ISSN TBA
Computational Social Dynamics
Computational Social Dynamics is dedicated to using computational methods, artificial intelligence, data science, and complex systems theory to study the formation mechanisms, evolutionary processes, and interactions of social, economic, organizational, and cultural phenomena. It focuses on the dynamic relationships among individual behavior, group interaction, social networks, and technological systems. The journal integrates computational social science, computer science, artificial intelligence, network science, economics, behavioral science, digital humanities, and complex systems research, and encourages computational modeling, machine learning, simulation analysis, and data-driven methods to deepen understanding of complex social phenomena.
- Theories and methods of computational social science:computational social science methods, machine learning and social data analysis, computational behavioral science, causal inference, social simulation, and complex social system modeling.
- Multi-agent systems and generative artificial intelligence:large language model agents, agent collaboration, collective decision-making, agent-based social simulation, tool use, agent evaluation, and reliability research on multi-agent systems.
- Social networks and collective behavior: social network analysis, information diffusion, opinion dynamics, group polarization, social norms, collective action,network structure, and collective behavior prediction.
- Algorithmic governance and the social impact of artificial intelligence:algorithmic bias, algorithmic fairness, explainable AI, platform governance, privacy protection, algorithmic regulation, and the social consequences of automated decision-making.
- Computational economics and financial networks:behavioral economics, game theory, economic networks, market simulation, financial risk contagion, mechanism design, resource allocation, and complex economic system analysis.
- AI for Science and computational scientific discovery:scientific machine learning, automated scientific workflows, scientific hypothesis generation, experimental design optimization, knowledge graphs, and the development and validation of intelligent research systems.
- Digital humanities and cultural computing: large-scale text and image analysis, digital archives, cultural networks, historical corpus analysis, computational narrative, and reproducible digital humanities research methods.
- Human–AI collaboration and socio-technical systems: AI-assisted decision-making, human–machine collaboration in intelligent manufacturing, intelligent systems in education and public services, healthcare applications, and assessment of the effectiveness, equity, and risks of related systems.