[Objective] This paper investigates the network environments facing knowledge-based team as well as their impacts to the job performance. [Methods] First, we constructed a Knowledge Supply & Demand System from the perspective of micro level knowledge management with the multi-agent based simulation technology. Second, we added time and financial costs as the criteria for performance evaluation. We developed this new system with Python NetworkX. [Results] We found that the large organizations reduced more costs of innovation than their small counterparts. Increasing the number of nodes in the neighborhood of individuals did not improve the innovation efficiency. Once the average number of fields exceeded a certain threshold, the cost of innovation began to rise. [Limitations] The study did not optimize interactions among individuals for collaborative innovation. [Conclusions] The proposed Knowledge Supply & Demand System simulates the knowledge integration process of an organization at the micro level. The new system helps us understand knowledge management, improve the efficiency of knowledge utilization, and reduce the cost of innovation.
吴江,陈君,张劲帆. 协同创新中知识供需系统的模拟研究*[J]. 现代图书情报技术, 2016, 32(9): 27-33.
Wu Jiang,Chen Jun,Zhang Jinfan. A Knowledge Supply-Demand Simulation System for Collaborative Innovation. New Technology of Library and Information Service, 2016, 32(9): 27-33.
Gao D, Deng X, Zhao Q, et al. Multi-Agent Based Simulation of Organizational Routines on Complex Networks [J]. Journal of Artificial Societies & Social Simulation, 2015, 18(3): Article No. 17.
(Song Gang, Zhang Nan.Innovation 2.0: Democratizing Innovation in the Knowledge Society[J]. Xinhua News Digest, 2009(10): 140-143.)
[3]
Practitioner P A K M. Knowledge Management: The Collaboration Thread[J]. Bulletin of the American Society for Information Science & Technology, 2005, 28(6): 8-11.
[4]
Serrano V, Fischer T.Collaborative Innovation in Ubiquitous Systems[J]. Journal of Intelligent Manufacturing, 2007, 18(5): 599-615.
[5]
Henderson R M, Clark K B.Architectural Innovation: The Reconfiguration of Existing Product Technologies and the Failure of Established Firms[J]. Administrative Science Quarterly, 1990, 35(1): 9-30.
[6]
陈劲. 协同创新与国家科研能力建设[J]. 科学学研究, 2011, 29(12): 2-3.
[6]
(Chen Jin.Collaborative Innovation and the Construction of National Scientific Research Capacity[J]. Studies in Science of Science, 2011, 29(12): 2-3.)
(Wu Jiang, Hu Bin, Liu Tianyin.A Simulation Study of the Impact of Interactive Memory System on Group-Task Interaction[J]. Journal of Management Sciences, 2009, 22(1): 48-58.)
(Xu Shenghua, Yin Hongli.Model of Knowledge Integration Based on System Dynamic in the Organization[J]. Chinese Journal of Management, 2013, 10(6): 890-897.)
(Liu Jingfang, Zou Ping, Zhang Pengzhu.Research on the Matching Model Between Tasks and Knowledge Individuals Based on Ontology[J]. Computer Engineering and Science, 2010, 32(9): 119-121.)
[12]
Carley K M.Computational and Mathematical Organization Theory: Perspective and Directions[J]. Computational & Mathematical Organization Theory, 1995, 1(1): 39-56.
[13]
Macal C M, North M J.Tutorial on Agent-based Modeling and Simulation [C]. In: Proceedings of the 37th Conference on Winter Simulation. 2005.
[14]
Carley K M.A Comparison of Artificial and Human Organizations[J]. Journal of Economic Behavior & Organization, 1996, 31(2): 175-191.
[15]
Hagberg A, Schult D, Swart P. Exploring Network Structure, Dynamics, and Function Using NetworkX [C]. In: Proceedings of the 7th Python in Science Conference. 2007.
