图书情报工作

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2025 , Vol. 69 >Issue 19: 13 - 25

DOI: https://doi.org/10.13266/j.issn.0252-3116.2025.19.002

研究论文

动态奖惩机制下数据质量协同监管多方博弈分析

  • 张金灿 ,
  • 张雪莹 ,
  • 郭可莹
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  • 郑州大学管理学院 郑州 450001
张金灿，副教授，博士，硕士生导师；张雪莹，硕士研究生；郭可莹，硕士研究生，通信作者，E-mail：1546643013@qq.com。

收稿日期: 2024-09-04

  修回日期: 2024-12-20

  网络出版日期: 2025-09-12

基金资助

本文系国家社会科学基金重点项目“众包平台的共生危机纾解与反垄断规制研究”（项目编号：23AGL041）研究成果之一。

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Multi-Party Game Analysis of Data Quality Collaborative Supervision Under Dynamic Reward and Punishment Mechanism

  • Zhang Jincan ,
  • Zhang Xueying ,
  • Guo Keying
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  • School of Management, Zhengzhou University, Zhengzhou 450001
Zhang Jincan, Associate Professor, PhD, Master’s Supervisor; Zhang Xueying, Master’s Candidate; Guo Keying, Master’s Candidate, Corresponding Author, E-mail: 1546643013@qq.com.

Received date: 2024-09-04

  Revised date: 2024-12-20

  Online published: 2025-09-12

Supported by

This work is supported by the key project of National Social Science Fund of China, titled “Research on the Symbiotic Crisis Relief and Antitrust Regulation of Crowdsourcing Platforms” (Grant No. 23AGL041).

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摘要

[目的/意义] 针对数据交易市场中的数据质量监管问题，构建多方案下的演化博弈模型，探寻能够有效纾解数据质量问题的最优监管机制，提高市场参与主体维护数据交易市场秩序的自觉性，规范市场交易环境。[方法/过程] 以数据供应方作为主要利益主体，引入处罚强度、连带责任等参数，构建政府与数据交易平台协同监管下数据供应方提供数据质量行为策略选择的静态演化博弈模型，并依次引入动态惩罚变量和非线性动态补贴变量，借助系统动力学对静态惩罚、动态惩罚与动态惩罚—补贴3种方案下的行为策略及稳定性进行仿真分析，并基于李雅普诺夫稳定性理论对稳定状态做双重验证。[结果/结论] 研究显示：静态惩罚与动态惩罚方案下3个博弈主体的行为策略均未达到最优稳定状态，但动态惩罚方案一定程度上能抑制博弈主体行为波动。动态惩罚—补贴方案能有效抑制博弈主体行为波动且监管效果达到最优，使得数据供给方提供高质量数据成为最优选择。

关键词： 数据交易; 数据质量; 动态奖惩; 演化博弈; 系统动力学

本文引用格式

张金灿 , 张雪莹 , 郭可莹 . 动态奖惩机制下数据质量协同监管多方博弈分析[J]. 图书情报工作, 2025 , 69(19) : 13 -25 . DOI: 10.13266/j.issn.0252-3116.2025.19.002

Abstract

[Purpose/Significance] To address the issue of data quality supervision in the data trading market, this paper constructs evolutionary game models under multiple solutions. It aims to explore the optimal supervision mechanism that effectively mitigates data quality problems, enhance the awareness of market participants in maintaining order, and regulate the data trading market environment. [Method/Process] With data providers as the main stakeholders, it introduced parameters such as penalty intensity and joint-several liability to construct a static evolutionary game model for the behavior strategy selection of the data provider, under the cooperative supervision of government departments and data trading platforms. Additionally, it incorporated dynamic penalty variables and nonlinear dynamic subsidy variables to optimize the model. Using system dynamics, it compared behavioral strategies and stability under three schemes: static punishment, dynamic punishment, and dynamic punishment-subsidy. Finally, the stable state was double-verified using Lyapunov stability theory. [Result/Conclusion] The research results indicate that the behavioral strategies of the three game players do not achieve the optimal stable state under either the static or dynamic penalty scheme. However, the dynamic penalty scheme can partially suppress behavioral fluctuations among the players. In contrast, the dynamic penalty-subsidy scheme effectively minimizes these fluctuations and achieves the optimal regulatory effect, making it the best choice for ensuring data providers supply high-quality data.

Key words： data trading; data quality; dynamic rewards and penalties; evolutionary game; system dynamics

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