网约车新政下网约车平台与网约车司机的演化博弈分析

雷丽彩，高 尚，蒋 艳

网约车新政下网约车平台与网约车司机的演化博弈分析

雷丽彩，高 尚，蒋 艳

（湘潭大学商学院，湖南 湘潭 411105）

近年来，网约车等新业态的兴起给乘客带来了新体验，同时也导致新旧矛盾交织以及利益关系碰撞。本文运用演化博弈理论分析方法，建立了新政实施背景下网约车平台和司机间的演化博弈模型，并对其博弈行为演化过程及演化稳定策略进行探讨。理论研究和仿真结果表明：政府部门对网约车市场的大力调控可以有效保护网约车平台“严格管理”的积极性，（合法营运,严格管理）成为唯一的演化稳定策略；但是当政府调控力度较小时，网约车平台“严格管理”的净收益小于其“消极管理”的净收益，使得其“严格管理”的积极性降低，从而滋长、纵容网约车司机“非法营运”行为的发生，（非法营运,消极管理）也可能成为演化博弈的稳定策略。要实现网约车平台“严格管理”率和司机“合法营运”率达到理想状态并可以长期保持，应该加大对策略对（非法营运,消极管理）的识别并给予网约车司机“非法营运”以较高的惩罚，并对网约车平台“严格管理”辅以适当的补偿措施。

网约车运营；网约车新政；演化博弈；演化稳定策略

0 引言

随着全球信息技术的快速发展和“互联网+”时代的到来，网络化潮流正在席卷全世界，以 Airbnb、Uber和滴滴出行为代表的共享经济（Sharing Economy）迅速崛起，使人类生活的各个方面都受到巨大的影响，城市居民的交通出行领域也不例外。在共享经济之风的劲吹下，网络约租车（以下简称“网约车”）也应运而生。网约车，也叫互联网专车，在交通运输部发布的《网络预约出租汽车经营服务管理暂行办法（征求意见稿）》中被定义为“以互联网技术为依托构建服务平台，接入符合条件的车辆和驾驶员，通过整合供需信息，提供非巡游的预约出租汽车服务”[1]。2016年7月28日，交通运输部联合公安部等七部门正式公布了《网络预约出租汽车经营服务管理暂行办法》（以下皆简称为“新政”），网约车获得合法身份；8月1日，滴滴和Uber（中国）宣布合并。新政的出台以及垄断巨头的酝酿诞生，意味着网约车行业将面临更多的行政许可和更少的补贴，同时也将触发网约车行业的利益相关者（stakeholders）如乘客、司机与网约车平台以及传统出租车行业的新一轮自主博弈。因此，在新政实施背景下研究网约车平台与网约车司机之间的演化博弈行为，为政府有关部门制定相应的监管措施提供更好的决策支持，具有比较重要的现实意义。

网约车从出现伊始，就在全世界各地引发了媒体和社会大众的广泛关注和争议，也是学者们讨论的热点问题之一。近年来，国内外已有不少学者对于政府监管下的网约车安全监管及其规制范式进行了研究[2]：如侯登华在阐述网约车经营模式和发展阶段的基础上，比较分析了新加坡、美国、英国和法国等对网约车的监管路径[3]。周丽霞介绍了Uber的运营模式，提出从准入条件、司机要求、车辆要求、保险服务和隐私保护等方面借鉴美国对Uber的监管经验[4]。罗清和等在总结西方发达国家“规制（Regulation）——放松规制（Deregulation）——再规制（Re-regulation）”的经验基础上，提出了适合我国国情的网约车监管路径选择[5]。尹贻林和杨旋从博弈论的角度探讨了新兴移动打车软件对我国传统出租车市场均衡的影响[6]。政府对出租车市场的监管所带来的经济效应也被广泛研究[7]，如Cetin和Eryigit通过实证研究表明，政府监管提高了出租车市场的牌照价格，导致出租车费上升[8]。Bengtsson通过试验研究表明有效的政府管制能提高效率，从而实现帕累托改进[9]。

