Multi-Agent-Based Dynamical Evolution Of Complex Industrial Knowledge Networks

Knowledge network is an important way in knowledge managing among firms. If we restrict the firm network in an certain industry, it is helpful in knowledge learning and knowledge spread because of the affiliation in the knowledge. Now it attracts attention how the structural characteristics of complex industrial knowledge network will affect the knowledge flowing. But it is also important how the dynamic process of knowledge will affect the formation of network structure. So the paper focuses on the interrelation between them.The paper founds two multi-agent-based models of complex industrial knowledge network. In the first model, the agents select some other agents to build knowledge connections according to their own requirements and we divide these agents into five categories. Then we find that some basic structural characteristics of typical complex social networks emergent. We also find that in a mixed knowledge network, the knowledge of the agents which seek connections with agents of high knowledge level and which go after tide increase most quickly. We also draw an inclusion that those agents locating at the center of the knowledge network lead the knowledge competition. The second model is an model with the same agents and it has the initial structure. We import the idea of evolutionary games. Agents can learn the strategy of neighbor and the structure of the network will change with time. According to the simulation, we find that cooperation emerges in the network and the knowledge of agents which insist in cooperation increase the most quickly.At last, we introduce two discussed cases to analyze and prove the development of the real network and the process of knowledge spread. We also explain and complementary the models.