| An industrial cluster consists of a large number enterprises and relevant agencies gathered in specific areas. A strong, persistent competitive advantage in space and the phenomenon is formed. China's industrial clusters have played an important role in the strengthening of local industry competitive advantages and the improving of economic growth. But the spontaneous industrial clusters has many problems that need scientific policy, the clear and strategic goal. So the deep study of industrial clusters becomes very important. Yet there are many inherent limitations for the study of industrial clusters by the traditional economics, so the complexity system theory is applied in industrial clusters in order to compensate for the lacks. This leads to the breakthrough about the methods and tools of industrial clusters: from static to dynamic, from observing phenomena to micro-action and from quantitative to qualitative and so on.In the paper, firstly, based on the analysis of the mechanism of the industrial clusters and the theory of complex adaptive system (CAS), the model of the virtual multi-agents of industrial cluster is constructed. This model is composed of the environment, agents and the rules of action. The environment is expressed by a two-dimensional space. Agents contain many dynamic characters that are updated with the evolution of systems. The rules of action are composed of the agents'moving, producing, cooperating and interacting with the environments. Furtherer, the model is implemented on the Swarm platform and the emergence of the macro-economy behaviors is explained. Meanwhile, the simulation results show that the cooperative relation network has a series of characters of the scale-free network, so that the theory of scale-free network can be used to analyze the properties of industrial cluster. Finally, the introductory policy for the government and the strategy for enterprises are given, which is effective to guide the decision-making of industrial clusters. |
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