Abstract
Many modern applications need evaluation of statements, the truth values “true” and “false” alone may not suffice, a statement can be neither true nor false, it may be true (or false) “in a certain sense.” They also need modeling of linguistic expressions and of fuzzy situations. “Binary thinking” does not suffice in many cases. Moreover, the choice of methods might better be problem driven, depending, for example, if we better use a pessimistic or an optimistic reasoning. Here is a brief introduction of how we can choose tools that are appropriate for mathematically modeling this kind of problems.
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Notes
- 1.
A terrible name since not the mathematics is fuzzy but the situations that are modeled.
References
Brüggemann R, Kerber A, Restrepo G (2011) Ranking objects using fuzzy orders with an application to refrigerants. MATCH Commun Math Comput Chem 66:581–603
Klir GJ, Yuan B (1995) Fuzzy sets and fuzzy logic (theory and applications). Prentice Hall, Englewood Cliffs
Restrepo G (2008) Assessment of the environmental acceptability of refrigerants by discrete mathematics: cluster analysis and Hasse diagram technique. Doctoral thesis, University of Bayreuth, Germany. Available via the address http://opus.ub.uni-bayreuth.de/frontdoor.php?source_opus=393\&la=de
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Thanks are due to the referees for helpful hints.
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© 2014 Springer Science+Business Media New York
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Kerber, A. (2014). Evaluation as a General Approach to Problem Driven Mathematical Modeling. In: Brüggemann, R., Carlsen, L., Wittmann, J. (eds) Multi-indicator Systems and Modelling in Partial Order. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8223-9_1
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DOI: https://doi.org/10.1007/978-1-4614-8223-9_1
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