Scientific modelling

From Wikiquote
Jump to navigation Jump to search
Geoffrey West on Heuristics, 2005.

Scientific modelling is the process of generating abstract, conceptual, graphical and/or mathematical models. Science offers a growing collection of methods, techniques and theory about all kinds of specialized scientific modelling.


Early 20th century[edit]

  • The notion that "applied" knowledge is somehow less worthy than "pure" knowledge, was natural to a society in which all useful work was performed by slaves and serfs, and in which industry was controlled by the models set by custom rather than by intelligence.
    • John Dewey (1916). Democracy and Education, section 17.


  • 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 (1962) Scientific method: optimizing applied research decisions p. 108.
  • 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.
    • p. 108 as cited in: Joe H. Ward, Earl Jennings (1973) Introduction to linear models. p. 4.
  • Scientists work from models acquired through education and through subsequent exposure to the literature often without quite knowing or needing to know what characteristics have given these models the status of community paradigms
  • Knowledge about the process being modeled starts fairly low, then increases as understanding is obtained and tapers off to a high value at the end.
  • Any model or description that leaves out conscious forces … is bound to be sadly incomplete and unsatisfactory … This scheme is one that puts mind back over matter, in a sense, not under or outside or beside it. It is a scheme that idealizes ideas and ideals over physical and chemical interactions, nerve impulse traffic, and DNA. It is a brain model in which conscious mental psychic forces are recognized to be the crowning achievement of some five hundred million years or more of evolution.
  • For the scientist a model is also a way in which the human though processes can be amplified. This method often takes the form of models that can be programmed into computers. At no point, however, the scientist intend to loose control of the situation because off the computer does some of his thinking for him. The scientist controls the basic assumptions and the computer only derives some of the more complicated implications.


  • The statistician knows ... that in nature there never was a normal distribution, there never was a straight line, yet with normal and linear assumptions, known to be false, he can often derive results which match, to a useful approximation, those found in the real world.
    • George E. P. Box (1976) Science and statistics, Journal of the American Statistical Association 71 (356) (Dec. 1976) 791
  • We have no idea about the 'real' nature of things … The function of modeling is to arrive at descriptions which are useful.


  • Models can easily become so complex that they are impenetrable, unexaminable, and virtually unalterable.
  • The models of management which individuals and organizations use come from a variety of sources. Sometimes the model comes from a theory. The theory may emerge from someone's thoughts about the desired characteristics of a manager, or about the characteristics of competent managers. Sometimes the model comes from a panel. A group of people, possibly in the job or at levels above the job within the organization, generates a model through discussion of what is needed to perform a management job competently.
    • Richard Boyatzis (1982) Competent manager : a model for effective performance p. 7.
  • The value of global modelling has been severely restricted by poor appreciation of the constraints under which governments and politicians operate. Equally, the value of governments and politicians has been severely restricted by largely ignoring the very real but less immediate problems tackled by modellers.
    • Eduard Pestel (1982) Modellers and politicians. In: Futures, Volume 14, Issue 2, April 1982, pp. 122–128.


  • Today, nearly all biologists acknowledge that evolution is a fact. The term theory is no longer appropriate except when referring to the various models that attempt to explain how life evolves...
  • Ackoff (1962)... differentiates between iconic models, which use the same materials but involve changes in scale, analogue models which also involve a change in the materials used in building the model, and symbolic models which represent reality by some symbolic system such as a system of mathematical equations.
    • Shatrughna P. Sinha (1993) Instant Encyclopaedia of Geography. Volume 11, p. 462.


