Scientific modelling

From Wikipedia, the free encyclopedia
  (Redirected from Scientific models)
Jump to: navigation, search
Example of scientific modelling. A schematic of chemical and transport processes related to atmospheric composition.

Scientific modelling is a scientific activity, the aim of which is to make a particular part or feature of the world easier to understand, define, quantify, visualize, or simulate. It requires selecting and identifying relevant aspects of a situation in the real world and then using different types of models for different aims, such as conceptual models to better understand, operational models to operationalize, mathematical models to quantify, and graphical models to visualize the subject. Modelling is an essential and inseparable part of scientific activity, and many scientific disciplines have their own ideas about specific types of modelling.[1][2]

There is also an increasing attention to scientific modelling[3] in fields such as philosophy of science, systems theory, and knowledge visualization. There is growing collection of methods, techniques and meta-theory about all kinds of specialized scientific modelling.

Overview[edit]

MathModel.svg

A scientific model seeks to represent empirical objects, phenomena, and physical processes in a logical and objective way. All models are in simulacra, that is, simplified reflections of reality, but, despite their inherent falsity, they are nevertheless extremely useful.[4] Building and disputing models is fundamental to the scientific enterprise. Complete and true representation may be impossible (see non-representational theory), but scientific debate often concerns which is the better model for a given task, e.g., which is the more accurate climate model for seasonal forecasting.[5]

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 that will not produce theoretical consequences that are contrary to what is found in reality. Predictions or other statements drawn from such a formal system mirror or map the real world only insofar as these scientific models are true.[6] [7]

For the scientist, a model is also a way in which the human thought processes can be amplified.[8] For instance, 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. Such computer models are in silico. Other types of scientific model are in vivo (living models, such as laboratory rats) and in vitro (in glassware, such as tissue culture).[9]

Scientific modelling basics[edit]

Modelling as a substitute for direct measurement and experimentation[edit]

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 Scientific Method) will always be more reliable than modelled estimates of outcomes.

Simulation[edit]

A simulation is the implementation of a model. A steady state simulation provides information about the system at a specific instant in time (usually at equilibrium, if such a state exists). A dynamic simulation provides information over time. A simulation brings a model to life and shows how a particular object or phenomenon will behave. Such a simulation can be useful for testing, analysis, or training in those cases where real-world systems or concepts can be represented by models.[10]

Structure[edit]

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.[11]

Systems[edit]

A system is a set of interacting or interdependent entities, real or abstract, forming an integrated whole. In general, a system is a construct or collection of different elements that together can produce results not obtainable by the elements alone.[12] 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. There are two types of system models: 1) discrete in which the variables change instantaneously at separate points in time and, 2) continuous where the state variables change continuously with respect to time.[13]

The process of generating a model[edit]

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 to say that the difference or differences between them is more than just a simple renaming of components.

Such differences may be due to differing requirements of the model's end users, or to conceptual or aesthetic differences among the modellers and to contingent 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 made that are pertinent to its validity[citation needed].

Building a model requires abstraction. Assumptions are used in modeling in order to specify the domain of application of the model. For example, the special theory of relativity assumes an inertial frame of reference. This assumption was contextualized and further explained by the general theory of relativity. A model makes accurate predictions when its assumptions are valid, and does not make accurate predictions when its assumptions do not hold. Such assumptions are often the point with which older theories are succeeded by new ones (the general theory of relativity works in non-inertial reference frames as well).

The term "assumption" is actually broader than its standard use, etymologically speaking. The Oxford English Dictionary (OED) and online Wiktionary indicate its Latin source as assumere ("accept, to take to oneself, adopt, usurp"), which is a conjunction of ad- ("to, towards, at") and sumere (to take). The root survives, with shifted meanings, in the Italian sumere and Spanish sumir. In the OED, "assume" has the senses of (i) “investing oneself with (an attribute), ” (ii) “to undertake” (especially in Law), (iii) “to take to oneself in appearance only, to pretend to possess,” and (iv) “to suppose a thing to be.” Thus, "assumption" connotes other associations than the contemporary standard sense of “that which is assumed or taken for granted; a supposition, postulate,” and deserves a broader analysis in the philosophy of science.[citation needed]

The process of evaluating a model[edit]

A model is evaluated first and foremost by its consistency to empirical data; any model inconsistent with reproducible observations must be modified or rejected. One way to modify the model is by restricting the domain over which it is credited with having high validity. A case in point is Newtonian physics, which is highly useful except for the very small, the very fast, and the very massive phenomena of the universe. 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[edit]

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[edit]

Business process modelling[edit]

Abstraction for Business process modelling[14]

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.[15]

Other types[edit]

Applications[edit]

Modelling and Simulation[edit]

One application of scientific modelling is the field of "Modelling and Simulation", generally referred to as "M&S". 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.[10]

The figure shows how Modelling and Simulation is used as a central part of an integrated program in a Defence capability development process.[10]

See also[edit]

References[edit]

  1. ^ Cartwright, Nancy. 1983. How the Laws of Physics Lie. Oxford University Press
  2. ^ Hacking, Ian. 1983. Representing and Intervening. Introductory Topics in the Philosophy of Natural Science. Cambridge University Press
  3. ^ Frigg and Hartmann (2009) state: "Philosophers are acknowledging the importance of models with increasing attention and are probing the assorted roles that models play in scientific practice". Source: Frigg, Roman and Hartmann, Stephan, "Models in Science", The Stanford Encyclopedia of Philosophy (Summer 2009 Edition), Edward N. Zalta (ed.), (source)
  4. ^ Box, George E.P. & Draper, N.R. (1987). [Empirical Model-Building and Response Surfaces.] Wiley. p. 424
  5. ^ Hagedorn, R. et al. (2005) http://www.ecmwf.int/staff/paco_doblas/abstr/tellus05_1.pdf Tellus 57A:219-233
  6. ^ Leo Apostel (1961). "Formal study of models". In: The Concept and the Role of the Model in Mathematics and Natural and Social. Edited by Hans Freudenthal. Springer. p. 8-9 (Source)],
  7. ^ Ritchey, T. (2012) Outline for a Morphology of Modelling Methods: Contribution to a General Theory of Modelling
  8. ^ C. West Churchman, The Systems Approach, New York: Dell publishing, 1968, p.61
  9. ^ Griffiths, E. C. (2010) What is a model?
  10. ^ a b c Systems Engineering Fundamentals. Defense Acquisition University Press, 2003.
  11. ^ Pullan, Wendy (2000). Structure. Cambridge: Cambridge University Press. ISBN 0-521-78258-9. 
  12. ^ Fishwick PA. (1995). Simulation Model Design and Execution: Building Digital Worlds. Upper Saddle River, NJ: Prentice Hall.
  13. ^ Sokolowski, J.A.,Banks, C.M.(2009). Principles of Modelling and Simulation. Hoboken, NJ: John Wiley and Sons.
  14. ^ Colette Rolland (1993). "Modeling the Requirements Engineering Process." in: 3rd European-Japanese Seminar on Information Modelling and Knowledge Bases, Budapest, Hungary, June 1993.
  15. ^ 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.

Further reading[edit]

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:

  • 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.
  • Tom Ritchey (2012). "Outline for a Morphology of Modelling Methods: Contribution to a General Theory of Modelling". In: Acta Morphologica Generalis, Vol 1. No 1. pp. 1-20.
  • 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[edit]