以上研究从理论探讨和案例分析角度为网约车的运营监管实践提供了可资借鉴的方法和建议，但是这些研究大多停留在定性的描述和推理阶段，关于网约车涉及的多元利益相关者之间博弈关系的研究尚未引起足够的关注。虽然演化博弈理论被广泛应用到社会经济和管理领域的各类实际问题中[10]，如地方政府与企业的高耗能产业退出机制[11]，我国土地囤积与土地监察的困境[12]，碳减排标签政策[13]，双寡头再制造的进入决策[14]，研发外包决策[15]，重大突发事件的防控措施[16]，合作溢出的机会主义行为[17]，出行方式选择行为[18]，逆向拍卖的分组评标行为[19]，食品供应商与制造商的质量投入博弈[20]，技术创新模式选择[21,22]，创新中小企业与商业银行的信贷博弈[23]，供应商和制造商的绿色采购博弈[24]，煤矿安全监管博弈[25,26]，石油市场支配地位博弈[27]，网络化智能电网的需求侧管控博弈[28]以及农民创业者供给村庄公共品的行为[29]]等，将该理论应用于电子商务交易行为的研究虽然也取得了一定的成果[30-32]，但是鲜有关于网约车市场利益相关者之间演化博弈行为的研究。而网约车市场是一个典型的双边市场，市场中形成了一个以网约车平台为中介，联结司机和乘客的三位一体的商业生态系统，三者相互依赖，相互影响。

因此，在新政实施和行业垄断巨头酝酿诞生的背景下，网约车平台和网约车司机会发生怎样的自主博弈行为呢？在这一过程中，影响双方博弈的均衡结果和稳定性的因素有哪些？为了回答上述问题，本文尝试借助演化博弈理论，深入研究新政出台背景下网约车平台和网约车司机之间的行为演化关系，在政府的监管调控下，分析影响演化博弈均衡结果的关键因素，为政府制定科学合理的网约车安全监管机制提供借鉴和参考。

1 新政背景下网约车平台与网约车司机的演化博弈模型

新政的出台尽管使网约车获得了“合法身份”，但同时也意味着网约车行业将面临更多的行政许可和更少的补贴，对于网约车平台和网约车司机也提出了更严格的要求。一方面，新政要求网约车平台按照相关规定对网约车司机进行严格监管，增加了网约车平台的运营成本；另一方面对于网约车司机而言，由于网约车所具有的“共享经济”本质，网约车司机本不用像巡游出租车司机一样面对严格的准入管制，也不需要向出租车公司缴纳“份子钱”，由此吸引了大批尝鲜者，然而，新政对网约车司机提出的更加严格的监管和限制条件，使得网约车司机还没来得及咀嚼分享经济的成果，就被推向去与留的十字路口。

1.1 网约车平台与网约车司机博弈模型建立

由此建立网约车平台和网约车司机之间博弈的支付矩阵如表1所示。

表1 新政背景下网约车平台与网约车司机博弈的支付矩阵

1.2 博弈模型的均衡解

根据支付矩阵，可以算出在新政实施的背景下以及政府有关部门的管制下，网约车司机的期望收益为：

则网约车司机的复制动态方程为：

同理，在新政实施的背景下以及政府有关部门的管制下，网约车平台的期望收益为：

则网约车平台的复制动态方程为：

2 模型演化动态的策略稳定性分析

2.1 情形①：网约车平台“严格管理”的净收益大于其“消极管理”的净收益的稳定性分析

表2 情形①时网约车平台与网约车司机之间演化博弈的稳定性分析

图1 情形①时网约车平台与网约车司机之间演化博弈的复制动态相位图

Figure 1 Copying dynamic phase diagram of evolutionary game between Internet-chauffeured-car platform and internet-chauffeured-car driver in case ①

2.2 网约车平台“严格管理”的净收益小于其“消极管理”收益时的稳定性分析

2.2.1情形②的策略稳定性分析

表3 情形②时网约车平台与网约车司机之间演化博弈的稳定性分析

图2 情形②时网约车平台与网约车司机之间演化博弈的复制动态相位图

Figure 2 Copying dynamic phase diagram of evolutionary game between net car platform and net car driver in case ②

