Information aggregation and psychological risk dual-driven venture capital decision: A decision model based on extended fuzzy sets

Given that traditional fuzzy sets in venture capital decision-making are difficult to accurately portray asymmetric risk under high-dimensional uncertainty, and traditional models are difficult to effectively integrate the decision maker's mental fluctuations and risk attitudes, this paper proposes a Multi-Attribute Group Decision Making (MAGDM) method based on p,q-Quasi Rung Orthogonal Pair Fuzzy Sets (PQ-QOFs), in the context of sustainable supplier selection. This study mainly includes two research points: (1) Information aggregation. We introduce the Aczel-Alsina operation rules for p,q-Quasirung orthopair fuzzy sets (PQ-QOFSs), then p,q-Quasirung orthopair fuzzy Aczel-Alsina average (PQ-QOFAAA) operator, p,q-Quasirung orthopair fuzzy Aczel-Alsina weighted average operator (PQ-QOFAAWA), generalized p,q-Quasirung orthopair fuzzy Aczel-Alsina Maclaurin symmetric mean (GPQ-QOFAAMSM) operator and generalized p,q-Quasirung orthopair fuzzy Aczel-Alsina weighted Maclaurin symmetric mean (GPQ-QOFAAWMSM) operator are introduced. (2) Describing the psychological state and risk attitude of decision makers (DMs). We introduce the positive ideal degree (PID) and negative ideal degree (NID) based on projection of PQ-QOFSs. We also combine the above operators with modified regret theory. Finally, two SSS problems are presented to manifest the effectiveness of the designed method. We conclude that the correlation between attributes, decision-makers' risk attitude, and psychological state should be given special attention in SSS process.