Self-organization and emergent behavior reveal the central scientific question of how simple rules generate complex systems and are key to understanding natural phenomena such as the origin of life, ecosystem evolution, and social collaboration. In artificial systems, such research provides a theoretical basis for the design of intelligent systems without central control (e.g., unmanned clusters, and distributed energy networks).

The study of self-organizing systems and emergent phenomena focuses on unraveling the dynamic mechanisms through which simple individuals form complex global structures via local interactions. Its significance lies in addressing the fundamental scientific question of “from microscopic rules to macroscopic order," providing a theoretical foundation for understanding life origins, ecosystems, and distributed intelligent systems. This field aims to develop adaptive models without centralized control, drive innovations in algorithms such as swarm robotics, autonomous robots, AI decision-making, and blockchain, and advance breakthroughs in complex systems theory through interdisciplinary integration between fields such as ecology and computer science. These efforts establish universal frameworks to address dynamic collaborative challenges in engineering, biological, and social domains and the controllability of artificial physical systems in reality (e.g. autonomous robots, AI decision-making).