Modeling and optimization of data trading supply chain regulatory strategies based on system dynamics

Jian Yang; Wenyu Jia; Qianfei Guo; Pengfei Pu; Jie Zhang; Jian Yang; Wenyu Jia; Qianfei Guo; Pengfei Pu; Jie Zhang

doi:10.3934/jimo.2026038

Journal of Industrial and Management Optimization

2026, Volume 22, Issue 2: 1034-1062. doi: 10.3934/jimo.2026038

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Research article

Modeling and optimization of data trading supply chain regulatory strategies based on system dynamics

1.
School of Information, Shanxi University of Finance and Economics, Taiyuan 030006, Shanxi, China
2.
Graduate School, Sehan University, Yeongam-gun 58447, Jeollanam-do, Republic of Korea
3.
School of Management, Tianjin Normal University, Tianjin, 300387, China
4.
Technology Innovation Center, Shanxi Information Industry Technology Research Institute Co., Ltd., Taiyuan 030012, Shanxi, China

Received: 27 October 2025 Revised: 13 January 2026 Accepted: 16 January 2026 Published: 23 January 2026
Primary: 91A80

With the rapid development of the data market and the emergence of new business models, data trading has become a paradigm for allocating production elements. However, most existing studies focused on traditional production elements or single-sided regulation, which did not consider the unique risks of data trading, such as usage tracking difficulties and potential data leakage, and rarely applied dynamic system theory to the analysis of multi-party quality regulation in the data market. In this paper, we first proposed a four-party evolutionary game model of data providers, data trading platforms, data demanders, and regulatory departments. Second, we focused on the evolutionary stability strategies and evolutionary trends of different participants, as well as the influential factors, such as regulatory strategies, regulatory costs, punishment intensity, and the prudent analysis strategy of the data demand side, to analyze the influence of different behavioral strategies on the regulation of data product quality. Third, we verified the stability of the equilibrium point of the four-party game system through system dynamics simulation experiments. Finally, we summarized our work and provided related recommendations for governing agencies and market stakeholders to facilitate the healthy development of the data market and maximize social welfare.
- data quality,
- data trading,
- evolutionary game,
- system dynamics
Citation: Jian Yang, Wenyu Jia, Qianfei Guo, Pengfei Pu, Jie Zhang. Modeling and optimization of data trading supply chain regulatory strategies based on system dynamics[J]. Journal of Industrial and Management Optimization, 2026, 22(2): 1034-1062. doi: 10.3934/jimo.2026038

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Abstract

With the rapid development of the data market and the emergence of new business models, data trading has become a paradigm for allocating production elements. However, most existing studies focused on traditional production elements or single-sided regulation, which did not consider the unique risks of data trading, such as usage tracking difficulties and potential data leakage, and rarely applied dynamic system theory to the analysis of multi-party quality regulation in the data market. In this paper, we first proposed a four-party evolutionary game model of data providers, data trading platforms, data demanders, and regulatory departments. Second, we focused on the evolutionary stability strategies and evolutionary trends of different participants, as well as the influential factors, such as regulatory strategies, regulatory costs, punishment intensity, and the prudent analysis strategy of the data demand side, to analyze the influence of different behavioral strategies on the regulation of data product quality. Third, we verified the stability of the equilibrium point of the four-party game system through system dynamics simulation experiments. Finally, we summarized our work and provided related recommendations for governing agencies and market stakeholders to facilitate the healthy development of the data market and maximize social welfare.

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