IKBFU's Vestnik

2019 Issue №01

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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.