Algorithmic Pricing and Platform Competition: The Substitutability of Discrimination Intensity under Behavior-Based Price Discrimination

Authors

• Mujiang Deng, School of Economics and Management, Beihang University, ChinaFollow
• Jun Wang, School of Economics and Management, Beihang University, ChinaFollow
• Jie Luo, School of Economics and Management, Beihang University, ChinaFollow

Abstract

Algorithmic pricing allows platforms to use users’ past affiliations for behavior-based price discrimination (BBPD). This paper studies when BBPD becomes strategically relevant and how it shapes dynamic platform competition. We develop a dynamic duopoly model with switching costs and cross-side network externalities. The theoretical comparison identifies a parameter region in which BBPD is privately attractive but mutual BBPD may reduce profits relative to uniform pricing. Using parameter values from this region, we employ MATD3 simulations to examine longer-horizon pricing dynamics. Learned policies show that old–new price gaps are negatively associated across platforms, suggesting that the two platforms’ discrimination intensities act as substitutes under algorithmic BBPD.

Recommended Citation

Deng, Mujiang; Wang, Jun; and Luo, Jie, "Algorithmic Pricing and Platform Competition: The Substitutability of Discrimination Intensity under Behavior-Based Price Discrimination" (2026). ICEB 2026 Proceedings (Beijing, China). 10.
https://aisel.aisnet.org/iceb2026/10