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Regret cross-efficiency evaluation using attitudinal entropy approach

Economics

Regret cross-efficiency evaluation using attitudinal entropy approach

H. Pan, G. Yang, et al.

Discover an innovative approach to Data Envelopment Analysis with the Regret Cross-Efficiency Model utilizing Attitudinal Entropy! Authored by Hao Pan, Guo-liang Yang, Xiao-lei Chen, Yuan-yu Lou, Teng Wang, and Zhong-cheng Guan, this research offers a significant advancement by integrating regret theory and attitudinal entropy for more robust rankings of Decision Making Units.

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Playback language: English
Abstract
This paper proposes a new cross-efficiency model called Regret Cross-Efficiency Model using Attitudinal Entropy Approach (RACE) to address limitations of existing Data Envelopment Analysis (DEA) methods. RACE incorporates regret theory and attitudinal entropy to synthesize cross-efficiencies under incomplete rationality, providing a more comprehensive and robust ranking of Decision Making Units (DMUs). Empirical examples demonstrate the validity and robustness of the RACE method.
Publisher
Humanities and Social Sciences Communications
Published On
Sep 30, 2024
Authors
Hao Pan, Guo-liang Yang, Xiao-lei Chen, Yuan-yu Lou, Teng Wang, Zhong-cheng Guan
Tags
Data Envelopment Analysis
cross-efficiency
regret theory
attitudinal entropy
Decision Making Units
model synthesis
empirical examples

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