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Effects of Structural Characteristics of Complex Knowledge Network on Knowledge Flow

Author: Ding
Tutor: ChenXiaoRong
School: Shanghai Jiaotong University
Course: Management Science and Engineering
Keywords: Complex knowledge network Knowledge flows Multi-agent simulation Central Intermediary
CLC: F270
Type: Master's thesis
Year: 2009
Downloads: 353
Quote: 3
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Abstract


Complex networks as a new tool for quantitative description of complex systems, the modeling and analysis of complex adaptive systems. If the abstract knowledge system elements \Complex knowledge network is a typical complex network. In this paper, based on multi-agent simulation technology to build the the calculated experimental model of a knowledge network. First of all, in a separate virtual knowledge network, discusses the evolution of the three groups of the level of knowledge of different subjects. The study found that the evolution of the main level of their knowledge the fastest high intermediary or high school concentric. Moreover, the conclusion of the study, and empirical research are in good agreement. Secondly, the establishment of two different virtual knowledge Knowledge Network A and Network B, to explore the different effects of the simultaneous evolution of the different mechanisms of knowledge cooperation links established between the knowledge network for Network A and Network B level of knowledge. The simulation results show that the spread of knowledge in the network by different coupling mechanisms between the main Network A and Network B between the degree of knowledge diffusion by the two networks to establish cooperative links between the main three attributes (high level of knowledge, high center and intermediary). High School concentric or intermediary body to establish cooperative links by selecting the High School concentric or intermediary body backward knowledge network with advanced knowledge network, knowledge diffusion networks. Finally, this paper investigates the the backward network density and network connection weights of the different effects of the degree of knowledge diffusion networks. Simulation results show, with the backward network itself density and connect the right weight of growth in a variety of different connection mechanism, backward network and advanced network of the knowledge level of the gap between accelerated narrow, and in the reach a certain density, and connect the right weight, backward network to achieve the same level of knowledge and advanced network. And backward network to take the case of different density and connection weights the high intermediary or high school concentric body of knowledge to establish the links between the networks, is still the most effective way to speed up the flow of knowledge between networks. The conclusions of this study has some reference for policymakers trying to speed up the backward knowledge network knowledge to enhance. Based on the simulation results, the paper analyzes the European Innovation Relay Alliance and the Silicon Valley - Hsinchu Knowledge Network Alliance. Through two case studies we found: First, to establish contact with the advanced knowledge network to speed up the upgrading of the level of knowledge in the backward knowledge network, closed to the development of local knowledge networks will only increase the overall knowledge gap with the advanced knowledge of network. Secondly, we must be targeted to establish contact with the advanced knowledge of network knowledge, first looking for High School concentric or intermediary body of knowledge in the knowledge network, and contributed to its extensive knowledge Contact.

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