2.2.2情形③的策略稳定性分析

表4 情形③时网约车平台与网约车司机之间演化博弈的稳定性分析

图3 情形②时网约车平台与网约车司机之间演化博弈的复制动态相位图

Figure 3 Copying dynamic phase diagram of evolutionary game between internet-chauffeured-car platform and internet-chauffeured-car driver in case ②

3 仿真算例

图4 网约车平台与网约车司机之间演化博弈的策略演化路径图

Figure 4 Strategy evolution path chart of evolutionary game between internet-chauffeured-car platform and internet-chauffeured-car driver

4 结论与建议

在政府监管部门的管制下，网约车平台与网约车司机的演化博弈，面临不同情况的初始条件会有不同的演化稳定策略，为了使网约车行业朝着一个良好的发展态势，针对严格管理与消极管理的网约车平台的净收益差异，政府监管部门需要改变对网约车平台的补偿力度和惩罚力度，提高对网约车平台的监管，使网约车平台更能积极主动的对网约车司机进行严格管理，从而引导网约车司机正常营运。

[1] 交通运输部.网络预约出租汽车经营服务管理暂行办法（交通运输部工业和信息化部公安部商务部工商总局质检总局国家网信办令 2016 年第 60 号）[Z]. 2016.

Ministry of Transport. Interim measures for the administration of online taxi management services (Ministry of Commerce and Information Technology, Ministry of Transport, Ministry of Commerce, Ministry of Commerce, Ministry of Commerce and Industry, General Administration of Quality Supervision, Inspection and Quarantine, National Network Letters, No. 60, 2016) [Z]. 2016.

[2] 王静. 中国网约车的监管困境及解决[J]. 行政法学研究, 2016, (02): 49-59.

Wang J. Regulatory quandary and its solutions of Internet chauffeured car in China[J]. Administrative Law Review,2016,(02):49-59.

[3] 侯登华.网约车规制路径比较研究—兼评交通运输部《网络预约出租汽车经营服务管理暂行办法（征求意见稿）》[J]. 北京科技大学学报（社会科学版）, 2015,31(6): 96-103.

Hou D H. Legitimacy and supervision system of Internet private hire vehicles[J]. Journal of University of Science and Technology Beijing(Social Sciences Edition), 2015,31(6): 96-103.

[4] 周丽霞. 规范国内打车软件市场的思考——基于美国对Uber商业模式监管实践经验借鉴[J]. 价格理论与实践, 2015,(7): 21-24.

Zhou L X. Thoughts on regulating the domestic taxi software market--based on the US experience in Uber business model supervision practice [J]. Price: Theory & Practice, 2015,(7): 21-24.

[5] 罗清和, 张畅, 潘道远. 我国“约租车”规制研究——兼及国外经验[J]. 北京交通大学学报（社会科学版, 2016,15(3): 31-37.

Luo Q H, Zhang C, Pan D Y. Regulation on Chinese on-demand ride services--A discussion based on foreign experience[J]. Journal of Beijing Jiaotong University(Social Sciences Edition), 2016,15(3): 31-37.

[6] 尹贻林,杨旋. 新兴移动打车软件对我国出租车市场均衡的影响[J]. 大连理工大学学报(社会科学版), 2016,37(2):65-70.

Yin Y L, Yang X. The impact of emerging taxi taking apps on market equilibrium in China[J]. Journal of Dalian University of Technology(Social Sciences), 2016,(02):65-70.

[7] Çetin T, Eryigit KY. The economic effects of government regulation: Evidence from the New York taxicab market[J]. Transport Policy, 2013,1(25): 169-177.

[8] Noble A W. The effectiveness of local government regulation of the taxi trade[D]. University of Birmingham,2014.

[9] Bengtsson N. Efficient informal trade: Theory and experimental evidence from the CapeTown taxi market [J]. Journal of Development Economics, 2015, 115: 85-98.

