关键词: 算法价格歧视, 政府监管, 动态奖惩机制, 演化博弈

Abstract: In the new form of the digital economy, the deep connection between the algorithm technology based on large amounts of data and the application scenarios such as travel, online shopping, and takeout flash delivery has put strong momentum into the high-quality development of the digital economy and become an important starting point for countries to build new competitive advantages. However, due to the deep commercialization and extensive marketization of algorithm technology and application scenarios, the negative sides caused by algorithm technology such as “big data killing”, inducing users to indulge in the network, and excessive consumption in the development of the digital economy have deeply impacted the market competition order and social management order. Among them, the algorithm price discrimination infringes on the interests of a large number of consumers, so the complaints from all walks of life are heard constantly.
The algorithmic price discrimination in this paper refers to the use of digital mining technology by the operators of the index service platform to set higher prices for old customers, and its economic essence is almost “first-level price discrimination” realized on data information analysis technology. How to realize the effective regulation of algorithm price discrimination in the era of digital economy is a theoretical and practical problem that needs to be solved. In order to solve the regulatory dilemma of algorithmic price discrimination on digital service platforms from the perspective of government regulation in the era of digital economy, this paper constructs an evolutionary game model between digital service platforms and consumers under four combination mechanisms of government reward and punishment policies, and analyzes the evolutionary path and influencing factors of digital service platforms and consumers’ strategic choices.
The results show that there will be no evolutionarily stable strategy when the government adopts static reward and punishment mechanism and dynamic reward and punishment mechanism. The adoption of dynamic punishment and static reward and punishment mechanism can effectively make up for the shortcomings of the first two mechanisms and achieve evolutionarily stable state. The dynamic reward and punishment mechanism is better than the dynamic punishment and static reward mechanism in the regulation of digital service platform algorithm price discrimination. In the final stable strategy combination of dynamic punishment and static reward and dynamic reward and punishment mechanism, the probability of digital service platform choosing fair pricing is positively correlated with digital mining technology and predicting consumers’ retention payment intention, while the probability of consumers choosing purchase is positively correlated with incentive coefficient and fine amount, and negatively correlated with digital mining technology and predicting consumers’ retention payment intention. Although this study provides new perspectives and policy recommendations for the regulation of digital platforms at both theoretical and practical levels, there are some limitations. First of all, the establishment of the game model is based on specific assumptions and simplified reality. Future studies can improve the applicability of the model by introducing more realistic factors. Secondly, the research mainly focuses on the static and dynamic reward and punishment policies of the government, without considering the influence of other potential market players such as regulators and third-party organizations. Future research can explore the evolutionary game model under a more complex market environment, considering multi-participant and multi-level strategy interaction.

Key words: algorithmic price discrimination, government supervision, dynamic reward and punishment mechanism, evolutionary game