THE THEORETICAL AND PRACTICAL FOUNDATIONS OF LOGICAL MODELING IN THE DECISION-MAKING PROCESS
Keywords:
logical modeling, multi-criteria decision-making, uncertainty, fuzzy logic, sensitivity analysisAbstract
In decision-making environments, incomplete, contradictory, and multi-source information limits the applicability of conventional mathematical methods. Logical modeling formalizes this difficulty through graded membership, interval evaluation, and uncertain belief structures. This approach converts subjective assessments into measurable indicators and makes it possible to establish a logical ranking of alternatives. A comparative analysis of multi-criteria decision-making methods shows that the choice of method depends on the nature of the criteria, the level of data precision, and the preference structure of decision-makers. In practical terms, logical modeling enables direct comparison of alternatives, integration of heterogeneous evaluation semantics, and modeling of multi-source uncertainty. Extended structures such as hesitant fuzzy sets transform expert opinions into a general decision criterion without losing their diversity. In group decision-making, accounting for interaction factors allows the influence of collective agreement, and not only individual preferences, to be measured. This approach is particularly effective in projects that require joint evaluation of spatial and technical criteria, such as photovoltaic power station site selection. Sensitivity analysis verifies model robustness by clarifying how changes in criteria weights and input data affect the results, while taxonomy-based systems support the objective identification of an appropriate method. Since uncertainty is expressed not only through probability but also through logical constraints and priority structures, such models strengthen the theoretical foundation of modern decision support systems. Integrating large language models into decision support raises new methodological questions for the future development of logical modeling.
References
1.Badings, T., Simão, T. D., Suilen, M., & Jansen, N. (2023). Decision-making under uncertainty: Beyond probabilities. International Journal on Software Tools for Technology Transfer, 25, 375–391. https://doi.org/10.1007/s10009-023-00704-3
2.Biswas, A., Baranwal, G., & Tripathi, A. K. (2022). ABAC: Alternative by alternative comparison based multi-criteria decision making method. Expert Systems with Applications, 208, Article 118174. https://doi.org/10.1016/j.eswa.2022.118174
3.Chen, S.-M., & Hsu, M.-H. (2023). Multiple attribute decision making based on novel score function of interval-valued intuitionistic fuzzy values, score matrix, and nonlinear programming model. Information Sciences, 645, Article 119332. https://doi.org/10.1016/j.ins.2023.119332
4.Cheng, R., Fan, J., Wu, M., & Seiti, H. (2024). A dual-level multi-attribute group decision-making model considering interaction factors based on CCSD and MARCOS methods with R-numbers. Expert Systems with Applications, 243, Article 122839. https://doi.org/10.1016/j.eswa.2023.122839
5.Cinelli, M., Kadziński, M., Miebs, G., Gonzalez, M., & Słowiński, R. (2022). Recommending multiple criteria decision analysis methods with a new taxonomy-based decision support system. European Journal of Operational Research, 302(2), 633–651. https://doi.org/10.1016/j.ejor.2022.01.011
6.Demir, G., Chatterjee, P., & Pamučar, D. (2024). Sensitivity analysis in multi-criteria decision making: A state-of-the-art research perspective using bibliometric analysis. Expert Systems with Applications, 237, Article 121660. https://doi.org/10.1016/j.eswa.2023.121660
7.Gölcük, İ., Durmaz, E. D., & Şahin, R. (2022). Interval type-2 fuzzy development of FUCOM and activity relationship charts along with MARCOS for facilities layout evaluation. Applied Soft Computing, 128, Article 109414. https://doi.org/10.1016/j.asoc.2022.109414
8.Handler, A., Larsen, K. R., & Hackathorn, R. (2024). Large language models present new questions for decision support. International Journal of Information Management, 79, Article 102811. https://doi.org/10.1016/j.ijinfomgt.2024.102811
9.Jing, L., Zhang, H., Dou, Y., Feng, D., Jia, W., & Jiang, S. (2024). Conceptual design decision-making considering multigranularity heterogeneous evaluation semantics with uncertain beliefs. Expert Systems with Applications, 244, Article 122963. https://doi.org/10.1016/j.eswa.2023.122963
10.Li, L., & Xu, Y. (2024). An extended hesitant fuzzy set for modeling multi-source uncertainty and its applications in multiple-attribute decision-making. Expert Systems with Applications, 238, Article 121834. https://doi.org/10.1016/j.eswa.2023.121834
11.Li, Y., He, X., Martínez, L., Zhang, J., Wang, D., & Liu, X. A. (2024). Comparative analysis of three categories of multi-criteria decision-making methods. Expert Systems with Applications, 238, Article 121824. https://doi.org/10.1016/j.eswa.2023.121824
12.Nezhad Shahmohammad, F., Pourrahimian, Y., & Akbari-Gharalari, N. (2024). Synthesizing complexity: Trends, challenges, and future directions in fuzzy-based multicriteria decision-making (FMCDM) methods. Applied Soft Computing, 167, Article 112362. https://doi.org/10.1016/j.asoc.2024.112362
13.Samin, H., Bencomo, N., & Sawyer, P. (2022). Decision-making under uncertainty: Be aware of your priorities. Software and Systems Modeling, 21, 2213–2242. https://doi.org/10.1007/s10270-021-00956-0
14.Wan, S.-P., Wu, H., & Dong, J.-Y. (2024). An integrated method for complex heterogeneous multi-attribute group decision-making and application to photovoltaic power station site selection. Expert Systems with Applications, 242, Article 122456. https://doi.org/10.1016/j.eswa.2023.122456
15.Yang, S., Liao, H., & Wu, X. (2025). Prescriptive analytics for dynamic multi-criterion decision making considering learned knowledge of alternatives. Expert Systems with Applications, 263, Article 126350. https://doi.org/10.1016/j.eswa.2024.126350
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