Effectiveness of Entropy Weight Method in Decision-Making

By Yuxin Zhu, Dazuo Tian, Feng Yan. 2020

918 citations recorded by the source corpus.

Entropy weight method (EWM) is a commonly used weighting method that measures value dispersion in decision-making. The greater the degree of dispersion, the greater the degree of differentiation, and more information can be derived. Meanwhile, higher weight should be given to the index, and vice versa. This study shows that the rationality of the EWM in decision-making is questionable. One example is water source site selection, which is generated by Monte Carlo Simulation. First, too many zero values result in the standardization result of the EWM being prone to distortion. Subsequently, this outcome will lead to immense index weight with low actual differentiation degree. Second, in multi-index decision-making involving classification, the classification degree can accurately reflect the information amount of the index. However, the EWM only considers the numerical discrimination degree of the index and ignores rank discrimination. These two shortcomings indicate that the EWM cannot correctly reflect the importance of the index weight, thus resulting in distorted decision-making results.

Subfields: Control and Systems Engineering, Environmental Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/b67d4eff32cb70c2761b68f9edb2914cde8f9350

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In Management Science and Operations Research

1. Rizal Hamid
2. Maznah Mat Kasim
3. Anath Rau Krishnan
4. Mohd Fahmi Ghazali

Citations in the corpus

• A Modified CRITIC Method to Estimate the Objective Weights of Decision Criteria (2021, background): “If a criterion’s scores show more variance from one alternative to another, this criterion is expected to provide more exciting or meaningful information [33].”