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

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.