Science, Technologies, Innovations №1(29) 2024, 92-102 p

http://doi.org/10.35668/2520-6524-2024-1-10

Reva O. M. — D. Sc. in Engineering, Full Professor, Head of the electronic government department in the management and administration division of National Aviation University, 1, Lubomir Guzar Ave, Kyiv, Ukraine, 03058;
+38 (067) 238- 31-77; ran54@meta.ua; ORCID: 0000-0002-5954-290X

Kamyshyn V. V. — D. Sc. in Pedagogy, Corresponding Member of the NAES of Ukraine, Director of Ukrainian Institute of Scientific and Technical Expertise and Information, 180, Antonovycha Str., Kyiv, Ukraine, 03150; +38 (044) 521-00-10; kvv@ukrintei.ua; ORCID: 0000-0002-8832-9470

Borsuk S. P. — D. Sc. in Engineering, Associate professor, Head researcher, Ukrainian Institute of Scientific and Technical Expertise and Information, 180, Antonovycha Str., Kyiv, Ukraine, 03150; greyone.ff@gmail.com; ORCID: 0000-0002-7034-7857

Yarotskyi S. V. — Head of Department in the Management and Administration Division of National Aviation University, 1, Lubomir Guzar Ave, Kyiv, Ukraine, 03058; +38 (067) 238-31-77; stas_gas@ua.fm; ORCID: 0000-0003-3934-4647 http://doi.org/10.35668/

Sahanovska L. A. — Senior Lecturer of the Department of Physical and Mathematical Disciplines and Information Technologies in Aviation Systems of the Flight Academy of the National Aviation University, 1, Stepan Choban Str., Kropyvnytskyi, Kirovohrad region, Ukraine, 25005; lora-sag@ukr.net; ORCID: 0000-0002-2560-4383

ENTROPY INDICATORS OF FRAGMENTATION AMONG SPECIALISTS OF THE SIGNIFICANCE OF FEATURES OF INVESTMENT ATTRACTIVENESS OF OBJECTS OF EXPERTISE

Abstract. The systems of preferences of expertise participants are an important indicator of the influence of the human factor on decision making. Their applied aspect lies in the use of an integral assessment of the investment attractiveness of objects of expertise/projects to solve multicriteria problems, as well as the establishment of “compromises” in the requirements for the degree of expression of investment attractiveness features inherent in each object/project. The system of advantages is an ordered series of specified features (n = 18): from more significant (significant, attractive, etc.) to less significant.
The implementation of a multi-step technology and algorithm for identifying and rejecting marginal thoughts, eliminating the “systematic error of the survivor” made it possible to identify four subgroups from the initial sample of experts numbering m = 90 people (mС = 30 people, mН = 12 people, mМ = 11 people, mТ = 6 people) whose internal group consistency of opinions about the significance of features of investment attractiveness satisfies the range of system-information criteria at an unusually high level of significance for human factor studies a = 1 %. It is substantiated that the group system of preferences of members of the mС subgroup should be considered basic. The opinions of marginal experts form a subgroup of mU = 31 people.
The degree to which experts differentiate the significance of features of investment attractiveness in the process of compiling them is determined by the number of “related” ranks and is taken into account when determining the Kendall dispersion coefficient of concordance (agreement). It is proposed to apply the entropy of the fragmentation of features for the same purpose. For each of the m subjects, normalized entropy indicators were determined, which were generalized both for group m and for subgroups mС, mН, mМ, mТ. Using the Student’s test, a statistically probable (a = 1%) agreement between the average entropy indicators was established. Therefore, the criteria for dividing them into subgroups-clusters according to the applied technology for identifying and screening out marginal thoughts and eliminating the “systematic survivor bias” are important.
The paradoxical nature of the research hypothesis has been established, since it is logical to assume that the more competent the expert, the more strictly he will order the studied features of investment attractiveness, and therefore the less entropy of ranks should then be observed in his system of advantages. On the other hand, the same high level of expert competence can lead to his conscious caution in ordering the studied traits, and therefore the use of a larger number of “connected (middle)” ranks, which will contribute to an increase in their entropy.
For the mС subgroup, recognized as the basic one, it was found that greater entropy is characteristic of a more significant feature of the investment attractiveness of the objects of examination. The well-known approach to determining entropy concordance coefficients did not turn out to be effective under the conditions of our research and needs further development.
Taking into account the issues highlighted, further steps are outlined for the development of information-entropy technologies for expert research.

