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Example of scientific modelling. A schematic of chemical and transport processes related to atmospheric composition.

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. Also a way to read elements easily which have been broken down to the simplest form

Modelling is an essential and inseparable part of all scientific activity, and many scientific disciplines have their own ideas about specific types of modelling. There is little general theory about scientific modelling, offered by the philosophy of science, systems theory, and new fields like knowledge visualization.


Scientific modelling basics



A model is a simplified abstract view of the complex reality. A scientific model represents empirical objects, phenomena, and physical processes in a logical way. Attempts to formalize the principles of the empirical sciences, use an interpretation to model reality, in the same way logicians axiomatize the principles of logic. The aim of these attempts is to construct a formal system for which reality is the only interpretation. The world is an interpretation (or model) of these sciences, only insofar as these sciences are true.[1]
For the scientist, a model is also a way in which the human thought processes can be amplified.[2] Models that are rendered in software allow scientists to leverage computational power to simulate, visualize, manipulate and gain intuition about the entity, phenomenon or process being represented.

Modelling as a substitute for direct measurement and experimentation

Models are typically used when it is either impossible or impractical to create experimental conditions in which scientists can directly measure outcomes. Direct measurement of outcomes under controlled conditions (see Controlled Experiment, Scientific Method) will always be more accurate than modeled estimates of outcomes. When predicting outcomes, models use assumptions, while measurements do not. As the number of assumptions in a model increases, the accuracy and relevance of the model diminishes.

Modelling language

A modelling language is any artificial language that can be used to express information or knowledge or systems in a structure that is defined by a consistent set of rules. The rules are used for interpretation of the meaning of components in the structure. Examples of modelling languages are the Unified Modeling Language (UML) for software systems, IDEF for processes and the VRML for 3-D computer graphics models designed particularly with the World Wide Web in mind.


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.[3]


Structure is a fundamental and sometimes intangible notion covering the recognition, observation, nature, and stability of patterns and relationships of entities. From a child's verbal description of a snowflake, to the detailed scientific analysis of the properties of magnetic fields, the concept of structure is an essential foundation of nearly every mode of inquiry and discovery in science, philosophy, and art.[4]


A system is a set of interacting or interdependent entities, real or abstract, forming an integrated whole. The concept of an 'integrated whole' can also be stated in terms of a system embodying a set of relationships which are differentiated from relationships of the set to other elements, and from relationships between an element of the set and elements not a part of the relational regime.

The process of generating a model

Modelling refers to the process of generating a model as a conceptual representation of some phenomenon. Typically a model will refer only to some aspects of the phenomenon in question, and two models of the same phenomenon may be essentially different, that is in which the difference is more than just a simple renaming. This may be due to differing requirements of the model's end users or to conceptual or aesthetic differences by the modellers and decisions made during the modelling process. Aesthetic considerations that may influence the structure of a model might be the modeller's preference for a reduced ontology, preferences regarding probabilistic models vis-a-vis deterministic ones, discrete vs continuous time etc. For this reason users of a model need to understand the model's original purpose and the assumptions of its validity[citation needed].

The process of evaluating a model

A model is evaluated first and foremost by its consistency to empirical data; any model inconsistent with reproducible observations must be modified or rejected. However, a fit to empirical data alone is not sufficient for a model to be accepted as valid. Other factors important in evaluating a model include:[citation needed]
  • Ability to explain past observations
  • Ability to predict future observations
  • Cost of use, especially in combination with other models
  • Refutability, enabling estimation of the degree of confidence in the model
  • Simplicity, or even aesthetic appeal
People may attempt to quantify the evaluation of a model using a utility function.


Visualization is any technique for creating images, diagrams, or animations to communicate a message. Visualization through visual imagery has been an effective way to communicate both abstract and concrete ideas since the dawn of man. Examples from history include cave paintings, Egyptian hieroglyphs, Greek geometry, and Leonardo da Vinci's revolutionary methods of technical drawing for engineering and scientific purposes.

Types of scientific modelling

Business process modelling

Abstraction for Business process modelling [5]

In business process modelling the enterprise process model is often referred to as the business process model. Process models are core concepts in the discipline of process engineering. Process models are:

  • Processes of the same nature that are classified together into a model.
  • A description of a process at the type level.
  • Since the process model is at the type level, a process is an instantiation of it.

The same process model is used repeatedly for the development of many applications and thus, has many instantiations.