[10] Weibull, Jorgen W. Evolutionary Game Theory [M]. Shanghai: Shanghai People's Publishing House; Shanghai: Truth and Wisdom press, 2015.05.

[11] 郭本海,方志耕,刘卿. 基于演化博弈的区域高耗能产业退出机制研究[J]. 中国管理科学, 2012,20(4): 79-85

Guo B H, Fang Z G, Liu Q. Study of regional energy-intensive industry’s exit mechanism based on evolutionary game[J]. Chinese Journal of Management Science, 2012,20(4): 79-85

[12] Zhang X L, Bao H J, Skitmore M. The land hoarding and land inspector dilemma in China: An evolutionary game theoretic perspective [J]. Habitat International, 2015,46: 187-195

[13] Zhao R, Zhou X, Han J J, et al. For the sustainable performance of the carbon reduction labeling policies under an evolutionary game simulation [J]. Technological Forecasting & Social Change, 2016,112: 262-274

[14] 郭军华,李帮义,倪明. 双寡头再制造进入决策的演化博弈分析[J]. 系统工程理论与实践, 2013,33(2): 370-377.

Guo J H, Li B Y, Ni M. Evolutionary game analysis of duopoly's remanufacturing entry decision[J]. Systems Engineering —Theory & Practice, 2013,33(2): 370-377.

[15] 汪勇杰,陈通,邓斌超. 政府补贴机制下研发外包的演化博弈分析[J]. 管理工程学报, 2017,31(2): 137-142.

Wang Y J, Chen T, Deng B C. Evolutionary game analysis of research and development outsourcing based on government subsidy mechanism[J]. Journal of Industrial Engineering and Engineering, 2017,31(2): 137-142.

[16] 刘德海,王维国,孙康. 基于演化博弈的重大突发公共卫生事件情景预测模型与防控措施[J]. 系统工程理论与实践, 2012,32(5): 937-946.

Liu D H, Wang W G, Sun K. Scenario forecasting model and prevention-control measurements of important public health event based evolutionary game[J]. Systems Engineering —Theory & Practice, 2012,32(5): 938-946.

[17] 邢乐斌,王旭. 基于合作溢出的机会主义行为演化博弈分析[J]. 管理工程学报, 2011, 25(3): 68-72.

Xing L B, Wang X. Evolutionary game analysis on opportunistic behavior based on cooperative spillover[J]. Journal of Industrial Engineering and Engineering Management, 2011, 25(3): 68-72.

[18] 肖海燕,王先甲. 政府参与模式下出行者出行方式选择行为的演化博弈分析[J]. 管理工程学报, 2010, 24(2): 115-118.

Xiao H Y, Wang X J. A study of trip model choice behavior of traveler based on the evolutionary game under the participation of government[J]. Journal of Industrial Engineering and Engineering Management, 2010, 24(2): 115-118.

[19] 刘旭旺,汪定伟. 分组评标专家行为的演化博弈分析[J]. 管理科学学报, 2015,18(1): 50-61.

Liu X W, Wang D W. Analysis of referee experts ’behaviors for grouped bid evaluation based on evolutionary game[J]. Journal of Management Sciences in China, 2015,18(1): 50-61.

[20] 许民利,王俏,欧阳林寒. 食品供应链中质量投入的演化博弈分析[J]. 中国管理科学, 2012,20(5): 131-141.

Xu M L, Wang Q, Ouyang L H. Investment decision of food supply chain quality based on the evolutionary game[J]. Chinese Journal of Management Science, 2012,20(5): 131-141.

