Variance Measure in Decision-Making Process: An Interpretation

Kattur Soundarapandian Ravichandran

doi:10.31181/msa21202519

Authors

Kattur Soundarapandian Ravichandran Department of Mathematics, Amrita School of Physical Sciences, Amrita Vishwa Vidyapeetham, Coimbatore, India Author https://orcid.org/0000-0003-2397-461X

DOI:

https://doi.org/10.31181/msa21202519

Keywords:

Decision-making, Variance measure, Uncertainty modelling, Attitudinal traits

Abstract

This paper considers variance measure that is popularly used in multi-attribute decision-making (MADM) either as a holistic approach or as a snippet of a holistic approach for determining values of key decision entities such as attributes and/or alternatives. Specifically, we attempt to understand the results that emanate from the variance measure through a detailed interpretive construct, which will serve as a guide to decision-making scholars for rational usage of the popular approach in their respective MADM problems. We present illustrative examples to clarify the interpretations. Furthermore, we explore how the interpretive construct can be extended to analyze consistency in attribute weighting, thereby strengthening the reliability of MADM results. We also demonstrate how the variance measure can complement rank-based and aggregation-based methods, offering a unified perspective that integrates statistical interpretation with decision-making logic.

Downloads

Download data is not yet available.

References

Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The CRITIC method. Computers & Operations Research, 22(7), 763–770. https://doi.org/10.1016/0305-0548(94)00059-H

Ecer, F., & Pamucar, D. (2022). A novel LOPCOW‐DOBI multi‐criteria sustainability performance assessment methodology: An application in developing country banking sector. Omega, 112, 102690. https://doi.org/10.1016/j.omega.2022.102690

Kao, C. (2010). Weight determination for consistently ranking alternatives in multiple criteria decision analysis. Applied Mathematical Modelling, 34(7), 1779–1787. https://doi.org/10.1016/j.apm.2009.09.022

Liu, S., Chan, F. T., & Ran, W. (2016). Decision making for the selection of cloud vendor: An improved approach under group decision-making with integrated weights and objective/subjective attributes. Expert Systems with Applications, 55, 37–47. https://doi.org/10.1016/j.eswa.2016.01.059

Tayalı, H. A., & Timor, M. (2017). Ranking with statistical variance procedure based analytic hierarchy process. Acta Infologica, 1(1), 31–38.

Zhao, H., Xu, Z., Wang, H., & Liu, S. (2017). Hesitant fuzzy multi-attribute decision-making based on the minimum deviation method. Soft Computing, 21, 3439–3459. https://doi.org/10.1007/s00500-015-2020-y

Selvachandran, G., Quek, S. G., Smarandache, F., & Broumi, S. (2018). An extended technique for order preference by similarity to an ideal solution (TOPSIS) with maximizing deviation method based on integrated weight measure for single-valued neutrosophic sets. Symmetry, 10(7), 236. https://doi.org/10.3390/sym10070236

Wang, J., Li, S., & Zhou, X. (2023). A novel GDMD-PROMETHEE algorithm based on the maximizing deviation method and social media data mining for large group decision making. Symmetry, 15(2), 387. https://doi.org/10.3390/sym15020387

Xu, D. S., Wei, C., & Wei, G. W. (2018). Minimum deviation method for single-valued neutrosophic multiple attribute decision making with preference information on alternatives. Journal of Intelligent Computing, 9, 54–75. https://doi.org/10.6025/jic/2018/9/1/54-75

Xu, Z. (2006). Minimizing deviations models for solving MADM problems with preference information on alternatives in uncertain linguistic settings. International Journal of Operations Research, 3(1), 30–35.

Sarwar, M., Ali, G., & Chaudhry, N. R. (2023). Decision-making model for failure modes and effect analysis based on rough fuzzy integrated clouds. Applied Soft Computing, 136, 110148. https://doi.org/10.1016/j.asoc.2023.110148

Krishankumar, R., Ravichandran, K. S., Liu, P., Kar, S., & Gandomi, A. H. (2021). A decision framework under probabilistic hesitant fuzzy environment with probability estimation for multi-criteria decision making. Neural Computing and Applications, 33, 8417–8433. https://doi.org/10.1007/s00521-020-05595-y

Wen, T. C., Chung, H. Y., Chang, K. H., & Li, Z. S. (2021). A flexible risk assessment approach integrating subjective and objective weights under uncertainty. Engineering Applications of Artificial Intelligence, 103, 104310. https://doi.org/10.1016/j.engappai.2021.104310

Kamari, M. S. M., Rodzi, Z. M., & Kamis, N. H. (2025). Pythagorean Neutrosophic Method Based on the Removal Effects of Criteria (PNMEREC): An innovative approach for establishing objective weights in multi-criteria decision-making challenges. Malaysian Journal of Fundamental and Applied Sciences, 21(1), 1678–1696. https://doi.org/10.11113/mjfas.v21n1.3600