One possible use of a process model is to prescribe how things must/should/could be done in contrast to the process itself which is really what happens. A process model is roughly an anticipation of what the process will look like. What the process shall be will be determined during actual system development.[6]

Other types


Modelling and Simulation

One application of scientific modelling is the field of "Modelling and Simulation", generally referred to as "M&S".[7] M&S has a spectrum of applications which range from concept development and analysis, through experimentation, measurement and verification, to disposal analysis. Projects and programs may use hundreds of different simulations, simulators and model analysis tools.

Example of the integrated use of Modelling and Simulation in Defence life cycle management. The modelling and simulation in this image is represented in the center of the image with the three containers.[3]

The figure shows how Modelling and Simulation is used as a central part of an integrated program in a Defence capability development process.[3]

See also


  1. ^ edited by Hans Freudenthal (1951), The Concept and the Role of the Model in Mathematics and Natural and Social Sciences, p. 8-9
  2. ^ C. West Churchman, The Systems Approach, New York: Dell publishing, 1968, p.61
  3. ^ a b c Systems Engineering Fundamentals. Defense Acquisition University Press, 2003.
  4. ^ Pullan, Wendy (2000). Structure. Cambridge: Cambridge University Press. ISBN 0521782589. 
  5. ^ Colette Rolland (1993). "Modeling the Requirements Engineering Process." in: 3rd European-Japanese Seminar on Information Modelling and Knowledge Bases, Budapest, Hungary, June 1993.
  6. ^ C. Rolland and C. Thanos Pernici (1998). "A Comprehensive View of Process Engineering". In: Proceedings of the 10th International Conference CAiSE'98, B. Lecture Notes in Computer Science 1413, Pisa, Italy, Springer, June 1998.
  7. ^ Because "Modeling and Simulation" is frequently taught in male dominated undergraduate environments, this field of application is deliberately named "Modeling and Simulation", rather than "Simulation and Modeling", to avoid distractions which may arise due to any possible association with the negative connotations of S&M.[citation needed]

Further reading

Nowadays there are some 40 magazines about scientific modelling which offer all kinds of international forums. Since the 1960s there is a strong growing amount of books and magazines about specific forms of scientific modelling. There is also a lot of discussion about scientific modelling in the philosophy-of-science literature. A selection:

  • C. West Churchman (1968). The Systems Approach, New York: Dell Publishing.
  • Rainer Hegselmann, Ulrich Müller and Klaus Troitzsch (eds.) (1996). Modelling and Simulation in the Social Sciences from the Philosophy of Science Point of View. Theory and Decision Library. Dordrecht: Kluwer.
  • Paul Humphreys (2004). Extending Ourselves: Computational Science, Empiricism, and Scientific Method. Oxford: Oxford University Press.
  • Johannes Lenhard, Günter Küppers and Terry Shinn (Eds.) (2006) "Simulation: Pragmatic Constructions of Reality", Springer Berlin.
  • Fritz Rohrlich (1990). "Computer Simulations in the Physical Sciences". In: Proceedings of the Philosophy of Science Association, Vol. 2, edited by Arthur Fine et al., 507-518. East Lansing: The Philosophy of Science Association.
  • Rainer Schnell (1990). "Computersimulation und Theoriebildung in den Sozialwissenschaften". In: Kölner Zeitschrift für Soziologie und Sozialpsychologie 1, 109-128.
  • Sergio Sismondo and Snait Gissis (eds.) (1999). Modeling and Simulation. Special Issue of Science in Context 12.
  • Eric Winsberg (2001). "Simulations, Models and Theories: Complex Physical Systems and their Representations". In: Philosophy of Science 68 (Proceedings): 442-454.
  • Eric Winsberg (2003). "Simulated Experiments: Methodology for a Virtual World". In: Philosophy of Science 70: 105–125.

External links


Up to date as of January 14, 2010

From Wikiquote

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.



  • We have no idea about the 'real' nature of things ... The function of modeling is to arrive at descriptions which are useful.
  • The role of conceptual modelling in information systems development during all these decades is seen as an ap-proach for capturing fuzzy, ill-defined, informal "real-world" descriptions and user requirements, and then transforming them to formal, in some sense com-plete, 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
  • "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... "
    • Neil A. Campbell, Biology 2nd ed., 1990, Benjamin/Cummings, p. 434
  • 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.
  • 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.
  • Start by eliminating the possibility of empty models by praying that Heaven will no longer put this invention of the Devil in our way. To do this, we add a constant c to our language ...
    • Proof of the compactness theorem by Henkin's method
    • Bruno Poizat and M. Klein, A Course in Model Theory: An Introduction to Contemporary Mathematical Logic, unidentified edition, page 52
  • 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.


See also

External links

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