Inter-organizational knowledge flow and agent collaborative decision-making constitute mutually interdependent processes critical for organizational performance in complex environments. This study proposes a novel deep neural network-based framework that explicitly models the bidirectional coupling mechanism between knowledge propagation dynamics and multi-agent coordination. The architecture integrates graph attention networks for knowledge transfer modeling with multi-agent reinforcement learning for decision coordination, establishing coupling interfaces that enable dynamic adaptation between these subsystems. The model incorporates temporal decay mechanisms, attention-based knowledge path optimization, and closed-loop feedback that propagates decision outcomes back to reshape knowledge transfer patterns. Experimental validation on synthetic and real-world datasets demonstrates substantial performance improvements of 8–24% over state-of-the-art baselines across knowledge transfer accuracy, decision success rates, and coordination efficiency metrics. Deployment in a supply chain coordination scenario achieved 18.5% cost reduction, 71% stockout frequency decrease, and 42.7% inventory turnover improvement. The coupling quality correlation coefficient reached 0.812, confirming strong interdependencies between knowledge evolution and decision outcomes. This work advances theoretical understanding of organizational knowledge systems while providing practical tools for enhancing inter-organizational collaboration.
Li et al. (Mon,) studied this question.