Based on the perspective of government and enterprises, we explore the cooperative strategy and cost-sharing problem of cooperative open sharing of data between government and enterprises. In order to accurately analyze the data-opening strategies of government and enterprises, stochastic differential game theory is applied to construct the Nash non-cooperative game, Stackelberg master-slave game and cooperative game models with government and enterprises as game subjects to obtain the optimal open data effort, the optimal trajectory of social data open sharing level and the optimal benefit function of government and enterprises in three scenarios. Combined with numerical simulations to analyze the sensitivity of the relevant parameters affecting the level of social data openness, the results of the study revealed the following: ① When the government's income distribution ratio is greater than 1/3, the benefits of the government and the enterprises under the Stackelberg master-slave game and the effort to open and share data are greater than in the Nash non-cooperative situation; in the case of a cooperative game, the degree of effort and total revenue of both parties reach the Pareto optimal state. ② When the government's income distribution ratio is greater than 1/3, the expectation and variance of the open data and shared stock under the cost-sharing situation and the corresponding limit value are all greater than the value in the Nash non-cooperative situation, and in the cooperative game, the expectation and variance of open data and shared stock and its corresponding limit value are the greatest. ③ The government and enterprises coexist with profit and risk under the influence of random interference factors, and high profit means high risk. This research provides a theoretical basis and practical guidance for promoting the open sharing of government and enterprise data.
Citation: Zifu Fan, Youpeng Tao, Wei Zhang, Kexin Fan, Jiaojiao Cheng. Research on open and shared data from government-enterprise cooperation based on a stochastic differential game[J]. AIMS Mathematics, 2023, 8(2): 4726-4752. doi: 10.3934/math.2023234
Based on the perspective of government and enterprises, we explore the cooperative strategy and cost-sharing problem of cooperative open sharing of data between government and enterprises. In order to accurately analyze the data-opening strategies of government and enterprises, stochastic differential game theory is applied to construct the Nash non-cooperative game, Stackelberg master-slave game and cooperative game models with government and enterprises as game subjects to obtain the optimal open data effort, the optimal trajectory of social data open sharing level and the optimal benefit function of government and enterprises in three scenarios. Combined with numerical simulations to analyze the sensitivity of the relevant parameters affecting the level of social data openness, the results of the study revealed the following: ① When the government's income distribution ratio is greater than 1/3, the benefits of the government and the enterprises under the Stackelberg master-slave game and the effort to open and share data are greater than in the Nash non-cooperative situation; in the case of a cooperative game, the degree of effort and total revenue of both parties reach the Pareto optimal state. ② When the government's income distribution ratio is greater than 1/3, the expectation and variance of the open data and shared stock under the cost-sharing situation and the corresponding limit value are all greater than the value in the Nash non-cooperative situation, and in the cooperative game, the expectation and variance of open data and shared stock and its corresponding limit value are the greatest. ③ The government and enterprises coexist with profit and risk under the influence of random interference factors, and high profit means high risk. This research provides a theoretical basis and practical guidance for promoting the open sharing of government and enterprise data.
[1] | J. Wang, Y. Wang, F. Cao, Game analysis of open government data sharing in the era of big data-based on the dynamic model of incomplete information, Intell. Sci., 36 (2018). https://doi.org/10.13833/j.issn.1007-7634.2018.11.003 doi: 10.13833/j.issn.1007-7634.2018.11.003 |
[2] | McKinsey Digital, Open data: unlocking innovation and performance with liquid information, 2013. Available from: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/open-data-unlocking-innovation-and-performance-with-liquid-information. |
