系统管理学报

系统管理学报 ›› 2020, Vol. 29 ›› Issue (3): 494-501.DOI: 10.3969/j.issn.1005-2542.2020.03.009

• 管理信息系统 • 上一篇    下一篇

考虑企业风险感知的R&D网络风险传播建模与仿真

刘慧，杨乃定，张延禄，李芮萌   

  1. 西北工业大学 管理学院，西安 710129
  • 出版日期:2020-05-29 发布日期:2020-07-09
  • 作者简介:刘 慧（1988—），女，博士生。研究方向为风险管理与管理系统工程等。
  • 基金资助:
    国家自然科学基金资助项目(71471146，71501158，71871182)；陕西省软科学研究计划面上项目(2017KRM058)；中央高校基本科研业务费专项资金资助项目(3102016RW005, 3102018JCC013）

Modeling and Simulation of R&D Network Risk Propagation Considering Risk Perception of Enterprises

LIU Hui, YANG Naiding, ZHANG Yanlu, LI Ruimeng   

  1. School of Management, Northwestern Polytechnical University, Xi’an 710129, China
  • Online:2020-05-29 Published:2020-07-09

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摘要：

考虑企业风险感知的基础上，借鉴传染病模型(SIS)思想，将企业状态分为未发生风险有感知、未发生风险无感知和发生风险有感知3种。在此基础上构建了R&D网络风险传播的模型，并进行了数理解析与仿真分析。结果表明：R&D网络风险传播存在稳定状态下的解，即风险传播阈值，该阈值是由企业的风险感知、风险恢复率以及企业间的合作紧密程度决定；考虑风险感知的R&D网络比未考虑风险感知的研发网络具有更高的鲁棒性；随着企业风险恢复概率的不断增大，风险在R&D网络的传播范围越小；企业的风险感知对风险传播速度与传播范围均具有负向影响。该研究成果对于提高R&D网络的抗风险能力具有重要意义。

关键词: 研发网络, 风险感知, 风险传播, 传染病模型, 建模仿真

Abstract:

Based on the consideration of the risk perception of enterprises, the state of each enterprise can be divided into unaware and susceptible, aware and susceptible, and unaware and infectible. This paper established a dynamic model of risk propagation in the R&D network based on the susceptible-infected-susceptible(SIS) model. The analytic solution and the simulations show that, first the threshold is determined by the risk perception of the enterprise, the transmission probability, and the degree of cooperation between two enterprises. Next, the percentage of infected enterprises can be influenced by the risk perception. Third, the robustness of the R&D network by taking risk perception into account is higher than that not considering risk perception. Fourth, with the increase of risk recovery probability, the robustness of the R&D network is much higher. Finally, risk perception has a negative effect on the speed and the scale of risk propagation in the R&D network. The research results are of great significance to improve the anti-risk ability of the R&D network.

Key words: R&, D network, risk perception, risk propagation, SIS modeling and simulation

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