Keywords: features of investment attractiveness of the expertise objects, recognition of significance, systems of advantages, entropy, competence of experts.

References

  1. Horodnichenko, Yu., Solohub, I., & Mauro, Beatris Veder di (Eds.) (2022). Vidbudova Ukrainy: pryntsypy ta polityka. Paryzkyi zvit I [Reconstruction of Ukraine: principles and politics]. Center Economic Policy Reserche. 508 p. Retrieved from: https://cepr.org/system/files/2022­12/reconstruction%20book_Ukrainian_0.pdf. [in Ukr.].
  2. Reva, O., Borsuk, S., Kamyshyn, V. & Nevynitsyn, A. (2023). Development of a Fuzzy Approach for Consistency Determination of ATC Students Opinions During Aircraft Flight Norms Violation Hazard Identification. Fuzzy Systems and Data Mining IX. Proceeding of FSDM 2023. P. 77–81.
  3. Svitlychna, T. I., & Dril, N. V. (2010). Konspekt lektsii z dystsypliny “Prohnozuvannia” [Synopsis of lectures on the discipline “Forecasting”]. Kharkiv, 112 p. [in Ukr.].
  4. Samokhvalov, Yu. Ya., & Naumenko, E. M. (2007). Ekspertnoe otsenyvanye: Metodycheskyi aspekt [Expert evaluation: methodical aspect]. Kyiv, 362 p. [in Russ.].
  5. Hnatiienko, H. M., & Snytiuk, V. Ye. (2008). Ekspertni tekhnolohii pryiniattia rishen [Expert decision­making technologies]. Kyiv, 444 p. [in Ukr.].
  6. Novosad, V. P., Seliverstov, R. H., & Artym, I. I. (2009). Kilkisni metody ekspertnoho otsiniuvannia [uantitative methods of expert assessment]. Kyiv, 36 p. [in Ukr.].
  7. Velychko, O. M., Kolomiiets, L. V., Hordiienko, T. B., Shevtsov, A. H., Karpenko, S. R., & Haber, A. A. (2015). Hrupove ekspertne otsiniuvannia ta kompetentnist ekspertiv [Group expert evaluation and competence of experts]. Odesa, 286 p. [in Ukr.].
  8. Iaroshchuk, L. D. (Ed.). (2022). Ekspertni metody v avtomatyzovanykh systemakh keruvannia: Formuvannia ta napriamy vykorystannia ekspertnykh znan [Expert methods in automated control systems: Formation and directions of use of expert knowledge]. Kyiv, 43 p. [in Ukr.].
  9. Davydenko, Ye. O. (2012). Formalizatsiia protsesu formuvannia skladu ekspertnoi hrupy dlia analizu ryzykiv IT­proektiv [Formalization of the process of formation of the expert group for risk analysis of IT projects]. Visnyk Khersonskoho natsionalnoho tekhnichnoho universytetu [Bulletin of the Kherson National Technical University].1 (44). P. 163–169. [in Ukr.].
  10. Tsyba, Ye. V., & Arkhypov, O. Ye. (2015). Identyfikatsiia modeli kompetentnosti ekspertiv za danymy bahatoobiektnoi ekspertyzy z riznymy rozpodilamy zashumlennia [Identification of expert competence models based on the data of multi­objective examination with different distributions of noise]. Teoretychni i prykladni problemy fizyky, matematyky ta informatyky [Theoretical and applied problems of physics, mathematics and informatics]. Kyiv, P. 213–215. [in Ukr.].
