theorist

Catherine Truss, Lynda Gratton, Veronica Hope-Hailey, Patrick McGovern and Philip Stiles (1997). "Soft and hard models of human resource management: a reappraisal." Journal of Management Studies, 34(1), 53-73.

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