Models of information situations of decision-making under conditions of risk, uncertainty, and fuzzy information
Abstract
The relevance is conditioned by the need to create mathematical support for a conceptual approach that would allow systematising information situations of decision-making under conditions of risk, uncertainty, fuzzy information, and provide a reasonable choice of methods for solving practical decision-making problems in these situations. The goal was to develop mathematical models of information situations of decision-making under conditions of risk, uncertainty, fuzzy information, and their possible combinations, and to build a model of the decision-making process based on them, which provides for the possibility of applying several methods of choosing alternatives and aggregating their results to improve the efficiency of decision-making. The research methodology was based on a systematic approach using set theory, probability theory, fuzzy set theory, decision theory and methods, and mathematical modelling to formalise information situations of decision-making. Mathematical models of information situations of decision-making under conditions of risk (Risk, R), complete uncertainty (Uncertainty, U), fuzzy information (Fuzzy, F), and possible combinations of situations were developed: R-U, R-F, U-F, R-U-F. The generalised model of information situation of decision-making R-U-F (MISDMRUF) was interpreted as a model of a decision-making problem that formalises the process of choosing an alternative in conditions of simultaneous presence of risk, uncertainty, and fuzzy information. This allowed using existing and creating new decision-making methods depending on the characteristics of the information situation of decisionmaking. A model of the decision-making process under conditions of risk, uncertainty and fuzzy information (MDMPRUF) was proposed, its tasks, main stages of implementation, and key features, in particular, versatility, flexibility, and integrativity, were defined. The results showed that the proposed MISDMRUF and MDMPRUF models were consistent with a number of studies in decision theory, but simultaneously fill in the existing gaps, providing a systematic, integrated approach to classifying information situations of decision-making and selecting decision methods under complex conditions of risk, uncertainty, and fuzzy information. The proposed mathematical models and an appropriate approach to solving decision-making problems can become the basis for creating information technologies for decision support under complex conditions
Keywords
mathematical models; decision-support models; decision-making methods; decision-support systems; information technologies
References
- Ali, A., Rehman, N., Ali, M., & Hila, K. (2024). A novel approach to three-way decision model under fuzzy soft dominance degree relations and emergency situation. Expert Systems with Applications, 239, article number 122369. doi: 10.1016/j.eswa.2023.122369.
- Aregbesola, G.D., Asghar, I., Akbar, S., & Ullah, R. (2025). Fuzzy logic model for informed decisionmaking in risk assessment during software design. Systems, 13(9), article number 825. doi: 10.3390/ systems13090825.
- Aslantas, M., Gündoğdu, F.K., & Moslem, S. (2025). Evaluating the potential risks posed by autonomous vehicles by using a decomposed fuzzy multi-criteria decision-making model. Transportation Engineering, 21, article number 100372. doi: 10.1016/j.treng.2025.100372.
- Gomes, M.I., & Martins, N.C. (2022). Mathematical models for decision making with multiple perspectives: An introduction. Boca Raton: CRC Press. doi: 10.1201/9781003015154.
- Hasiuk, I., & Ivanii, O. (2024). Logical-formal and criteria methods of management decision-making in public administration. Scientific Perspectives, 8(50), 112-129. doi: 10.52058/2708-7530-2024-8(50)-112-129.
- Høj, N.P., Kroon, I.B., Nielsen, J.S., & Schubert, M. (2025). System risk modelling and decision-making – reflections and common pitfalls. Structural Safety, 113, article number 102469. doi: 10.1016/j.strusafe.2024.102469.
- Jaccard, J., & Jacoby, J. (2020). Theory construction and model-building skills: A practical guide for social scientists (2nd ed.). New York: The Guilford Press.
- Kaya, S.K., Pamucar, D., & Aycin, E. (2022). A new hybrid fuzzy multi-criteria decision methodology for prioritizing the antivirus mask over COVID-19 pandemic. Informatica, 33(3), 545-572. doi: 10.15388/22-INFOR475.
- Kozielski, M., Prokopowicz, P., & Mikolajewski, D. (2026). New fuzzy aggregators for ordered fuzzy numbers for trend and uncertainty analysis. Electronics, 15(2), article number 309. doi: 10.3390/electronics15020309.
- Li, T., Ali, M.K.M., & Zhou, X. (2025). Interval-valued hesitant fuzzy decision model based on improved projection measure. International Journal of Fuzzy Systems. doi: 10.1007/s40815-025-02113-x.
- Maksymov, A.Y. (2025). Information technology for solving multi-criteria decision-making problems under conditions of risk, uncertainty and fuzzy information. (Doctoral dissertation, Cherkasy State Technological University, Cherkasy, Ukraine).
- Montero, V.J., Logrosa, G., Calorio, J.L., Lato, J.I., Hassall, M., & Mata, M.A. (2025). Risk modeling with Bowtie method for decision-making towards public health and safety. Safety Science, 185, article number 106777. doi: 10.1016/j.ssci.2025.106777.
- Munier, N. (2021). Mathematical modelling of decision problems: Using the SIMUS method for complex scenarios. Cham: Springer International Publishing. doi: 10.1007/978-3-030-82347-4.
- Puška, A., Božanić, D., Nedeljković, M., & Janošević, M. (2022). Green supplier selection in an uncertain environment in agriculture using a hybrid MCDM model: Z-numbers-Fuzzy LMAW-Fuzzy CRADIS model. Axioms, 11(9), article number 427. doi: 10.3390/axioms11090427.
- Rajesh, R. (2024). Grey models for data analysis and decision-making in uncertainty during pandemics. International Journal of Disaster Risk Reduction, 113, article number 104881. doi: 10.1016/j.ijdrr.2024.104881.
- Syniuk, O. (2025). Behavioral and analytical aspects of managerial decision-making in a changing environment. Economy and Society, 80. doi: 10.32782/2524-0072/2025-80-53.
- Us, S.A., & Koriashkina, L.S. (2018). Models and methods of decision making. Dnipro: Dnipro University of Technology.
- Voloshyn, O.F., & Mashchenko, S.O. (2018). Models and methods of decision making (3rd ed.). Kyiv: Liudmyla Publishing House.
- Więckowski, J., Kizielewicz, B., & Sałabun, W. (2025). Fuzzy RANCOM: A novel approach for modeling uncertainty in decision-making processes. Information Sciences, 694, article number 121716. doi: 10.1016/j. ins.2024.121716.
- Zheldak, T., Koryashkina, L., & Us, S. (2020). Fuzzy sets in management and decision-making. Dnipro: Dnipro University of Technology.