  11. Pievtsov, H. V., Usachova, O. A., Patsek, P., Romaniuk, A. O. (2020). Kombinovana metodyka otsiniuvannia kompetentnosti ekspertiv pry vybori stsenariiu orhanizatsii informatsiino­psykholohichnoho vplyvu [The combined method of assessing the competence of experts when choosing the scenario of the organization of information and psychological influence]. Nauka i tekhnika Povitrianykh Syl Zbroinykh Syl Ukrainy [Science and technology of the Air Force of the Armed Forces of Ukraine]. 2 (39), 24–36.https://doi.org/10.30748/nitps.2020.39.03. [in Ukr.].
  12. Arkhypov, A. E., Arkhypova, S. A., & Nosok, S. A. (2007). Entropyinui podkhod k otsenyvanyiu sohlasovannosty suzhdenyi ekspertov [Entropy approach to the evaluation of the consistency of experts]. ASAU. 10 (30). P. 8–14. [in Ukr.].
  13. Kasianov, V. A. (2007). Sub’ektyvnui analyz [Subjective analysis]. Kyiv, 512 p. [in Russ.].
  14. Reva, O. M., Borsuk, S. P., Zasanska S. V., & Yarotskyi, S. V. (2021). Obgruntuvannia napriamiv vdoskonalennia ekspertnykh tekhnolohii v doslidzhenniakh liudskoho chynnyka [Justification of directions for improvement of expert technologies in human factors research]. Suchasni informatsiini ta innovatsiini tekhnolohii na transporti (MINNT–2021) [Modern information and innovative technologies in transport (MINNT­2021)]. Kherson. P. 49–54. [in Ukr.].
  15. Yarotskyi, S. V. (2019). Pilotna otsinka stavlennia ekspertiv do znachushchosti kharakternykh rys innovatsiinoi pryvablyvosti obiektiv intelektualnoi vlasnosti [Pilot assessment of the attitude of experts to the significance of the characteristic features of the innovative attractiveness of intellectual property objects]. Aviatsiino­kosmichna tekhnika ta tekhnolohiia [Aviation and space technology and technology]. 4. 112–121. https://doi.org/10.32620/aktt.2021.4sup2.15 [in Ukr.].
  16. Zadeh, L. A. (1973). Outline of a new approach to the analyses of complex system and decision processes. IEEE Trans. System Man Cybernetics. Vol. 3, No. 1. P. 28–44.
  17. Zadeh, L. A. (1976). A fuzzy algorithmic approach to the definition of complex or imprecise concepts. Intern. Journal Man­Machin Studies. Vol. 8. No. 3. P. 249–291.
  18. Reva, O. M., Kamyshyn, V. V., Borsuk, S. P., Yarotskyi, S. V., & Sahanovska, L. A. (2023). Metodolohiia systemno­informatsiinoi kvalimetrii investytsiinoi pryvablyvosti obiektiv ekspertyzy [Methodology of the system­informational qualimetry of the investment attractiveness of objects of examination]. Kyiv, 150 p. [in Ukr.].
  19. Voloshyn, A. (2007). Decision­Making Support Systems as Personal Intellectual Device of a Decision­Maker. Information: Technologies & Knowledge. Vol. 1. No. 2. P. 159–162.
  20. Maliarets, L. M., & Minienkova, O. V. (2017). Vyrishennia problem bahatokryterialnosti v otsintsi diialnosti pidpryiemstva na osnovi metodiv bahatokryterialnoi optymizatsii [Solving the problems of multicriteria in the assessment of enterprise activity based on methods of multicriteria optimization]. Problemy ekonomiky [Problems of economics]. 1. 421–427. [in Ukr.].