[21] 戴园园,梅强. 我国高新技术企业技术创新模式选择研究——基于演化博弈的视角[J]. 科研管理, 2013,34(1): 2-10

Dai Y Y, Mei Q. The technological innovation mode selection of Chinese new and high-tech enterprises based on evolutionary game[J]. Science Research Management, 2013,34(1): 2-10

[22] Canbolat E O, Beraha A., Bas A. Application of evolutionary game theory to strategic innovation [J]. Procedia-Social and Behavioral Sciences, 2016,235: 685-693

[23] 梁益琳,张玉明. 创新型中小企业与商业银行的演化博弈及信贷稳定策略研究[J]. 经济评论,2012,(1): 16-24

Liang Y L, Zhang Y M. Research on evolutionary game and credit stable strategies between innovative SMEs and commercial banks[J]. Economic Review, 2012,(1): 16-24

[24] Ji P., Ma X., Li G. Developing green purchasing relationships for the manufacturing industry: An evolutionary game theory perspective [J]. Int. J. Production Economics, 2015,166: 155-162

[25] Liu D H, Xiao X Z, Li H Y, et al. Historical evolution and benefit–cost explanation of periodical fluctuation in coal mine safety supervision: An evolutionary game analysis framework [J]. European Journal of Operational Research, 2015,243: 974-984

[26] Liu Q L, Li X C, Hassall M. Evolutionary game analysis and stability control scenarios of coal mine safety inspection system in China based on system dynamics [J]. Safety Science, 2015,80: 13-22

[27] Wood A D, Mason C F, Finnoff D. OPEC, the Seven Sisters, and oil market dominance: An evolutionary game theory and agent-based modeling approach [J]. Journal of Economic Behavior & Organization, 2016,132: 66-78

[28] Zhu B, Xia XH, Wu Z. Evolutionary game theoretic demand-side management and control for a class of networked smart grid [J]. Automatica, 2016,70: 94-100

[29] 黄启发,庄晋财,尹金承. 基于演化博弈论的农民创业者供给村庄公共品行为研究[A]. 见: 管理科学与工程学会主编. 管理科学与工程学会2016年年会论文集[C]. 镇江: 江苏大学出版社, 2016, 272-283

Huang Q F, Zhuang J C, Yin J C. Research on the behavior of farmers-entrepreneurs supplying village public goods based on Evolutionary Game Theory[A]. See: Editor-in-Chief, Management Science and Engineering Society. Proceedings of the 2016 Annual Meeting of the Institute of Management Science and Engineering [C]. Zhenjiang: Jiangsu University Press, 2016, 272-283

[30] 周春林,邹丽芳. 电子商务交易双方的博弈模型分析[J]. 经济问题探索, 2010,(2):187-190

Zhou C L, Zou L F. Analysis of game model of both e-commerce transaction parties [J]. Inquiry into Economic Issues, 2010,(2): 187-190

[31] 蹇洁,袁恒,陈华. 第三方网络交易平台与网店经营主体进行博弈与交易监管[J]. 商业研究, 2014,(8): 142-149

Jian J, Yuan H, Chen H. Evolutionary game model and transaction regulation of the third-party network trading platform and online shop management entities[J]. Commercial Research, 2014,(8): 142-149

[32] 董瑞,陈琳,王先甲. 基于补偿-惩罚措施的双边平台的促合作控制研究[C]//管理科学与工程学会主编. 管理科学与工程学会2016年年会论文集[C]. 镇江: 江苏大学出版社, 2016: 409-424

Dong R, Chen L, Wang X J. Research on promoting cooperation control of bilateral platform based on compensation-punishment measures [A]. See: Editor-in-Chief, Management Science and Engineering Society. Proceedings of the 2016 Annual Meeting of the Institute of Management Science and Engineering [C]. Zhenjiang: Jiangsu University Press, 2016:409-424

[33] 谢志刚. “共享经济”的知识经济学分析——基于哈耶克知识与秩序理论的一个创新合作框架[J]. 经济学动态,2015,(12):78-87.

Xie Z G. The analysis of knowledge economy of "Sharing Economy"——An innovative cooperation framework based on Hayek's theory of knowledge and order [J]. Economics Information,2015, (12):78-87.

