Algorithm and Simulation of Chinese Traditional Industrial Clusters' Low Carbon Evolution Based on Evolutionary Games Theory on Complex Networks

摘要

Industrial clusters are complex networks formed by numerous agents who continuously imitate, learn from each other and make optimal choice accordingly. The paper uses random learning game and multi-agent system models to construct a Chinese traditional industrial clusters' low carbon evolution model and introduce an algorithm based on the network' external effect and characteristics of agents' adaptive behavior. Then the simulation of low-carbon competition, emergence and evolution was conducted, which produced some valuable conclusions.