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<p>Group decision-making in social networks faces two major challenges as incomplete trust relationships and uncertainty in preference information, both of which compromise decision reliability. To address these issues, this study proposes a two-stage decision-making framework that combines the structural perspective of trust networks with the cognitive perspective of individual preferences. In the former stage, quantum probability theory is employed to model order and interference effects in trust propagation, enabling more accurate inference of missing trust and objective assignment of expert weights. In the latter stage, four right-skewed piecewise linear utility functions are developed to capture the heterogeneity of decision-makers’ risk perceptions, transforming interval-valued fuzzy preferences into psychologically consistent crisp values. On this basis, optimization models are constructed to support the selection among alternatives. A case study on new energy vehicle supplier selection is performed to validate the proposed approach, and comparative analyses further demonstrate its effectiveness. This framework enhances the rationality and adaptability of group decision-making and provides practical support for applications such as organizational management and supplier evaluation in contexts with structural incompleteness and cognitive uncertainty.</p>

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Optimizing Group Decision-Making with Interval Preferences in Trust Networks : A Quantum Interference and Risk Perception Approach

  • 期刊论文

  • 发表日期：

    2026年2月26日

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  • Jianing Gao,
  • Xiaoxia Xu,
  • Zaiwu Gong,
  • Francisco Javier Cabrerizo

摘要

Group decision-making in social networks faces two major challenges as incomplete trust relationships and uncertainty in preference information, both of which compromise decision reliability. To address these issues, this study proposes a two-stage decision-making framework that combines the structural perspective of trust networks with the cognitive perspective of individual preferences. In the former stage, quantum probability theory is employed to model order and interference effects in trust propagation, enabling more accurate inference of missing trust and objective assignment of expert weights. In the latter stage, four right-skewed piecewise linear utility functions are developed to capture the heterogeneity of decision-makers’ risk perceptions, transforming interval-valued fuzzy preferences into psychologically consistent crisp values. On this basis, optimization models are constructed to support the selection among alternatives. A case study on new energy vehicle supplier selection is performed to validate the proposed approach, and comparative analyses further demonstrate its effectiveness. This framework enhances the rationality and adaptability of group decision-making and provides practical support for applications such as organizational management and supplier evaluation in contexts with structural incompleteness and cognitive uncertainty.