Modeling And Simulation Of Inter-firm Knowledge Network Based On Multi-agent System

With the increasing knowledge flow and market competition, seeking outside knowledge has become a key way to enhance innovation capacity and gain competitive advantage. Inter-firm networks provide an effective mechanism for firms to obtain technical knowledge and improve innovation capacity. Therefore, the resulting inter-firm knowledge network is a good platform for business to pursue further development.Inter-firm network and knowledge system is a complex network respectively; with the interactions among firms, inter-firm knowledge system is a more complexed system. Empirical research and case studies have some limitations in studying such a dynamic and complexed system, while multi-agent based simulation can recognize the features better from the perspective of complex adaptive systems.This paper developed a simulation model by Netlogo platform based on systematic and comprehensive literature review in the field of inter-firm knowledge network. With the attributes of agents-knowledge endowment, learning ability and knowledge creation ability and the attributes of the network-network size and density, the whole network evoles according to the leaning knowledge, updating links and knowledge depreciation rules. By analyzing the effects on the performance of the evolution of knowledge networks of the five factors, network size, initial network density, learning ability, neighborhood threshold and initial knowledge level, the dynamics and complexity of network evolution were studied.The simulation results show that the five factors have significant impacts on the mean and standard deviation of the network's knowledge stock, as well as the mean and standard deviation of network's links. With further simulations and analysis, some nonlinear features are identified, in the relationship of network size, initial network density, initial knowledge level and the network evolution performance.