运用博弈论研究产学研合作中的知识转移问题，构建产学研知识转移动态控制模型，计算高校和企业在合作博弈状态下获取最大收益时的纳什均衡投入和最优获利能力，分析资源限制水平、共生作用系数变动下纳什均衡投入和最优获利能力的变化情况。模拟分析表明，高校和企业可以根据系统内的知识产出量和吸收量来调整对知识产出和知识吸收的控制投入，从而影响产学研的最优获利能力；提高共生作用系数或降低资源限制水平均可降低产学研的纳什均衡投入，提高其最优获利能力。
In the collaboration practice among industry, universities and research institutes, universities and enterprises can influence the amount of knowledge of both sides by changing the knowledge transfer input to resolve the problem of income among industry, universities and research institutes. Universities' knowledge output and enterprises' absorptive amount can be affected at time, natural growth rate, restriction level of resource, symbiotic relationship, partner's behaviour and self behavior. In this paper, adjustments of knowledge transfer input for universities and enterprises to pursue their own maximum interest are analyzed through building dynamic control model. the objective function is calcucated, the equilibrium of cooperative game in knowledge transfer is solved and a simulation analysis in made. In this model, universities and enterprises are the control subjects. They pursue their own maximum interest by adjusting the investment of knowledge transfer. Based on the method of calculating the dynamic control model, the initial value of model parameter is gotten through the empirical research and makes a simulation. Simulation results show that universities and enterprises can adjust the investment according to the knowledge output and knowledge absorption in the system, and then affect the optimal profit ability of industry, universities and research institutes.The increase of the symbiotic effect coefficient and decrease of resource constraints levels is helpful to minimize the Nash equilibrium input and maximize the optimal profit ability. in view of this, In the process of "industry-university-institute" cooperation, universities and enterprises should adjust the investment at any time according to the knowledge output and knowledge absorption in the system to maintain production profitability at a higher level. In addition, in the process of "industry-university-institute" cooperation, universities and enterprises shall establish extensive connection, strengthen mutual exchanges between the two sides, improve the tacit understanding degree and make full use of the symbiotic effect between the two sides.
[1] 曹霞,刘国巍.基于社会资本的产学研合作创新超网络分析[J].管理评论,2013,25(4):115-124.
[2] Khan Z, Shenkar O, Lew Y K. Knowledge transfer from international joint ventures to local suppliers in a developing economy[J]. Journal of International Business Studies, 2015, 46(6):112.
[3] 刘芳.社会资本对产学研合作知识转移绩效影响的实证研究[J].研究与发展管理,2012,24(1):103-111.
[4] 曹霞,于娟,张路蓬.不同联盟规模下产学研联盟稳定性影响因素及演化研究[J].管理评论,2016,28(2):3-14.
[5] Lai J, Lui S S, Tsang E W K. Intrafirm knowledge transfer and employee innovative behavior:the role of total and balanced knowledge flows[J]. Journal of Product Innovation Management, 2015,33(1):90-103.
[6] 高丽坤,艾时钟.企业间知识转移的泄密问题研究[J].中国管理科学,2015,23(S1):364-369.
[7] Lin Lihui, Geng Xianjun, Whinston A B. A sender-receiver framework for knowledge transfer[J]. MIS quarterly, 2005,29(2):197-219.
[8] 李洋,刘伟.基于演化博弈理论的软件隐形需求开发研究[J].系统管理学报,2015,24(4):480-495.
[9] Jiang Zhangsheng, Hao Yunhong. Game analysis of technology innovation alliance stability based on knowledge transfer[J]. Computational & Mathematical Organization Theory, 2013, 19(4):403-421.
[10] Li Y M,Li J H. Knowledge sharing in communities of practice:A game theoretic analysis[J]. European Journal of Operational Research, 2010, 207(2):1052-1064.
[11] Bandyopadhyay S, Pathak P.Knowledge sharing and cooperation in outsourcing projects——A game theoretic analysis[J]Decision Support Systems,2007,43 (2):349-358.
[12] Szulanski G, Ringov D, Jensen R J. Overcoming stickiness:How the timing of knowledge transfer methods affects transfer difficulty[J]. Organization Science, 2016, 27(2):304-322.
[13] 许学国,梅冰青,吴耀威.跨国公司-代工企业知识转移主体行为研究[J].经济与管理研究,2016,37(4):119-126.
[14] 蒋樟生,胡珑瑛.技术创新联盟知识转移决策的主从博弈分析[J].科研管理,2012,33(4):41-47+61.
[15] 董广茂,林敏,雷宏振. 竞争条件下企业知识转移战略及其对产业演化的影响[J].管理学报,2014,11(8):1246-1252.
[16] 陈果,齐二石.风险态度不成对下的企业间知识转移研究[J].中国管理科学,2016,24(12):82-90.
[17] Haurie A, Krawczyk J. An introduction to dynamic games[M].2000.
[18] Chen K, Xiao Tiaojun. Ordering policy and coordination of a supply chain with two-period demand uncertainty[J]. European Journal of Operational Research, 2011, 215(2):347-357.
[19] Hamdouch Y Multi-period supply chain network equilibrium with capacity constraints and purchasing strategies[J].Transportation Research, Part C, 2011, 19(5):803-820.
[20] Dong F, Deglise-Hawkinson J R, Van Oyen M P, et al. Dynamic control of a closed two-stage queueing network for outfitting process in shipbuilding[J]. Computers & Operations Research,2016.72:1-11.
[21] Liu Zugang, Nagurney A. Multiperiod competitive supply chain networks with inventorying and a transportation network equilibrium reformulation[J].Optimization and Engineering, 2012, 13(2):471-503.
[22] Venayak N, Anesiadia N, Cluett W R, et al. Engineering metabolism through dynamic control[J].Current Opinion in Biotechnology,2015,34:142-152.
[23] Antonelli M, Baccioli A, Francesconi M, et al. Dynamic modelling of a low-concentration solar power plant:A control strategy to improve flexibility[J]. Renewable Energy,2016.95:574-585.
[24] Su Zhilong, Li Xiaokang, Jiang Xuesong, et al. Dynamic control of the location of nanoparticles in hybrid co-assemblies[J]. Nanoscale, 2015, 7(12):5262-9.
[25] 黄孝鹏,周献中.复杂决策任务层级分解动态控制策略与算法[J].军事运筹与系程,2013,27(1):71-75.
[26] 李恒,张友安,孙富春,等.多操纵面飞机全局集合稳定非线性自适应动态控制分配[J].控制与决 策,2013,28(3):379-390.
[27] 彭兰梅.施工进度计划的动态控制在高速公路改造工程中的应用[J].中外公路,2013,33(5):349-351.
[28] 谢文博,付明玉,施小成.一类非线性系统的加速度规划输出跟踪动态控制[J].控制理论与应用,2013,27(1):71-75.
[29] Huang Jiajiang, Liu Xin, Li Dan, et al. Dynamic control of enhancer repertoires drives lineage and stage-specific transcription during hematopoiesis[J]. Developmental cell, 2016, 36(1):9-23.
[30] Ferrer P, Castillo-Neyra R, Roy C N, et al. Dynamic control of hepatic Plasmodium numbers by hepcidin despite elevated liver iron during iron supplementation[J]. Microbes and Infection, 2016, 18(1):48-56.
[31] Kamimura M, Sugawara M, Yamamoto S, et al. Dynamic control of cell adhesion on a stiffness-tunable substrate for analyzing the mechanobiology of collective cell migration[J]. Biomaterials science, 2016, 4(6):933-937.