  21. Reva, O. M., Kamyshyn, V. V., Sahanovska, L. A., & Yarotskyi, S. V. (2022). Teoretychni osnovy modeliuvannia “kompromisu” u vymohakh do vsebichnoho rozvytku obdarovanosti tykh, khto navchaietsia [Theoretical bases of modeling “compromise” in the requirements for comprehensive development of giftedness of those who study]. Osvita ta rozvytok obdarovanoi osobystosti [Education and development of a gifted personality]. 3 (86). 20–27. [in Ukr.].
  22. Reva, O., Kamyshyn, V., Borsuk, S., Yarotskyii S., & Avramchuk, B. (2023). Eliminating “systematic survivorship bias”; in the attitude of specialists to the significance of investment attractive features of examined objects. Eastern­European Journal of Enterprise Technologies. Vol. 6. 13 (126), P. 54–64. https://doi.org/10.15587/1729­4061.2023.292875. [in Ukr.].
  23. Reva, O., & Kamyshyn, V. (2022). Systemno­informatsiine obgruntuvannia kryteriiv uzghodzhenosti system perevah uchasnykiv osvitno­vykhovnoho protsesu [System and information justification of the criteria of consistency of preference systems of participants in the educational process]. Pedahohichni innovatsii: idei, realii, perspektyvy [Pedagogical innovations: ideas, realities, perspectives]. 1 (28). 70–78. https:/doi.org/10.32405/2413­4139­2020­1(28)­70­78. [in Ukr.].
  24. Reva, O. M., Kamyshyn, V. V., Kyrychenko, K. V.,  Yarotskyi, S. V., & Sahanovska, L. A. (2023). Formuvannia spektru systemno­infomatsiinykh kryteriiv uzghodzhenosti ekspertnykh dumok [Formation of the spectrum of system­informational criteria for consistency of expert opinions]. Nauka, tekhnolohii, innovatsii [Science, technologies, innovations]. 2 (26), 26–39. http://doi.org/10.35668/2520­6524­2023­2­04/. [in Ukr.].
  25. Claude E. Shannon, & Warren Weaver (1963). The Mathematical Theory of Communication. Univ of Illinois Press.
  26. Kaufman, A. (1977). Introduction a la théorie des sous­ensembles flous. Paris, 334 p.
  27. Freik, N. D., & Ilkiv, N. B. (2011). Entropiia u pohliadakh pryrodnychykh nauk [Entropy in the views of natural sciences]. Fizyka i khimiia tverdoho tila [Physics and chemistry of solids]. Vol. 12. No. 3. P. 809–814. [in Ukr.].
  28. Serdiuk, S. M. (2014). Erhonomichni pytannia proektuvannia liudyno­mashynnykh system [Ergonomic issues of designing man­machine systems]. Zaporizhzhia, 334 p. [in Ukr.].
  29. Partyko, Z. V. (2001). Obrazna kontseptsiia teorii informatsii — Image conception of the information theory. Lviv, 132 p.
  30. Kozhevnykov, V. L., & Kozhevnykov, A. V. (2011). Teoriia informatsii ta koduvannia [Theory of information and coding]. Dnipropetrovsk, 108 p. [in Ukr.].
  31. Podlevskyi, B. M., Rykaliuk, R. Ye. (2020). Teoriia informatsii v zadachakh [Information theory in tasks]. Kyiv, 271 p. [in Ukr.].
  32. Müller, P. Heinz, Neumann, Peter, &Storm, Regina. Tafeln der matematischen Statistik. Verlag: VEB Fachbuchverlag, Leipzig, 1979. 275 p. [in Ukr.].
  33. Tarasov, V. A., Herasymov, B. M., Levyn, Y. A., & Korneichuk, V. A. (2007). Intellektualnue systemu podderzhki priniatiia reshenii: teoriia, syntez, effektyvnost [Intelligent systems of support for decision­making: Theory, synthesis, effectiveness]. Kyiv, 336 p. [in Russ.].