[3] | People's Post and Telecommunications, Deepen the sharing and utilization of government and enterprise data, and promote the development of the data element market, 2020. Available from: https://www.cnii.com.cn/rmydb/202005/t20200506_174099.html. |
[4] | L. Di, Research on open government data abroad, Library Forum, 2014, 86–93. https://doi.org/doi:10.3969/j.issn.1002-1167.2014.09.015 doi: 10.3969/j.issn.1002-1167.2014.09.015 |
[5] | M. Wu, The use of open data in the UK and US and insights, Library Infor., 2012,127–130. https://doi.org/10.3969/j.issn.1003-6938.2012.01.026 doi: 10.3969/j.issn.1003-6938.2012.01.026 |
[6] | J. Chu, Content analysis of New Zealand's open government data website and implications for China, Mod. Intell., 38 (2018), 79–83. https://doi.org/10.3969/j.issn.1008-0821.2018.11.014 doi: 10.3969/j.issn.1008-0821.2018.11.014 |
[7] | J. Chu, M. Wang, U.S. open data sharing strategies and implications for China, Intell. Theory Practice, 42 (2019), 153–158. https://doi.org/10.16353/j.cnki.1000-7490.2019.08.027 doi: 10.16353/j.cnki.1000-7490.2019.08.027 |
[8] | Y. Sun, S. Zhao, F. Zhang, X. Li, A comparative study on the guarantee mechanism for open sharing of government data and information in China, the United States and the United Kingdom, Library Intell. Work, 62 (2018), 5–14. https://doi.org/10.13266/j.issn.0252-3116.2018.21.001 doi: 10.13266/j.issn.0252-3116.2018.21.001 |
[9] | M. Chen, Study on the safeguarding mechanism of open data against corruption in France, Intell. Mag., 38 (2019), 155–161. https://doi.org/10.3969/j.issn.1002-1965.2019.01.024 doi: 10.3969/j.issn.1002-1965.2019.01.024 |
[10] | A. Blesa, D. Íñiguez, R. Moreno, G. Ruiz, Use of open data to improve automobile insurance premium rating, Int. J. Market Res., 62 (2020), 58–78. https://doi.org/10.1177/1470785319862734 doi: 10.1177/1470785319862734 |
[11] | B. L. Sullivan, T. Phillips, A. A. Dayer, C. L. Wooda, A. Farnswortha, M. J. Iliffa, et al., Using open access observational data for conservation action: a case study for birds, Biol. Conserv., 208 (2017), 5–14. https://doi.org/10.1016/j.biocon.2016.04.031 doi: 10.1016/j.biocon.2016.04.031 |
[12] | R. O. Gilmore, K. E. Adolph, D. S. Millman, A. Gordon, Transforming education research through open video data sharing, Adv. Eng. Educ., 5 (2016), 1–17. |
[13] | M. J. Pencina, D. M. Louzao, B. J. McCourt, M. R. Adams, R. H. Tayyabkhan, P. Ronco, et al., Supporting open access to clinical trial data for researchers: the Duke Clinical Research Institute-Bristol-Myers Squibb Supporting Open Access to Researchers Initiative, Am. Heart J., 172 (2016), 64–69. https://doi.org/10.1016/j.ahj.2015.11.002 doi: 10.1016/j.ahj.2015.11.002 |
[14] | F. Huettmann, M. Schmid, G. Humphries, A first overview of open access digital data for the Ross Sea: complexities, ethics, and management opportunities, Hydrobiologia, 761 (2015), 97–119. https://doi.org/10.1007/s10750-015-2520-x doi: 10.1007/s10750-015-2520-x |
[15] | K. C. Boschmann, U. M. Angst, A. M. Aguilar, B. Elsener, A novel approach to systematically collect critical chloride contents in concrete in an open access data base, Data Brief, 27 (2019), 104675. https://doi.org/10.1016/j.dib.2019.104675 doi: 10.1016/j.dib.2019.104675 |
[16] | B. Fan, W. Fan, C. Smithc, H. Garnerde, Adverse drug event detection and extraction from open data: a deep learning approach, Inform. Process. Manag., 57 (2020), 102131. https://doi.org/10.1016/j.ipm.2019.102131 doi: 10.1016/j.ipm.2019.102131 |
[17] | J. A. Smith, A. L. Benson, Y. Chen, S. A. Yamada, M. C. Mims, The power, potential, and pitfalls of open access biodiversity data in range size assessments: lessons from the fishes, Ecol. Indic., 110 (2020), 105896. https://doi.org/10.1016/j.ecolind.2019.105896 doi: 10.1016/j.ecolind.2019.105896 |
[18] | J. Wang, Y. Li, Research on the quality control mechanism of government open data based on evolutionary game theory, Mod. Intell., 39 (2019), 93–102. https://doi.org/10.3969/j.issn.1008-0821.2019.01.012 doi: 10.3969/j.issn.1008-0821.2019.01.012 |
[19] | L. Cui, L. Zhai, X. Zhu, A study on inter-governmental information disclosure at the same level based on an evolutionary game, Intell. Theory Practice, 39 (2016), 56–60. https://doi.org/10.16353/j.cnki.1000-7490.2016.06.011 doi: 10.16353/j.cnki.1000-7490.2016.06.011 |
[20] | X. Li, H. Jiang, An evolutionary game analysis of promoting government data opening and Enterprise utilization, Proceedings of the 13th Annual China Management Conference, 2018,464–471. |
[21] | Y. Wei, X. Chen, X. Zhou, Data sharing, corporate strategies and government monitoring incentives - based on an evolutionary game analysis, Financ. Sci., 4 (2020), 107–120. |
