Theoretical analysis of fuzzy logic and Q. E. method in economics
- Pages
- 59-68
Abstract
This paper analyzes the key elements of fuzzy logic and showes that through rational, behavioral economics and neo-classical economics it is possible to develop models using the Q. E. methodology. Therefore, it is plausible to apply contemporaneous Q. E. methodology in combination with the rationality and the behavioral approach. The fuzzy logic and the generator is the source of this mechanism for the production of the appropriate models.
Reference
1. Calvin minds in the making. URL: https://www.calvin.edu/~pribeiro/ othrlnks/Fuzzy/history.htm (дата обращения: 22.04.2019).
2. Challoumis C. Methods of Controlled Transactions and the Behavior of Companies According to the Public and Tax Policy // Economics. 2018. № 6(1). Р. 33—43. doi: https://doi.org/10.2478/eoik-2018—0003.
3. Challoumis C. The arm's length principle and the fixed length principle economic analysis // World Scientific News. 2019. Vol. 115. P. 207—217.
4. Challoumis C. Analysis of Axiomatic Methods in Economics. URL: https:// ssrn.com/abstract=3168087 (дата обращения: 11.02.2019).
5. Challoumis C. Fuzzy Logic Concepts in Economics. URL: https://papers. ssrn.com/sol3/papers.cfm?abstract_id=3185732 (дата обращения: 11.02.2019).
6. Challoumis C. Multiple Axiomatics Method Through the Q. E. Methodolog. URL: https://ssrn.com/abstract=3223642(дата обращения: 11.02.2019).
7. Challoumis C. Quantification of Everything (a Methodology for Quantification of Quality Data with Application and to Social and Theoretical Sciences). URL: https://ssrn.com/abstract=3136014 (дата обращения: 11.02.2019).
8. Colubi A., Gonzalez-Rodriguez G. Fuzziness in data analysis: Towards accuracy and robustness // Fuzzy Sets and Systems. 2015. Vol. 281. P. 260—271. doi: https://doi.org/10.1016/j.fss.2015.05.007.
9. Godo L., Gottwald S. Fuzzy sets and formal logics // Fuzzy Sets and Systems. 2015. Vol. 281. P. 44—60. doi: https://doi.org/10.1016/j.fss.2015.06.021.
10. Holčapek M., Perfilieva I., Novák V., Kreinovich V. Necessary and sufficient conditions for generalized uniform fuzzy partitions // Fuzzy Sets and Systems. 2015. Vol. 277. P. 97—121. doi: https://doi.org/10.1016/j.fss.2014.10.017.
11. Kacprzyk J., Zadrozny S., De Tré G. Fuzziness in database management systems: Half a century of developments and future prospects // Fuzzy Sets and Systems. 2015. Vol. 281. P. 300—307. doi: https://doi.org/10.1016/j.fss.2015.06.011.
12. Klawonn F., Kruse R., Winkler R. Fuzzy clustering: More than just fuzzification // Fuzzy Sets and Systems. 2015. Vol. 281. P. 272—279. doi: https://doi. org/10.1016/j.fss.2015.06.024.
13. Nasibov E., Atilgan C., Berberler M. E., Nasiboglu R. Fuzzy joint points based clustering algorithms for large data sets // Fuzzy Sets and Systems. 2015. Vol. 270. P. 111—126. doi: https://doi.org/10.1016/j.fss.2014.08.004.
14. Pedrycz W. From fuzzy data analysis and fuzzy regression to granular fuzzy data analysis // Fuzzy Sets and Systems. 2015. Vol. 274. P. 12—17. doi: https://doi. org/10.1016/j.fss.2014.04.017.
15. Ross T. J. Fuzzy Logic With Engineering Applications. Chichester, 2017.
16. Stanford University. An introduction to philosophy. URL: http://web.stanford.edu/~bobonich/glances%20ahead/IV.excluded.middle.html (дата обращения: 11.02.2019).
17. Sy Dzung Nguyen, Seung-Bok Choi. Design of a new adaptive neuro-fuzzy inference system based on a solution for clustering in a data potential field // Fuzzy Sets and Systems. 2015. Vol. 279. P. 64—86. doi: https://doi.org/10.1016/j.fss. 2015.02.012.
18. Trillas E. Glimpsing at guessing // Fuzzy Sets and Systems. 2015. Vol. 281. P. 32—43. doi: https://doi.org/10.1016/j.fss.2015.06.026.
19. Verdegay J. L. Progress on Fuzzy Mathematical Programming: A personal perspective // Fuzzy Sets and Systems. 2015. Vol. 281. P. 219—226. doi: https:// doi.org/10.1016/j.fss.2015.08.023.