Track Co-Chairs
Guoyin Jiang
Professor
jiangguoyin@uestc.edu.cn
University of Electronic Science and Technology of China
Xiaodong Feng
Associate Professor
fengxd1988@hotmail.com
Sun Yat-sen University, China
Wenping Liu
Professor
Wenpingliu2009@gmail.com
Hubei University of Economics
Brief Introduction
Digital platforms have served as important forms of business application and social management since they appear. They can be seen as the ecosystem of service-oriented business models, where various information technologies, i.e., big data, AI, cloud computing, and blockchain, are utilized as infrastructures to connect the collaboration and interaction between suppliers and consumers in real-time as a value proposition. In essence, digital platforms are complex adaptive network ecosystems that integrate multi-dimensional elements such as humans, AI agents, technology, algorithms, capital, and digital resources. The systems contain heterogeneous subjects, among which there are diversified nonlinear interactions, continuous evolution, and unbalanced dynamic processes.
Complexity is the key feature of digital platforms, which becomes more prominent in the new era with the ubiquitous application of AI. The platforms contain a huge number and scale of constituent entities and subsystems, diverse behavior and interplay between various subjects with uncertainty, and continuous emergence and evolution of the system structures and functions. It is necessary to face up to the complexity of the platforms by revealing the essential characteristics of the platforms in the dimensions of technology, structure, behavior, and digital resources. It also requires that the platform governance should constantly adapt to the changes in the new internal and external environment, such as the wide application of LLM (Large Language Models)-based robots, and carry out complex adaptive governance.
Therefore, it is particularly vital to investigate the underlying complexity of digital platforms and build adaptive governance mechanisms. Fortunately, various computation methodologies, such as agent-based models, system dynamics, game theory, machine learning/deep learning, and big data analytics, provide powerful tools to understand the complexity of digital platforms. The purpose of this track is to broaden and deepen the understanding of the complexity of digital platforms and complex adaptive governance by developing computational models with empirical study by computation methods.
Topics
1. Modeling the Operation of Digital Platform Ecosystems
2. Complex Adaptive Governance of Digital Platforms
3. Data Resources Management of Online Platforms
4. Application of LLMs or Generative AI in Digital Platforms
5. Competition/Cooperation of Human and AI-based agents in Digital Platforms
6. Algorithm, Data and AI Governance in Digital Platforms
7. Dynamic Evolution of Interplay Behavior in Digital Platforms
8. Modeling and Simulation Methods for Digital Platform Operations
9. Data Science for Management Decisions in Digital Platforms
10. Data-driven Social Computing in Digital Platforms
11. Application of Machine Learning/Deep Learning/Reinforcement Learning/Knowledge Graph for Complexity Analysis in Digital Platforms