基于演化博弈的数据服务商参与数据黑市治理研究

doi:10.13998/j.cnki.issn1002-1248.24-0768

摘要/Abstract

摘要：

[目的/意义] 探讨如何通过数据服务商参与数据黑市治理，重塑数据治理环境，有效遏制数据黑市。 [方法/过程] 采用演化博弈模型来模拟数据服务商、数据企业、数据黑市人员和监管方在数据市场中的动态策略互动。讨论如何通过机制设计，释放数据服务商遏制数据黑市的潜力。 [结果/结论] 模拟结果显示：通过政策、声誉、合作以及惩罚4个方面的机制设计，数据服务商参与治理机制可以减轻数据黑市监管压力，提高政策灵活性，降低治理成本。尤其在数据服务商和数据企业都追求长期合作与声誉效益时效果最显著。具体而言，1）政策支持能够激发数据服务商在治理黑市中的潜力和积极性；2）在声誉和合作机制下，数据服务商参与黑市治理不仅可以帮助监管方和企业减轻监管压力，还能够增强数据企业主动打击黑市交易的意愿；3）合理的政府监管和惩罚制度可以确保数据服务商有效参与治理黑市。相应提出了数据服务商参与治理机制设计与维系建议，涵盖激励机制、多元合作、惩罚机制和技术保障4个方面。

关键词: 数据服务商, 数据黑市, 演化博弈, 参与治理, 数据治理

Abstract:

[Purpose/Significance] In the context of China's commitment to the development of the data element circulation market, the governance of the data black market has emerged as a pressing issue that demands urgent attention. The purpose of this paper is to discuss the implementation of the operating law of the market economy, the utilization of the interaction of multiple parties with interests in the data circulation market to achieve meaningful participation in the governance of data service providers, the curbing of the black market trading of data elements, and the achievement of the goal of reducing the government's management costs and helping the data trading market to break through the black market shroud. [Methods/Processes] A four-party evolutionary game model is used to simulate the dynamic interaction strategy choices and the results of evolutionary stabilization strategies of data service providers, data enterprises, data black market participants and regulators in data factor circulation configuration. The study explores the conditions necessary for transforming data service providers into active participants to validate the effectiveness of the data service providers' participation in the governance mechanism. The study also discusses the methods by which the goal of enhancing the effectiveness of the data service providers' participation in the governance mechanism may be achieved. This is accomplished through the design of multi-party interaction mechanisms, and the input-output ratio of the participation governance mechanism in curbing black market trading. [Results/Conclusions] The simulation results demonstrate that by designing mechanisms in four aspects: policy, reputation, cooperation, and punishment, the participation governance mechanism of data service providers can reduce the regulatory pressure on data black markets, enhance policy flexibility, and minimize governance costs. In particular, the effect is most significant when both data service providers and data enterprises pursue long-term cooperation and reputational benefits. Specifically, 1) policy support can stimulate the potential and motivation of data service providers in governing black markets. 2) Under the reputation and cooperation mechanism, the participation of data service providers in the governance mechanism can not only help the regulator and enterprises to reduce the regulatory pressure, but also enhance the willingness of data enterprises to take the initiative in combating black market transactions and form the synergy of multi-party collaborative governance. 3) Adequate government regulation and a suitable system of sanctions can ensure the effective participation of data service providers in the governance of the black market, and ensure that the participation of data service providers in the governance and government regulation forms a synergy. Accordingly, the design and maintenance of the mechanism for data service providers' participation in governance is proposed, covering four aspects: an incentive mechanism, multiple forms of cooperation, a penalty mechanism, and technical guarantee.

Key words: data service providers, data black markets, evolutionary gaming, participation in governance, data governance

中图分类号: G256.5

张峰领, 马小琪. 基于演化博弈的数据服务商参与数据黑市治理研究[J]. 农业图书情报学报, 2024, 36(11): 47-63.

Fengling ZHANG, Xiaoqi MA. Data Service Providers' Participation in Data Black Market Governance Based on Evolutionary Games[J]. Journal of Library and Information Science in Agriculture, 2024, 36(11): 47-63.

图/表 14

图1

表1

参数介绍"

参数	说明	数值范围
$C f$	数据企业独立管理的成本	$C f > 0$
$C s$	数据服务商参与管理准备成本	$C s > 0$
$C g$	监管方全面监管的成本	$C g > 0$
$C r$	监管方当前监管成本	$C r > 0$
$E i$	数据黑市人员交易净收益	$E i > 0$
$E s$	数据企业与数据服务商合作管理的费用	$E s > 0$
$P f$	监管方对一般泄露事件的数据企业的惩罚	$P f > 0$
$P d$	监管方对重大事件与失职的数据企业的惩罚	$P d > 0$
$P i$	数据黑市人员受到的惩罚	$P i > 0$
$G$	监管方全方位监管的综合收益	$G > 0$
$R f$	数据企业严格管理获市场声誉收益	$R f > 0$
$R s$	数据服务商成功监管的市场声誉收益	$R s > 0$
$Q$	政府对参与治理数据服务商的政策激励	$Q ≥ 0$
$D f$	数据泄露带给数据企业的损失	$D f > 0$
$D g$	数据泄露带给信息安全损失	$D g > 0$
$b$	数据服务商的法律援助能力	$0 < b ≤ 1$
$h$	当前监管能力，全方位监管时h=1	$0 < h ≤ 1$
$r$	数据企业严格管理的治理水平	$0 < r ≤ 1$
$u$	数据服务商的数据黑市监管技术水平	$0 < u ≤ 1$

