Individual vs. group dynamics: Impacts on equilibrium states in self-organizing systems

Recent research highlights the critical need to learn complex structures from data and adapt to evolving data patterns. In this work, we introduce the Decentralized SyncMap model, which shifts the focus from group-based to individual-based interactions in order to reveal more meaningful relationships while operating in lower-dimensional spaces. To improve the model’s robustness and adaptability by incorporating past information, we further propose a temporal memory mechanism. The temporal memory of attractors enhances the model’s ability to capture rare but important features, while the temporal memory of repellers helps the model identify local chunk patterns. By leveraging these individual dynamics, the model naturally preserves internal structures that traditional group-level methods tend to overlook. Our experiments on synthetic chunking tasks show that the Decentralized SyncMap model achieves an average NMI of 0.876, outperforming Word2Vec and both the Original and Symmetrical SyncMap models by 12.6%, 10.6%, and 5.5%, respectively. On real-world datasets, it attains an average NMI of 0.840, performing better than competing approaches by 400.1%, 52.7%, and 4.6%. These results suggest that a model emphasizing individual dynamics provides a more effective means of uncovering latent patterns in complex, evolving data.