基于多Agent系统的科研合作网络知识扩散建模与仿真
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摘要
科研合作网络的知识扩散主要受知识溢出和知识创新这两个过程的影响,据此提出知识扩散的机制。采用多Agent系统建模方法构建科研合作网络知识扩散仿真演化模型,分析了网络结构、知识溢出效应和个体知识创新能力对知识扩散的影响。通过知识扩散效果评价指标的对比分析,得出如下结论:科研合作网络的拓扑结构对知识扩散效果造成影响,BA无标度网络结构优于其他网络结构(规则网络、小世界网络、随机网络);知识溢出效应主要影响知识扩散前期,随着知识溢出效率因子的递增,网络平均知识存量总体震荡上升,知识扩散速率递增,同时网络知识存量分布均衡度递减;个体知识创新能力主要影响知识扩散后期,个体知识创新能力因子大的网络表现出较强的平均知识存量增长,也加剧了网络知识存量的不均衡分布。
It has been proposed that knowledge diffusion in scientific research cooperation networks is affected mainly by knowledge spillover and knowledge innovation. On the basis of this idea, this paper proposes a knowledge diffusion mech‐anism. This multi-agent system modeling method is used to build a simulation evolution model of knowledge diffusion in scientific research cooperative networks. Thereafter, the influence of network structures, knowledge overflow effects, and individual knowledge innovation ability on knowledge diffusion is analyzed. Through a comparative analysis of evaluation indexes of the knowledge diffusion effect, the following conclusions are drawn. The topology of scientific research cooper‐ation networks has an impact on the knowledge diffusion effect, and a BA scale-free network structure is superior to other network structures(regular network, small world network, random network). The knowledge spillover effect affects mainly the early stage of knowledge diffusion. With the increase in the knowledge spillover efficiency factor, the average network knowledge stock increases in oscillations, the knowledge diffusion rate increases, and the balance degree of network knowledge stock distribution decreases. Individual knowledge innovation ability affects mainly the later stage of knowl‐edge diffusion. Networks with large individual knowledge innovation ability factors show strong average knowledge stock growth, which also aggravates the unbalanced distribution of knowledge stock among individuals.
It has been proposed that knowledge diffusion in scientific research cooperation networks is affected mainly by knowledge spillover and knowledge innovation. On the basis of this idea, this paper proposes a knowledge diffusion mech‐anism. This multi-agent system modeling method is used to build a simulation evolution model of knowledge diffusion in scientific research cooperative networks. Thereafter, the influence of network structures, knowledge overflow effects, and individual knowledge innovation ability on knowledge diffusion is analyzed. Through a comparative analysis of evaluation indexes of the knowledge diffusion effect, the following conclusions are drawn. The topology of scientific research cooper‐ation networks has an impact on the knowledge diffusion effect, and a BA scale-free network structure is superior to other network structures(regular network, small world network, random network). The knowledge spillover effect affects mainly the early stage of knowledge diffusion. With the increase in the knowledge spillover efficiency factor, the average network knowledge stock increases in oscillations, the knowledge diffusion rate increases, and the balance degree of network knowledge stock distribution decreases. Individual knowledge innovation ability affects mainly the later stage of knowl‐edge diffusion. Networks with large individual knowledge innovation ability factors show strong average knowledge stock growth, which also aggravates the unbalanced distribution of knowledge stock among individuals.
引文
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[3] Krogh G V. Care in knowledge creation[J]. California Manage-ment Review, 1998, 40(3):133-153.
[4]周素萍.高科技中小企业集群知识扩散模型构建及阶段分析[J].企业经济, 2013(1):77-80.
[5]巴志超,李纲,朱世伟.科研合作网络的知识扩散机理研究[J].中国图书馆学报, 2016, 42(5):68-84.
[6] Cowan R, Jonard N. Network structure and the diffusion of knowledge[J]. Journal of Economic Dynamics&Control, 2004,28(8):1557-1575.
[7] Morone P, Taylor R. Knowledge diffusion dynamics and network properties of face-to-face interactions[J]. Journal of Evolutionary Economics, 2004, 14(3):327-351.
[8]孙耀吾,卫英平.高技术企业联盟知识扩散研究——基于小世界网络的视角[J].管理科学学报, 2011, 14(12):17-26.
[9]李志宏,朱桃.基于加权小世界网络模型的实践社区知识扩散研究[J].软科学, 2010, 24(2):51-55.
[10]李纲,巴志超.科研合作超网络下的知识扩散演化模型研究[J].情报学报, 2017, 36(3):274-284.
[11]岳增慧,许海云,方曙.基于个体行为的科研合作网络知识扩散建模研究[J].情报学报, 2015, 34(8):819-832.
[12]岳增慧,许海云,方曙.基于微分动力学的科研合作网络知识扩散模型及影响机制研究[J].情报学报, 2015, 34(11):1132-1142.
[13]夏昊翔,王国秀,宣照国,等.针对科研合作网络演化建模的基于Agent实验平台原型[J].情报学报, 2010, 29(4):634-640.
[14] Sun X, Kaur J, Milojevi S, et al. Social dynamics of science[J].Scientific Reports, 2013, 3(1):1069-1069.
[15] Romer P M. Increasing returns and long-run growth[J]. Journal of Political Economy, 1986, 94(5):1002-1037.
[16]赵蓉英,张洋,邱均平.知识网络研究(Ⅲ)——知识网络的特性探析[J].情报学报, 2007, 26(4):583-587.
[17] Jerome L W. Innovation in social networks:Knowledge spillover is not enough[J]. Knowledge Management Research&Practice,2013, 11(4):422-431.
[18] Konno T. Network effect of knowledge spillover:Scale-free net-works stimulate R&D activities and accelerate economic growth[J]. Physica A:Statistical Mechanics and its Applications, 2016,458:157-167.
[19]孙兆刚,刘则渊.知识产生溢出效应的分析[J].科学学与科学技术管理, 2004, 25(3):57-61.
[20]盛昭瀚,张军,杜建国.社会科学计算实验理论与应用[M].上海:上海三联书店, 2009:124-128.
[21] Newman M E J, Watts D J. Renormalization group analysis of the small-world network model[J]. Physics Letters A, 1999, 263(4-6):341-346.
[22] Barabási A, Albert R. Emergence of scaling in random networks[J]. Science, 1999, 286(5439):509-512.
[23]蒋新.布鲁克斯基本方程的数学引伸[J].情报科学, 1988(3):6-8.
[24]杨志锋,邹珊刚.知识资源,知识存量和知识流量:概念,特征和测度[J].科研管理, 2000, 21(4):105-111.