Explore how adaptive agent interactions at the micro-level emerge as macro-level system order, based on computational experiments and complex systems science research
Complete CAS research workflow from system analysis to multi-agent modeling
Apply Complex Adaptive Systems theory to explore evolutionary laws and emergent mechanisms in social, economic, and ecological complex systems
Computational experiment methods based on Multi-Agent Simulation (MAS) to build rigorous theory validation platforms
Focusing on Complex Adaptive Systems science frontier
Research agents with learning, adaptation, and evolution capabilities, exploring how individual rules and collective order relate in dynamic environments
Analyze topological structure features of complex systems, study how network connection patterns affect information propagation, influence diffusion, and collective behaviors
Develop statistical learning-based emergent pattern recognition algorithms to detect clustering, phase transitions, synchronization, diffusion, and polarization phenomena
Study synergistic evolution processes between agents and environment, agents and networks, networks and behaviors, revealing multi-scale feedback mechanisms
Explore ABM-based computational experiment paradigms, developing standardized workflows for hypothesis generation, model calibration, and result analysis
Apply Sobol global sensitivity methods to systematically identify key parameters, enhancing model interpretability and predictive capability
Classic works in Complex Adaptive Systems theory
Use our multi-agent modeling simulation experiment platform to explore emergent laws in Complex Adaptive Systems