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<p>This paper proposes a consensus reaching model for group decision making (GDM) based on the Pearson correlation coefficient (PCC), in which expert uncertainty is captured through non-reciprocal pairwise comparison matrices (NrPCMs). First, a novel consistency index is introduced to measure the inconsistency of NrPCMs, effectively reflecting the interplay between uncertainty and inconsistency. Second, a PCC-based optimization method is developed to derive the priority vector from NrPCMs. Results indicate that this method better preserves the original decision information compared to several existing approaches. Third, an intelligent-optimization-algorithm-driven consensus model is established, incorporating a consensus index that quantifies the similarity between individual and collective NrPCMs by using PCC. A case study demonstrates that the proposed model has an advantage to improve the computational efficiency of GDM under uncertainty.</p>

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A PCC-based consensus reaching model in group decision making with non-reciprocal pairwise comparison matrices

  • 期刊论文

  • 发表日期：

    2026年2月27日

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  • Fang Liu,
  • Ji-Ting Mo,
  • Jing Wen

摘要

This paper proposes a consensus reaching model for group decision making (GDM) based on the Pearson correlation coefficient (PCC), in which expert uncertainty is captured through non-reciprocal pairwise comparison matrices (NrPCMs). First, a novel consistency index is introduced to measure the inconsistency of NrPCMs, effectively reflecting the interplay between uncertainty and inconsistency. Second, a PCC-based optimization method is developed to derive the priority vector from NrPCMs. Results indicate that this method better preserves the original decision information compared to several existing approaches. Third, an intelligent-optimization-algorithm-driven consensus model is established, incorporating a consensus index that quantifies the similarity between individual and collective NrPCMs by using PCC. A case study demonstrates that the proposed model has an advantage to improve the computational efficiency of GDM under uncertainty.