Software System for Visualization and Checking the Consistency of Experts’ Evaluative Knowledge
https://doi.org/10.25205/1818-7900-2023-21-1-32-45
Abstract
Expert evaluations are used everywhere and in solving a wide range of problems, but often there is a problem of inconsistency in the set of expert evaluations. In this paper, we propose an algorithm for checking the evaluative expert knowledge for consistency. As a result of the algorithm, we not only get an answer about whether the data is consistent or not, but also visualize the original data in the form of a tree. If the introduced expert evaluations are inconsistent, the constructed tree “highlights” the edges in which there is an inconsistency. The algorithm also offers two alternative ways of resolving evaluation inconsistencies. The article describes a software system developed on the basis of this algorithm.
About the Authors
E. D. Malaeva
Novosibirsk State University
Russian Federation
Russian Federation
Elena D. Malaeva, student
Novosibirsk
G. E. Yakhyaeva
Novosibirsk State University
Russian Federation
Russian Federation
Gulnara E. Yakhyaeva, Candidate of Physical and Mathematical Sciences, Associate Professor
Department of General Informatics
Novosibirsk
References
1. Gutsykova S. Method of expert evaluations. Theory and practice. Moscow: Institute of Psychology RAS, 2011. (in Russ.)
2. Palchunov D. E., Tishkovsky D. E., Tishkovskaya S. V., Yakhyaeva G. E. Combining logical and statistical rule reasoning and verification for medical applications. 2017 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON), Novosibirsk, Russia, 2017, p. 309-313. DOI: 10.1109/SIBIRCON.2017.8109895.
3. Danelyan T. Y. Formal methods of expert estimations // Statistics and Economics. 2015. No. 1. Pp. 183–187 (in Russ.) DOI: 10.21686/2500-3925-2015-1-183-187
4. Burtseva T. A., Zueva I. A. Directions of perfection of methods of monitoring the implementation of development strategies of regions in terms of digital information // Vestnik Moskovskogo universiteta imeni S. Yu. Vitte. Seriya 1: Ekonomika i upravlenie. 2018. Vol. 27, no. 4. Pp. 43–50. (in Russ.) URL: https://elibrary.ru/item.asp?id=36961402&ysclid=llupce77vy754071911.
5. Volkovskaya V. M., Zakharov V. V. Method of collective idea generation // Innovatsionnye protsessy v sfere informatsionnykh tekhnologii i sovremennogo obrazovaniya v regionakh Rossii : sbornik nauchnykh statei po materialam Vserossiiskoi nauchno-prakticheskoi konferentsii. Stavropol, November 16–17, 2020. Pp. 85–89. (in Russ.)
6. Stonchyute K. E., Gurbo A. A., Puzyrevskaya A. A. Methods of expert evaluations: Delphi method // Scientific knowledge of modernity. 2021. Vol. 53, no. 5. Pp. 16–19. (in Russ.)
7. Datenko V. I. Oriented graphs application for the prediction of the outcomes in multicomponent task // Prikladnye informatsionnye sistemy v tekhnologiyakh nazemnogo transporta (mashinostroenie) : materialy Vserossiiskoi nauchno-prakticheskoi konferentsii s mezhdunarodnym uchastiem, Taganrog, December 20–21, 2018. Pp. 47–50. (in Russ.)
8. Evstigneeva E. O., Novikova I. V. The method of expert evaluations in forecasting // Problemy i perspektivy razvitiya eksperimental’noi nauki : sbornik statei Mezhdunarodnoi nauchno-prakticheskoi konferentsii, Novosibirsk, November 28, 2019. Pp. 72–74. (in Russ.)
9. Tsugunyan A. M., Kvasko M. A. Methods for making strategic decisions at the micro and macro levels // Sovremennaya mirovaya ekonomika: problemy i perspektivy v epokhu razvitiya tsifrovykh tekhnologii i biotekhnologii : sbornik nauchnykh statei mezhdunarodnoi nauchnoi konferentsii, Moscow, March 29–31, 2019. Pp. 53–55. (in Russ.)
10. Kozenko I. A. The use of expert evaluations in determining consumer preferences // Actual issues of the modern economy. 2018. No. 9. Pp. 287–296. (in Russ.)
11. Demina L. M., Divina T. V. Research of consumer preferences based on expert evaluations. Mocow: MSIU, 2012. (in Russ.)
12. Divina T. V., Petrakova E. A., Vishnevsky M. S. Basic methods of analysis of expert assessments // Economy and business: theory and practice. 2019. No. 7. Pp. 42–44. (in Russ.) DOI: 10.24411/2411-0450-2019-11072
13. Dulesov A. S., Semyonova M. Y. Subject probability in measure detection of object state uncertainty // Fundamental’nye issledovaniya. 2012. No. 3. Pp. 81–86. (in Russ.)
14. Yakhyaeva G. E., Palchunova O. D. Fuzzy models as a formalization of evaluative knowledge // Dvadtsataya Natsional’naya konferentsiya po iskusstvennomu intellektu s mezhdunarodnym uchastiem, KII-2022 : Trudy konferentsii. Moscow, December 21-23, 2022. Pp. 97–109. (in Russ.)
15. Yakhyaeva G. Method for Verifying the Logical Correctness of Experts’ Evaluative Knowledge. 2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON), Yekaterinburg, Russian Federation, 2022, p. 850–854.
16. Palchunov D. E., Yakhyaeva G. E. Fuzzy algebraic systems // Vestnik NSU. Series: Mathematics, mechanics, computer science. 2010. Vol. 10, no. 3. Pp. 76–93. (in Russ.)
17. Il’in V. A., Kim G. D. Linear Algebra and Analytic Geometry. Moscow: Prospekt, 2007. (in Russ.)
18. Yakhyaeva G., Skokova V. Subjective Expert Evaluations in the Model-Theoretic Representation of Object Domain Knowledge. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021, p. 152–165.
Review
For citations:
Malaeva E.D., Yakhyaeva G.E. Software System for Visualization and Checking the Consistency of Experts’ Evaluative Knowledge. Vestnik NSU. Series: Information Technologies. 2023;21(1):32-45. (In Russ.) https://doi.org/10.25205/1818-7900-2023-21-1-32-45
Views:
165
Email this article 