Evolutionary game analysis of car-hailing industry between platforms and drivers based on new policies of car-hailing

LEI Licai, GAO Shang, JIANG Yan

(Business School, Xiangtan University, Xiangtan 411105, China)

With the mushroomed growth of the sharing economy, platform economics represented by Airbnb, Uber, and Didi, has greatly affected all aspects of human life, especially in the area of urban residents' travel. Under the circumstance, car-hailing service also came into being. In the context of the rapid development of global information technology and the advent of the "Internet +" era, the demand for China's car-hailing market is growing. Moreover, a large number of stakeholders, such as car-hailing platforms, passengers, drivers, and traditional taxi industries are involved in the car-hailing industry. According to the behavioral decision theory, each person wants to be able to maximize its revenue, which will inevitably lead to conflicts among stakeholders. Consequently, it is of great significance to introduce and apply the control of the government to the car-hailing market. To a certain extent, the reinforcement of control will promote the healthy development of the car-hailing market.

As the promulgation of car-hailing regulation and the birth of industrial monopoly giants, the car-hailing market will face more administrative licensing and fewer subsidies. In the meanwhile, a new round of the independent game is triggered among the stakeholders. Multiple stakeholders of the car-hailing market are faced with new opportunities and challenges. The requirements for car-hailing platforms and drivers are becoming more stringent. Therefore, it is necessary to study the selection behavior of evolutionary game between car-hailing platforms and drivers, which will provide a useful reference for the government to play a regulatory role in maintaining the sustainable development of car-hailing service.

In the first part, according to the analytical method of evolutionary game theory, the evolutionary game model between car-hailing platforms and drivers is proposed, and a payoff matrix is established. On this foundation, the replicator dynamic equations are used to depict the evolutionary path of car-hailing platforms’ and drivers’ selection behavior under the control of the government. In the second part, based on the replicator dynamic equations, we consider different controls of the government over the car-hailing service to calculate the equilibrium point. We take advantage of the Jacobin matrix to analyze the stability of the equilibrium point and obtain the dynamical diagram of the evolutionary game and the evolutionarily stable strategy. In the third part, we conduct the simulation experiments of the evolutionary game mode between the car-hailing platforms and drivers，and the dynamical route of the evolutionary game is presented. Finally, the advice on government control of the car-hailing market is put forward in terms of the evolutionary game model results.

Theoretical research and simulation results indicate that the ratio of “strict management” for car-hailing platforms and the ratio of “legal operation” for drivers achieve and maintain perfect condition, which depends on the tremendous control of the government. When the tremendous control is exerted over the car-hailing market, the net income of the “strict management” is more than that of the “loose management” for the car-hailing platforms, which turns out that the only evolutionarily stable strategy is “Legal Operation, Strict Management,” which is what we have expected. Contrarily, when the government exerts the less control, the evolutionarily stable strategy may be “Illegal Operation, Loose Management” for lack of effective government regulation. The game falls into the prisoner’s dilemma, which goes against the sustainable development for the car-hailing industry. Based on the simulation experiments, the strategy of “Illegal Operation, Loose Management” should be identified. Simultaneously, it is necessary to impose a greater penalty on drivers with “illegal operation” and take appropriate compensation measures for car-hailing platforms with “strict management”，which will make the car-hailing platforms more active to rigidly manage drivers, and guide the “legal operation.”

Car-hailing service; New policies of car-hailing; Evolutionary game; Evolutionary stable strategy

2017-06-03

2017-09-15

Supported by the Ministry of Education Layout Foundation of Humanities and Social Science (19YJA630030), the Excellent Youth Project of Educational Commission of Hunan Province (17B267) and the Social Science Foundation of Hunan Province (17YBA369)

F570

A

1004-6062(2020)01-0055-008

10.13587/j.cnki.jieem.2020.01.007

2017-06-03

2017-09-15

教育部人文社会科学规划基金资助项目（19YJA630030）；湖南省教育厅优秀青年项目（17B267）；湖南省社科基金资助项目（17YBA369）

雷丽彩（1984—），女，湖南桂阳人；湘潭大学商学院副教授，南京大学图书情报与档案管理博士后；主要从事电子商务运营模式、行为决策理论及其应用方面的研究。

中文编辑：杜 健；英文编辑：Charlie C. Chen