“To an outsider, the most significant innovation in the global warming controversy is the overt reliance that is being placed on models. Back in the days of nuclear winter, computer models were invoked to add weight to a conclusion: “These results are derived with the help of a computer model.” But now, large-scale computer models are seen as generating data in themselves. No longer are models judged by how well they reproduce data from the real world—increasingly, models provide the data. As if they were themselves a reality. And indeed they are, when we are projecting forward.”

— Michael Crichton

Aliens Cause Global Warming (2003)

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Michael Crichton 121

American author, screenwriter, film producer 1942–2008

Mark S. Fox (1952) Canadian computer scientist and Professor of Industrial Engineering

Mark S. Fox, John F. Chionglo, and Fadi G. Fadel (1993) " A common-sense model of the enterprise http://windsor.mie.utoronto.ca/enterprise-modelling/papers/fox-ierc93.pdf." Proceedings of the 2nd Industrial Engineering Research Conference. Vol. 1. 1993.

“The accounting methods based on mathematical models, the use of computers for computations and information data processing make up only one part of the control mechanism, another part is the control structure.”

Leonid Kantorovich (1912–1986) Russian mathematician

"Mathematics in Economics: Achievements, Difficulties, Perspectives," 1975

“Now the salient characteristic of the decision tools employed in management science is that they have to be capable of actually making or recommending decisions, taking as their inputs the kinds of empirical data that are available in the real world, and performing only such computations as can reasonably be performed by existing desk calculators or, a little later electronic computers. For these domains, idealized models of optimizing entrepreneurs, equipped with complete certainty about the world - or, a worst, having full probability distributions for uncertain events - are of little use. Models have to be fashioned with an eye to practical computability, no matter how severe the approximations and simplifications that are thereby imposed on them…
The first is to retain optimization, but to simplify sufficiently so that the optimum (in the simplified world!) is computable. The second is to construct satisficing models that provide good enough decisions with reasonable costs of computation. By giving up optimization, a richer set of properties of the real world can be retained in the models… Neither approach, in general, dominates the other, and both have continued to co-exist in the world of management science.”

Herbert A. Simon (1916–2001) American political scientist, economist, sociologist, and psychologist

Source: 1960s-1970s, "Rational decision making in business organizations", Nobel Memorial Lecture 1978, p. 498; As cited in: Arjang A. Assad, ‎Saul I. Gass (2011) Profiles in Operations Research: Pioneers and Innovators. p. 260-1.

“Information engineering has been defined with the reference to automated techniques as follows: An interlocking set of automated techniques in which enterprise models, data models and process models are built up in a comprehensive knowledge-base and are used to create and maintain data-processing systems.”

James Martin (author) (1933–2013) British information technology consultant and writer

Source: Information Engineering (1989), p. viii

“We build models to increase productivity, under the justified assumption that it's cheaper to manipulate the model than the real thing. Models then enable cheaper exploration and reasoning about some universe of discourse. One important application of models is to understand a real, abstract, or hypothetical problem domain that a computer system will reflect. This is done by abstraction, classification, and generalization of subject-matter entities into an appropriate set of classes and their behavior.”

Stephen J. Mellor (1952) British computer scientist

Source: MDA Distilled. Principles of Model-Driven Architecture, 2003, p. 35-36.

“For the theory-practice iteration to work, the scientist must be, as it were, mentally ambidextrous; fascinated equally on the one hand by possible meanings, theories, and tentative models to be induced from data and the practical reality of the real world, and on the other with the factual implications deducible from tentative theories, models and hypotheses.”

George E. P. Box (1919–2013) British statistician

Source: Science and Statistics (1976), p. 792

“The word model is used as a noun, adjective, and verb, and in each instance it has a slightly different connotation. As a noun "model" is a representation in the sense in which an architect constructs a small-scale model of a building or a physicist a large-scale model of an atom. As an adjective "model" implies a degree or perfection or idealization, as in reference to a model home, a model student, or a model husband. As a verb "to model" means to demonstrate, to reveal, to show what a thing is like.”

Russell L. Ackoff (1919–2009) Scientist

Source: 1960s, Scientific method: optimizing applied research decisions, 1962, p. 108 as cited in: Charles West Churchman, Richard O. Mason (1976) World modeling: a dialogue. p. 23.

“The existence of exotic dark matter particles outside the standard model of particle physics constitutes a central hypothesis of the current standard model of cosmology (SMoC). Using a wide range of observational data I outline why this hypothesis cannot be correct for the real Universe.”

Pavel Kroupa (1963) Australian astrophysicist

[Pavel Kroupa, 2014, Lessons from the Local Group (and beyond) on dark matter, arXiv.org, http://arxiv.org/abs/1409.6302]

“As information systems play a more active role in the management and operations of an enterprise, the demands on these systems have also increased. Departing from their traditional role as simple repositories of data, information systems must now provide more sophisticated support to manual and automated decision making; they must not only answer queries with what is explicitly represented in their Enterprise Model, but must be able to answer queries with what is implied by the model. The goal of the TOVE Enterprise Modelling project is to create the next generation Enterprise Model, a Common Sense Enterprise Model. By common sense we mean that an Enterprise Model has the ability to deduce answers to queries that require relatively shallow knowledge of the domain.”

Mark S. Fox (1952) Canadian computer scientist and Professor of Industrial Engineering

Source: Methodology for the Design and Evaluation of Ontologies (1995), p. 1: Introduction

“[A computer program for Task A qua an explanatory model and how a human cognizer actually carries out Task A are equivalent in the strong sense when it can be shown that]… the model and the organism are carrying out the same process.”

Zenon Pylyshyn (1937) Canadian philosopher

Source: Computation and cognition, 1984, p. xv; As cited in: Journal of Intelligent Systems, Volume 4. (1994), p. 313

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