| 技术创新网络知识增长过程建模与仿真研究 |
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| 引用本文： | 禹献云,曾德明,陈艳丽,文金艳.技术创新网络知识增长过程建模与仿真研究[J].科研管理,2013,34(10):35-41. |
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| 作者姓名： | 禹献云 曾德明 陈艳丽 文金艳 |
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| 作者单位： | 1. 湖南大学工商管理学院, 湖南 长沙 410082;
2. 中南大学商学院, 湖南 长沙 410082 |
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| 基金项目： | 国家自然科学基金项目(71173071):知识流动视角下高技术产业创新体系国际化理论与政策研究,起止时间2012.1-2015.12;国家自然科学基金项目(71233002):自主创新背景下我国高技术产业标准化战略与政策研究,起止时间2013.1-2017.12 |
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| 摘 要： | 基于国内外现有研究成果,针对网络密度对技术创新网络知识增长的影响机理,从知识扩散与知识创新两个方面进行了理论分析,提出了网络密度与技术创新网络知识增长呈倒U型关系的研究假设。应用基于多主体(Agent)的建模方法建立一个基于多Agent的技术创新网络知识增长过程模型,采用NetLogo仿真平台进行仿真,对研究假设进行检验。研究结果表明:网络密度具有双面性,太高或太低都不利于技术创新网络的知识增长;网络密度过高往往先抑制技术创新网络知识扩散,随后进一步提高再是阻碍知识创新。
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| 关 键 词： | 网络密度 知识增长 知识扩散 知识创新 基于Agent建模与仿真 |
| 收稿时间： | 2012-02-13 |
Modeling and simulation of the knowledge increase process in the technology innovation network |
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| Yu Xianyun,Zeng Deming,Chen Yanli,Wen Jinyan.Modeling and simulation of the knowledge increase process in the technology innovation network[J].Science Research Management,2013,34(10):35-41. |
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| Authors: | Yu Xianyun Zeng Deming Chen Yanli Wen Jinyan |
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| Institution: | 1. School of Business Administration, Hunan University, Changsha 410082, China;
2. Business School of Central South University, Changsha 410082, China |
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| Abstract: | Based on the current studies at home and abroad in technology innovation networks, this paper examines the influence of technical innovation network density on network knowledge increase from the aspects of knowledge diffusion and knowledge innovation, and put forward a hypothesis that the network density has an inverted U-shaped relationship with knowledge increase. In order to test this hypothesis, a Multi-agent model was established, which demonstrates the process of knowledge increase in innovation network, and the NetLogo simulation platform was adopted to test the hypothesis. The results indicate that the effect of network density has the feature of duality. In other word, the network density will be disadvantageous to the knowledge increase of the innovation network when it is too high or too low. In the meantime, these results show that the inhibition of knowledge diffusion, which will then impede knowledge innovation, will appear when network density is too high and continue to increase. |
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| Keywords: | network density knowledge increase knowledge diffusion knowledge innovation agent-based model and simulation |
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