  • A model is a physical, mathematical, or logical representation of a system entity, phenomenon, or process. A simulation is the implementation of a model over time. A simulation brings a model to life and shows how a particular object or phenomenon will behave. It is useful for testing, analysis or training where real-world systems or concepts can be represented by a model.
    • From: Systems Engineering Fundamentals. Defense Acquisition University Press, 2001.
  • There are many specific techniques that modellers use, which enable us to discover aspects of reality that may not be obvious to everyone...
  • Modelling is an essential and inseparable part of all scientific, and indeed all intellectual, activity. How then can we treat it as a separate discipline? The answer is that the professional modeller brings special skills and techniques to bear in order to produce results that are insightful, reliable, and useful. Many of these techniques can be taught formally, such as sophisticated statistical methods, computer simulation, systems identification, and sensitivity analysis. These are valuable tools, but they are not as important as the ability to understand the underlying dynamics of a complex system well enough to assess whether the assumptions of a model are correct and complete. Above all, the successful modeller must be able to recognise whether a model reflects reality, and to identify and deal with divergences between theory and data.
  • Visual modeling is a usage of images in various business-fields (in the industry, science, management etc). There are additional limitations on these images distinguishing them from arbitrary pictures - they are created from the standard “patterns” having defined semantics and way of usage.
  • The role of conceptual modelling in information systems development during all these decades is seen as an approach for capturing fuzzy, ill-defined, informal "real-world" descriptions and user requirements, and then transforming them to formal, in some sense complete, and consistent conceptual specifications.
    • Janis A. Burbenko jr. (2007) "From Information Algebra to Enterprise Modelling and Ontologies - a Historical Perspective on Modelling for Information Systems". In: Conceptual Modelling in Information Systems Engineering. John Krogstie et al. eds. p. 1.
  • My first heresy says that all the fuss about global warming is grossly exaggerated. Here I am opposing the holy brotherhood of climate model experts and the crowd of deluded citizens who believe the numbers predicted by the computer models. Of course, they say, I have no degree in meteorology and I am therefore not qualified to speak. But I have studied the climate models and I know what they can do. The models solve the equations of fluid dynamics, and they do a very good job of describing the fluid motions of the atmosphere and the oceans. They do a very poor job of describing the clouds, the dust, the chemistry and the biology of fields and farms and forests. They do not begin to describe the real world that we live in. The real world is muddy and messy and full of things that we do not yet understand. It is much easier for a scientist to sit in an air-conditioned building and run computer models, than to put on winter clothes and measure what is really happening outside in the swamps and the clouds. That is why the climate model experts end up believing their own models.
  • Investors should be skeptical of history-based models. Constructed by a nerdy-sounding priesthood using esoteric terms such as beta, gamma, sigma and the like, these models tend to look impressive. Too often, though, investors forget to examine the assumptions behind the symbols. Our advice: Beware of geeks bearing formulas.
  • Models are of central importance in many scientific contexts. The centrality of models such as the billiard ball model of a gas, the Bohr model of the atom, the MIT bag model of the nucleon, the Gaussian-chain model of a polymer, the Lorenz model of the atmosphere, the Lotka-Volterra model of predator-prey interaction, the double helix model of DNA, agent-based and evolutionary models in the social sciences, or general equilibrium models of markets in their respective domains are cases in point. Scientists spend a great deal of time building, testing, comparing and revising models, and much journal space is dedicated to introducing, applying and interpreting these valuable tools. In short, models are one of the principal instruments of modern science.
    • Roman Frigg and Stephan Hartmann (2009) "Models in Science", The Stanford Encyclopedia of Philosophy (Summer 2009 Edition), Edward N. Zalta (ed.), (source).


  • Scientific modeling, the generation of a physical, conceptual, or mathematical representation of a real phenomenon that is difficult to observe directly. Scientific models are used to explain and predict the behaviour of real objects or systems and are used in a variety of scientific disciplines, ranging from physics and chemistry to ecology and the Earth sciences. Although modeling is a central component of modern science, scientific models at best are approximations of the objects and systems that they represent—they are not exact replicas. Thus, scientists constantly are working to improve and refine models.
  • Complexity scientists concluded that there are just too many factors—both concordant and contrarian—to understand. And with so many potential gaps in information, almost nobody can see the whole picture. Complex systems have severe limits, not only to predictability but also to measurability. Some complexity theorists argue that modelling, while useful for thinking and for studying the complexities of the world, is a particularly poor tool for predicting what will happen.
    • L.K. Samuels, In Defense of Chaos: The Chaology of Politics, Economics and Human Action, Cobden Press, (2013) p. 226
  • Economists also use models to learn about the world, but instead of being made of plastic, they are most often composed of diagrams and equations. Like a biology teacher’s plastic model, economic models omit many details to allow us to see what is truly important. Just as the biology teacher’s model does not include all the body’s muscles and capillaries, an economist’s model does not include every feature of the economy.
    • N. Gregory Mankiw, Principle of Economics (6th ed., 2012), Ch. 2. Thinking Like an Economist

See also[edit]

External links[edit]

Wikipedia has an article about:
Wikimedia Commons has media related to: