Abstract: With the evolution of information technology and the expansion of data capital, novel forms of unfair competition behaviors, such as the utilization of “big data to stifle maturity” and the imposition of a “pick one or the other” dilemma on users, continue to emerge. Therefore, it becomes paramount to enhance or innovate the supervision and the methods employed by government regulators concerning the development and utilization of enterprise data resources. This imperative action is aimed at safeguarding user rights and interests, fostering the industry’s robust growth, and enhancing overall social welfare. Elevating or innovating the level or mode of government regulators’ regulation concerning the development and utilization of enterprise data resources is significant in protecting user rights, promoting the industry’s healthy development, and improving social welfare. The concept of data resource development refers to the utilization of historical data by enterprises to analyze, summarize, and draw conclusions about existing purchasing population profiles. This process vividly portrays the distribution of user populations for products, thereby validating product positioning’s appropriateness and enabling timely adjustments. These insights lay the groundwork for the design of effective marketing strategies and product solutions. While the development and utilization of data resources by platform enterprises yield economic utility, they also bring associated risks. Within the market environment of information technology, big data, and industrial integration, enterprises leverage data resource development and utilization to achieve cross-domain competitive advantages. However, this practice not only significantly encroaches upon user rights and interests but also disrupts market equilibrium and, in certain cases, reduces social benefits. Hence, the primary focus of research is directed toward enhancing government supervision levels on the development and utilization of enterprise data resources while concurrently innovating the regulatory approach.
This research objective aims to advance industry health, protect user rights, and elevate overall social welfare. Given these considerations, the present paper undertakes a theoretical analysis of how the development and utilization of data resources impact social welfare through the number of platform users. It constructs a Lotka-Volterra evolutionary model that encompasses government regulation and data resource development and utilization. By merging the concepts of evolutionary economics with dynamic methodologies, it evaluates the effectiveness of governmental regulation, the stability of regulatory outcomes, and the market volatility inherent in the evolutionary process. Through theoretical derivation and simulation research, the study discovers the following insights:
(1)A slower pace of data resource development and utilization corresponds to heightened sensitivity to government regulation. A higher preset level of regulatory oversight by government regulators renders it more challenging for enterprises to undertake data resource development and utilization.
(2)As long as enterprises engage in the development and utilization of data resources, government regulators cannot automatically eliminate the risks arising from such development occurring without any supervision.
(3)When challenges arise in regulating data resource development and utilization by enterprises, or when regulatory authorities exhibit limited tolerance for regulatory costs, or when the sensitivity of regulatory costs to the development and utilization of data resources is inadequate, or when government regulatory authorities are insufficiently sensitive to the formation of industry monopolies by enterprises, the low-cost regulatory approach alone is insufficient to curb the development and utilization of data resources by enterprises.
(4)The level of development and utilization of data resources plays a role in initially promoting and then inhibiting the enhancement of social welfare levels. Government regulation exerts a facilitating effect on the improvement of social welfare levels. Addressing this issue requires two-pronged action: on one hand, they should implement robust regulatory procedures to strictly curb data misuse and unreasonable monopoly behavior by platform enterprises. During the initial stages of regulation, regulatory authorities need to enhance the visibility of maximum regulatory penalties for excessive data resource development and utilization within regulatory laws and regulations. This approach establishes a strong deterrent, thereby reducing the likelihood of platform enterprises violating regulations and fostering positive social effects through the development and utilization of data resources. On the other hand, government regulators should conduct random inspections on the development and utilization of data resources by enterprises in key areas with a certain probability. In cases where excessive development and utilization of data resources by platform enterprises are identified, they should be subjected to legal punishment and required to rectify their practices to prevent harm to social welfare.

Key words: development and utilization of data resources, government regulation, Lotka-Voltorra model, nonlinear dynamics, evolutionary analysis

摘要： 随着“大数据杀熟”、强制用户“二选一”等新型不正当竞争行为不断涌现,提高或创新政府的监管水平或监管方式,对于促进行业健康发展、提高社会福利具有重要意义。本文理论分析了数据资源开发利用通过平台用户数量来影响社会福利水平,构建了政府监管与数据资源开发与利用的Lotka-Volterra演化模型,解析了政府监管的有效性、监管结果的稳定性以及演化过程的市场波动情况。研究表明:只要企业存在数据资源的开发与利用,政府监管部门无法在零监管情况下使得企业数据资源开发利用引发的风险自动消除。当企业数据资源开发与利用监管难度较高或监管部门对监管成本的接受度、监管成本对数据资源开发利用的敏感度、政府监管部门对企业形成行业垄断的敏感度不足时,低成本的监管水平不足以抑制企业进行数据资源的开发与利用。数据资源的开发利用水平对社会福利水平的提升起到了先促进后抑制的作用,政府监管有利于提高社会福利水平。

关键词: 数据资源开发利用, 政府监管, Lotka-Volterra模型, 非线性动力学, 演化分析