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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 by referencing it to existing and usually commonly accepted knowledge. 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.〔Cartwright, Nancy. 1983. ''How the Laws of Physics Lie''. Oxford University Press〕〔Hacking, Ian. 1983. ''Representing and Intervening. Introductory Topics in the Philosophy of Natural Science''. Cambridge University Press〕 There is also an increasing attention to scientific modelling〔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 ))〕 in fields such as science education, 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 == right 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 that, despite being approximations, can be extremely useful.〔Box, George E.P. & Draper, N.R. (1987). (Model-Building and Response Surfaces. ) ''Wiley''. p. 424〕 Building and disputing models is fundamental to the scientific enterprise. Complete and true representation may be impossible, 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.〔Hagedorn, R. ''et al.'' (2005) (http://www.ecmwf.int/staff/paco_doblas/abstr/tellus05_1.pdf ) ''Tellus'' 57A:219-233〕 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.〔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 ))]'',〕 〔Ritchey, T. (2012) (Outline for a Morphology of Modelling Methods: Contribution to a General Theory of Modelling )〕 For the scientist, a model is also a way in which the human thought processes can be amplified.〔C. West Churchman, ''The Systems Approach'', New York: Dell publishing, 1968, p.61〕 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).〔Griffiths, E. C. (2010) (What is a model? )〕 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Scientific modelling」の詳細全文を読む スポンサード リンク
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