Abstract
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.
Data availability
The synthetic experimental datasets, model implementation code, baseline implementations, training scripts, and evaluation procedures generated during the current study are provided in Supplementary File S1 to enable full replication of reported results. The supplementary materials include: (a) synthetic dataset generation scripts with configurable parameters for organizational network size and knowledge characteristics, (b) complete PyTorch implementation of the proposed coupling model following the ODD protocol description, (c) implementations of all five baseline methods with identical preprocessing pipelines, (d) hyperparameter configuration files and training logs, and (e) sensitivity analysis scripts and visualization code. Real-world organizational data from the supply chain case study are subject to confidentiality agreements with participating enterprises and cannot be made publicly available; however, aggregated statistical summaries and anonymized network structure characteristics are included in the supplementary materials to facilitate understanding of real-world application contexts.
Abbreviations
- DNN:
-
Deep neural networks
- GNN:
-
Graph neural networks
- MARL:
-
Multi-agent reinforcement learning
- CNN:
-
Convolutional neural networks
- RNN:
-
Recurrent neural networks
- API:
-
Application programming interface
- ROI:
-
Return on investment
- GPU:
-
Graphics processing unit
- CUDA:
-
Compute unified device architecture
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ML conceptualized the research framework, designed the multi-agent reinforcement learning methodology, developed the MADQN-PER algorithm, conducted the computational experiments, performed data analysis, and drafted the original manuscript. WY contributed to the enterprise collaborative network modeling, participated in algorithm implementation, assisted with experimental design and validation, and contributed to manuscript revision. YL supervised the overall research project, provided critical insights on the theoretical framework, guided the experimental design, secured computational resources, reviewed and edited the manuscript, and coordinated the research activities. All authors have read and approved the final manuscript.
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This study involves computational modeling and simulation using synthetic datasets and anonymized enterprise collaboration records. No human subjects research or personally identifiable information was collected as part of this study. The anonymized organizational data used in the real-world application case was obtained with appropriate institutional permissions and data use agreements. The research complies with relevant data protection regulations and ethical guidelines for computational research.
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Li, M., Yu, W. & Li, Y. Deep neural network-based coupling model of inter-organizational knowledge flow and agent collaborative decision-making. Sci Rep (2026). https://doi.org/10.1038/s41598-026-37838-8
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DOI: https://doi.org/10.1038/s41598-026-37838-8
