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  5. Modeling in measurement science: from the prescriptive model to the descriptive model for the practical implementation of the former

Modeling in measurement science: from the prescriptive model to the descriptive model for the practical implementation of the former

F. Paveze
Системи обробки інформації. — 2018. — № 4(155). – С. 134-139.
UDK 006.91
Article language: english
Annotations languages:


Annotation: In measurement science the object of a measurement is called “measurand”. It is defined in VIM:2012 clause 2.3 as “quantity intended to be measured” (but in GUM:1995, B.2.9 it is defined instead according to VIM:1993 clause 2.6 “particular quantity subject to measurement”. In VIM:2012, NOTE 1 to clause 2.3 specifies “the specification of a measurand requires … description [i.e., a model] of the state of the phenomenon, body, or substance”. The concept of measurand should be shared by the relevant Community, because the same measurand is supposed to be the object of replicated measures that must be comparable, i.e., it should be recognised as a quantity having a current recognisable meaning for the community. In the dialect of the science philosophers, this means that it should be projected into a “social framework”. Also in the scientific frame this means that the measurand model must be of the “prescriptive” type, meaning “giving directions or injunctions”— not always meaning “physical model”. The design of an experiment (DoE) must start from this initial conceptual model of the measurand, “socially shared”, not from the building up of the descriptive model (often called the “experimental model”) of the measuring system, which is specific to each measurement arrangement. Also each measuring system must be modelled, based on the specific solutions that are chosen in order to implement the prescription in each experiment. The initial conceptual model, being independent on any specific experimental implementation, is clearly a highly idealised one. It does not even allow appreciating the experimental difficulties and compromise (which are graduated depending on the target uncertainty). They arise from three categories of sources indicated in the VIM:2012: 1) the phenomenon, body, or substance; 2) the measuring system; 3) the conditions under which the measurement is carried out. A corresponding model must describe the measurement conditions (often called “physical”, “experimental” or “observation” model—here it does not necessarily correspond to any of them). The paper discusses the non-simple roadmap bringing from the prescriptive model to the descriptive one.


Keywords: measurand, model of the measurand, prescriptive model, descriptive model, measurement conditions

References

1. BIPM (2008), International Vocabulary of Metrology – Basic and General Concepts and Associated Terms (VIM), 3rd ed., available at: www.bipm.org/en/publications/ guides.
2. International Organization for Standardization (1993), Guide to the Expression of Uncertainty in Measurement (GUM), Genève, Switzerland.
3. BIPM (1993), International Vocabulary of Metrology – Basic and General Concepts and Associated Terms (VIM), 2rd ed.
4. International Organization for Standardization (2006), Statistics – Vocabulary and Symbols ISO 3534:2006, 3rd ed., Genève, Switzerland.
5. De Courtenay, N. and Grégis F. (2017), The evaluation of measurement uncertainty and its epistemological ramifica-tions, Studies in History and Philosophy of Science, Vol. 65-66, pp. 21-32. https://doi.org/10.1016/j.shpsa.2017.05.003.
6. The Free Dictionary, available at: https://www.thefreedictionary.com/prescriptiveness.
7. Wikipedia, available at: http://wikipedia.org.
8. Pavese, F. (2018), On the classification into random and systematic effects. Advanced Mathematical and Computational Tools in Metrology and Testing XI, http://dx.doi.org/10.1142/9789813274303_0006.

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Reference:
Paveze, F. (2018), Modeling in measurement science: from the prescriptive model to the descriptive model for the practical implementation of the former, Information Processing Systems, Vol. 4(155), pp. 134-139. https://doi.org/10.30748/soi.2018.155.19.