The rapid evolution of the new economy, driven by digitalization and platformization, has transformed the foundations of production and cooperation. Traditional economic models often fail to capture the decentralized, heterogeneous, and adaptive characteristics of these systems. This dissertation addresses these limitations by integrating nonequilibrium statistical physics with agent-based modeling to investigate the fundamental mechanisms of cooperation in complex socio-technical environments.
The research develops two complementary models to explore these dynamics:
Model I: Resource Sharing in Multi-layer Technological Networks. It constructs a nonequilibrium framework coupling behavioral dynamics with an adaptive reputation field. The results indicate that moderate inter-layer coupling and feedback reinforcement promote stable cooperation and higher systemic efficiency.
Model II: Egalitarian Peer-to-Peer Cooperation. It examines multi-skilled individuals balancing altruism, efficiency, and self-interest. Simulation results show that the system evolves toward hybrid equilibrium states characterized by partial altruism and structural differentiation.
Together, these models establish a unified framework connecting micro-level trust formation with macro-level structural reorganization. The findings provide an interdisciplinary foundation for studying collective intelligence and the design of decentralized, self-organizing economic systems.