[22] | X. Xu, Y. Li, Q. Pang, Evolutionary game analysis of government open data sharing in the digital economy, Intell. Mag., 39 (2020). https://doi.org/10.3969/j.issn.1002-1965.2020.12.018 doi: 10.3969/j.issn.1002-1965.2020.12.018 |
[23] | S. Yin, N. Zhang, K. Ullah, S. Gao, Enhancing digital innovation for the sustainable transformation of manufacturing industry: a pressure-state-response system framework to perceptions of digital green innovation and its performance for green and intelligent manufacturing, Systems, 10 (2022), 72. https://doi.org/10.3390/systems10030072 doi: 10.3390/systems10030072 |
[24] | R. Hu, Deep fictitious play for stochastic differential games, Commun. Math. Sci., 19 (2021), 325–353. https://doi.org/10.4310/cms.2021.v19.n2.a2 doi: 10.4310/cms.2021.v19.n2.a2 |
[25] | D. Ma, J. Stochastic, Differential game model of closed-loop supply chain with Retailer's relatively fairness, Chin. J. Manag., 15 (2018), 467–474. https://doi.org/10.3969/j.issn.1672-884x.2018.03.019 doi: 10.3969/j.issn.1672-884x.2018.03.019 |
[26] | H. Zhu, Y. Liu, C. Zhang, G. Zhang, Strategies of knowledge sharing in synergetic innovation based on stochastic differential game, Sci. Res. Manag., 38 (2017), 17–25. https://doi.org/10.19571/j.cnki.1000-2995.2017.07.003 doi: 10.19571/j.cnki.1000-2995.2017.07.003 |
[27] | Q. Wang, Z. Yuan, T. Jiang, Research on knowledge sharing strategy of collaborative innovation system under random factor interference, Sci. Technol. Manag. Res., 39 (2019), 139–145. https://doi.org/10.3969/j.issn.1000-7695.2019.10.020 doi: 10.3969/j.issn.1000-7695.2019.10.020 |
[28] | X. Song, G. Zhang, X. Zhang, A study on government-enterprise disaster relief coordination strategy considering random interference, Math. Practice Theory, 50 (2020), 135–146. |
[29] | Y. Lu, C. Zhang, H. Zhu, Stochastic differential game for linear meirkov switching system with poisson jumps and its appplication to financial market, J. Syst. Sci. Math. Sci., 38 (2018), 537–552. https://doi.org/10.12341/jssms13399 doi: 10.12341/jssms13399 |
[30] | L. Cheng, X. Zhu, J. Lu, A study on benefit distribution of government data open sharing platform in the context of big data-based on the perspective of synergy effect, Intell. Theory Practice, 42 (2019), 71–75. https://doi.org/10.16353/j.cnki.1000-7490.2019.04.013 doi: 10.16353/j.cnki.1000-7490.2019.04.013 |
[31] | M. Wang, Y. Liu, W. Shi, M. Li, C. Zhong, A study on the collaborative sharing strategy and emission reduction benefits of low carbon technologies in different places under carbon trading policy, Syst. Eng. Theory Practice, 39 (2019), 1419–1434. https://doi.org/10.12011/1000-6788-2017-1748-16 doi: 10.12011/1000-6788-2017-1748-16 |
[32] | Z. Fan, J. Cheng, Research on data opening strategy and cooperation benefit distribution mechanism based on differential game, Oper. Manag., 30 (2020), 100–107. |
[33] | M. Hedayati, H. A. Tehrani, A. F. Jahromi, M. H. N. Skandari, D. Baleanu, A novel high accurate numerical approach for the time-delay optimal control problems with delay on both state and control variables, AIMS Math., 7 (2020), 9789–9808. https://doi.org/10.3934/math.2022545 doi: 10.3934/math.2022545 |
[34] | C. D. Nyoumbi, A. Tambue, A fitted finite volume method for stochastic optimal control problems in finance, AIMS Math., 6 (2021), 3053–3079. https://doi.org/10.3934/math.2021186 doi: 10.3934/math.2021186 |
[35] | W. Choi, Y. Choi, A sharp error analysis for the DG method of optimal control problems, AIMS Math., 7 (2022), 9117–9155. https://doi.org/10.3934/math.2022506 doi: 10.3934/math.2022506 |
[36] | W. Zhang, C. Liu, L. Lin, J. Jiao, A two-stage study on the allocation of carbon emission reduction targets for supply chains, China Manag. Sci., 29 (2021), 90–101. https://doi.org/10.16381/j.cnki.issn1003-207x.2019.0268 doi: 10.16381/j.cnki.issn1003-207x.2019.0268 |
[37] | Q. Xu, Algorithmic design of equilibrium bidding strategies for bargaining games, Comput. Eng. Appl., 56 (2020), 170–175. https://doi.org/10.3778/j.issn.1002-8331.2004-0408 doi: 10.3778/j.issn.1002-8331.2004-0408 |
[38] | W. Deng, L. Dai, Z. Zhang, Z. Fan, A study on the coordination mechanism of government data sharing based on evolutionary game, Intell. Sci., 2022, 1–10. |
[39] | S. Yin, T. Dong, B. Li, S. Gao, Developing a conceptual partner selection framework: digital green innovation management of prefabricated construction enterprises for sustainable urban development, Buildings, 12 (2022), 721. https://doi.org/10.3390/buildings12060721 doi: 10.3390/buildings12060721 |