Information Advantage, Differentiated Pricing and Consumer Surplus: Algorithm Driven Dynamic Pricing of Online Ride Hailing Platforms

Abstract

With the development of digital platforms, algorithmic dynamic pricing has been gradually applied to ride‑hailing and other markets. Taking ride‑hailing platforms as the research object, this paper analyzes how platforms leverage consumer demand differences and information advantages to implement dynamic pricing based on third‑degree price discrimination, two‑sided market theory and game theory, and discusses its impacts on market equilibrium and consumer surplus. The differences in consumers' willingness to pay and price elasticity provide the basis for the platform to implement differentiated pricing, and the information advantage of the platform further enhances its price adjustment ability. In the strategic interaction, dynamic pricing can not only adjust the relationship between supply and demand, but also transfer some consumer surplus to the platform. There are differences in the impact on high- and low-elasticity consumers. Therefore, this paper believes that dynamic pricing itself has a certain role in resource allocation, but when the advantages of algorithmic information are used for excessive differentiated pricing, it may damage consumer welfare. In this regard, regulatory measures including price constraints and algorithm information disclosure can curb the excessive expansion of platform pricing power while preserving the efficiency of dynamic pricing.