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<p>In Large Scale Group Decision Making (LSGDM), the differences in decision-makers’ professional backgrounds and attitudes often lead to high-quality decisions being overshadowed by numerous low-quality decisions, thus affecting the accuracy of the final decision. This study proposes a new decision-making method to address this challenge. First, a few experts are invited to make decisions as cluster centers, followed by obtaining decisions from a large number of ordinary decision-makers. The ordinary decisions are then generated and modified using a Discrete Conditional Variational Autoencoder (DCVAE) to enhance decision quality while maintaining consistency with expert decisions. Finally, the normalized prediction selection rate (NPSR) and the Borda Count consensus method are integrated to obtain the final result. Experimental results demonstrate the effectiveness of this method in improving the quality of LSGDM, providing a new solution to the coexistence of high- and low-quality decisions.</p>

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A large scale group decision making with expert guidance via discrete conditional variational autoencoder

  • 期刊论文

  • 发表日期：

    2025年2月11日

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  • Hengshan Zhang,
  • Adong He,
  • Jiaze Sun,
  • Yanping Chen

摘要

In Large Scale Group Decision Making (LSGDM), the differences in decision-makers’ professional backgrounds and attitudes often lead to high-quality decisions being overshadowed by numerous low-quality decisions, thus affecting the accuracy of the final decision. This study proposes a new decision-making method to address this challenge. First, a few experts are invited to make decisions as cluster centers, followed by obtaining decisions from a large number of ordinary decision-makers. The ordinary decisions are then generated and modified using a Discrete Conditional Variational Autoencoder (DCVAE) to enhance decision quality while maintaining consistency with expert decisions. Finally, the normalized prediction selection rate (NPSR) and the Borda Count consensus method are integrated to obtain the final result. Experimental results demonstrate the effectiveness of this method in improving the quality of LSGDM, providing a new solution to the coexistence of high- and low-quality decisions.