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Maximum Entropy-Minimum Residual Model: An Optimum Solution to Comprehensive Evaluation and Multiple Attribute

Qi-Yi Tang1, Yu-Xuan Lin2,3

  • 1Institute of Insect Sciences, Zhejiang University, Hangzhou 310028, China.

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Summary
This summary is machine-generated.

This study introduces a new Maximum Entropy-Minimum Residual (MEMR) model for creating composite indicators. MEMR offers a robust and interpretable method for assigning factor weights in comprehensive evaluations.

Keywords:
composite indicatorcomprehensive evaluationentropymultiple attribute decision making

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Area of Science:

  • Multidisciplinary
  • Data Science
  • Applied Mathematics

Background:

  • Composite indicators are crucial for holistic assessments but face challenges in weight assignment.
  • Existing weighting methods can yield trivial, counterintuitive, or computationally infeasible results.

Purpose of the Study:

  • To propose a novel Maximum Entropy-Minimum Residual (MEMR) model for generating composite indicator weights.
  • To address limitations of current weighting methodologies.

Main Methods:

  • Developed a new model based on the Maximum Entropy-Minimum Residual (MEMR) principle.
  • Directly estimated the relationship between factor weights and the composite indicator.
  • Compared MEMR with existing weighting methods through case studies.

Main Results:

  • The MEMR model effectively extracts common features while preserving factor diversity.
  • MEMR demonstrated more robust, consistent, and interpretable results compared to other methods.
  • The model is suitable for comprehensive evaluations with quantitative factors.

Conclusions:

  • The MEMR model provides a superior approach to composite indicator weight generation.
  • The method is versatile and applicable across various fields.
  • Associated optimization techniques and statistical tests are available in DPS software.