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<p>Multi-attribute group decision-making (MAGDM) constitutes a pivotal methodology for resolving complex problems requiring collective expertise. However, critical limitations persist in current frameworks, including overreliance on unilateral expert weight strategies that neglect the interplay between informational objectivity and social influence, treatment of evaluation systems as structural “black boxes”, unrealistic assumptions regarding perfect expert rationality, and inequitable modification of opinions during consensus building. To address these issues, this study develops an integrated MAGDM framework that incorporates three synergistic innovations. First, we introduce a hybrid weighting mechanism that combines information entropy and quantum Bayesian networks (QBNs) to quantify the interference effects of trust propagation, and solve the combination ratio through Monte Carlo simulation. Second, a fair consensus adjustment model is constructed to optimize the distribution of opinion revisions and balance consensus reaching with the retention of original opinions. Third, to characterize experts’ risk-avoidance behavior and evaluate the internal structure and behavioral characteristics of the system, we design a parallel data envelopment analysis (DEA) cross-efficiency model and a regret-based perceived utility value (PUV). Finally, an illustrative example is presented to validate the effectiveness of the proposed approach, while its robustness and superiority are demonstrated through sensitivity analysis and comparative experiments.</p>

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Fairness consensus adjustment and bifocal expert weight integration in multi-attribute group decision-making with parallel expert evaluation systems

  • 期刊论文

  • 发表日期：

    2025年11月10日

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  • Jinpei Liu,
  • Rong Xu,
  • Wenqing Xu,
  • Longlong Shao

摘要

Multi-attribute group decision-making (MAGDM) constitutes a pivotal methodology for resolving complex problems requiring collective expertise. However, critical limitations persist in current frameworks, including overreliance on unilateral expert weight strategies that neglect the interplay between informational objectivity and social influence, treatment of evaluation systems as structural “black boxes”, unrealistic assumptions regarding perfect expert rationality, and inequitable modification of opinions during consensus building. To address these issues, this study develops an integrated MAGDM framework that incorporates three synergistic innovations. First, we introduce a hybrid weighting mechanism that combines information entropy and quantum Bayesian networks (QBNs) to quantify the interference effects of trust propagation, and solve the combination ratio through Monte Carlo simulation. Second, a fair consensus adjustment model is constructed to optimize the distribution of opinion revisions and balance consensus reaching with the retention of original opinions. Third, to characterize experts’ risk-avoidance behavior and evaluate the internal structure and behavioral characteristics of the system, we design a parallel data envelopment analysis (DEA) cross-efficiency model and a regret-based perceived utility value (PUV). Finally, an illustrative example is presented to validate the effectiveness of the proposed approach, while its robustness and superiority are demonstrated through sensitivity analysis and comparative experiments.