| 网约车乘客隐私保护的演化博弈研究 |
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| 引用本文： | 董振宁,王俊杰,罗克文,陈浪城. 网约车乘客隐私保护的演化博弈研究[J]. 广东工业大学学报, 2023, 40(1): 19-28. DOI: 10.12052/gdutxb.220097 |
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| 作者姓名： | 董振宁 王俊杰 罗克文 陈浪城 |
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| 作者单位： | 1. 广东工业大学 管理学院，广东 广州 510520;2. 广东工业大学 人事处，广东 广州 510006;3. 广东工业大学 网络信息与现代教育技术中心，广东 广州 510006 |
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| 基金项目： | 广东省哲学社会科学规划学科共建项目(GD18XJY07)；广东省哲学社会科学规划项目(GD21CJY24) |
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| 摘 要： | 为了研究网约车隐私保护问题，本文构建了政府(监管，不监管)和平台(自律，不自律)的演化博弈模型，运用复制动态方程寻找演化稳定策略，并运用 Matlab 软件模拟不同情形下的演化路径，分析政府监管带来声誉提升的程度等4个参数对演化路径的影响。研究发现：(1) 政府监管收益大于不监管，平台自律收益大于不自律时，会出现(监管，自律)的理想结果；(2) 平台自律成本较低、政府监管成本较高时，即使政府不监管，平台也会自律；(3) 公众对政府监管给予肯定的程度提升、政府监管成本降低会引导政府由不监管转化为监管；(4) 平台获得来自社会的正向收益提高、自律成本降低会使平台由不自律转化为自律。这些结论对于政府优化监管政策设计具有指导意义。
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| 关 键 词： | 网约车 隐私保护 演化博弈 Matlab仿真 |
| 收稿时间： | 2022-05-31 |
An Evolutionary Game Analysis of Online Car-hailing Passengers' Privacy Protection |
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| Dong Zhen-ning,Wang Jun-jie,Luo Ke-wen,Chen Lang-cheng. An Evolutionary Game Analysis of Online Car-hailing Passengers' Privacy Protection[J]. Journal of Guangdong University of Technology, 2023, 40(1): 19-28. DOI: 10.12052/gdutxb.220097 |
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| Authors: | Dong Zhen-ning Wang Jun-jie Luo Ke-wen Chen Lang-cheng |
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| Affiliation: | 1. School of Management, Guangdong University of Technology, Guangzhou 510520, China;2. Personnel Department, Guangdong University of Technology, Guangzhou 510006, China;3. Center of Campus Network & Modern Educational Technology, Guangdong University of Technology, Guangzhou 510006, China |
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| Abstract: | To study the privacy protection of online car-hailing, an evolutionary game model including the government (regulatory, non-regulatory) and platform (self-discipline, non-self-discipline) is constructed, using the replication dynamic equation to find an evolutionary stable strategy, and using Matlab to simulate the situation under different circumstances. The influences of four parameters, including the degree of reputation improvement brought by government regulation, on the evolution path, are analyzed. The study finds : (1) When the benefits of government regulation are greater than those of non-regulation, and the benefits of platform self-discipline are greater than those of non-self-regulation, ideal result (regulation, self-regulation) will appear; (2) When the cost of platform self-regulation is low and the cost of government regulation is high, even if the government does not supervise, the platform will also be self-disciplined; (3) The increased public affirmation of government regulation and the reduction of government regulation costs leads the government to transform from non-regulation to regulation; (4) When the platform obtains positive benefits from society and reduces the cost of self-discipline, its strategy changes from non-self-discipline to self-discipline. These conclusions have guiding significance for the government to optimize the design of regulatory policy. |
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| Keywords: | car-hailing privacy protection evolutionary game Matlab simulation |
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