表1

表2

数据服务商参与黑市治理机制支付矩阵"

项目		数据服务商
项目		参与	参与	不参与	不参与
数据企业	严格管理	$E s - C s + R s u + Q,$	$E s - C s + Q,$	$0,$	$0,$	选择性监管	监管方
		$R f + E i b - E s - D f (1 - u) - P f,$	$R f - E s,$	$R f - P f - D f (1 - r),$	$R f - C f,$
		$- D g (1 - u) - C r,$	$0,$	$- D g (1 - r) - C r,$	$0,$
		$E i - E i b - P i u$	$0$	$E i - P i r$	$0$
	宽松管理	$- C s,$	$- C s,$	$0,$	$0,$	选择性监管
		$- P d - D f,$	$0,$	$- P d - D f,$	$0,$
		$- D g (1 - h) - C r,$	$0,$	$- D g (1 - h) - C r,$	$0,$
		$E i - P i h$	$0$	$E i - P i h$	$0$
	严格管理	$E s - C s + R s u + Q,$	$E s - C s + Q,$	$0,$	$0,$	全方位监管
		$R f + E i b - E s - P f,$	$R f - E s,$	$R f - P f,$	$R f - C f,$
		$G - C g,$	$G - C g,$	$G - C g,$	$G - C g,$
		$E i - E i b - P i$	$0$	$E i - P i$	$0$
	宽松管理	$- C s,$	$- C s,$	$0,$	$0,$	全方位监管
		$- P d - D f,$	$0,$	$- P d - D f,$	$0,$
		$G - C g,$	$G - C g,$	$G - C g,$	$G - C g,$
		$E i - P i$	$0$	$E i - P i$	$0$
		泄露	不泄露	泄露	不泄露
		黑市人员

表2

图2

图 3

图4

图 5

图6

表3

Q、 R f和 R s的最优ESS均衡组合"

$Q$	$R s = 0.5$	$R s = 1$	$R s = 1.5$	$R s = 2$
$R f = 0.5$	1.1(80)	1.1(40)	1(35)	1(30)
$R f = 1$	1.1(89)	1	1	1
$R f = 1.5$	1(110)	1	1	1.9
$R f = 2$	1.1(140)	1.1	1.1	1.1

表3

表4

Q、 R f和 E s的最优ESS均衡组合"

$Q$	$E s = 2.2$	$E s = 2.4$	$E s = 2.6$	$E s = 2.8$	$E s = 3$	$E s = 3.1$
$R s = R f = 0$	0.9(140)	0.7	0.5	0.3	0.1	0
$R s = R f = 0.2$	0.9(125)	0.7(99)	0.5(110)	0.3(100)	0.1(99)	0(98)
$R s = R f = 0.4$	0.9(95)	0.7	0.5	0.3	0.1	0
$R s = R f = 0.6$	0.9(80)	0.7	0.5	0.3	0.1	0
$R s = R f = 0.8$	0.9(75)	0.7	0.5	0.3	0.1	0
$R s = R f = 1$	0.9(55)	0.7	0.5	0.3	0.1	0

表4

图7

表5

黑市人员处罚力度 ( P i )对演化平衡的影响"

$P i$	$P i = 0$	$P i = 0.5$	$P i = 1$	$P i = 1.4$	$P i = 1.5$	$P i = 1.7$	$P i = 2$	$P i = 2.5$
ESS点	[1,1,1,1]	[1,1,1,1]	[1,1,1,1]	[1,1,1,1]	E₁₂(120)	E₁₂(104)	E₁₂(98)	E₁₂(143)

表5

表6

企业一般泄露事件处罚力度 ( P f )对演化平衡的影响"

$P f$	$P f = 0$	$P f = 0.5$	$P f = 1$	$P f = 1.5$	$P f = 2$	$P f = 2.5$	$P f = 3$
ESS点	E₁₂ (100)	E₁₂ (100)	E₁₂ =(90)	E₁₂ (90)	E₁₂ (90)	E₁₂ (85)	E₁₂ (140)

表6

表7

企业重大泄露事件处罚力度 ( P d )对演化平衡的影响"

$P d$	$P d = 0$	$P d = 1$	$P d = 1.5$	$P d = 2$	$P d = 7$	$P d = 8$	$P d = 9$
ESS点	无ESS	无ESS	E₁₂ (110)	E₁₂ (95)	E₁₂ (85)	E₁₂ (95)	E₁₂ (100)

表7

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