Personalized Pricing Through Strategic User Profiling in Social Networks

2025-08-23

AI Summary

This paper investigates a dynamic Bayesian game between buyers and sellers in social networks under privacy-enhancing behaviors: as users evade browsing tracking, sellers shift to inferring preferences from social discussions for personalized pricing, while users trade off privacy disclosure against price impacts. We formulate the first dynamic Bayesian game model integrating backward induction and forward reasoning, solving for the unique perfect Bayesian equilibrium under information revelation and belief consistency constraints—yielding closed-form solutions. Key counterintuitive findings include: (i) improved profiling accuracy paradoxically raises uniform pricing to incentivize greater social exposure; and (ii) informed consent policies may reduce welfare for the majority of users. The results uncover profound implications of social profiling on pricing mechanisms and user welfare, providing theoretical foundations for platform regulation and privacy policy design.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Machine Learning: Privacy

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Tracking, profiling, and countermeasures against them

Abstract

Traditional user profiling techniques rely on browsing history or purchase records to identify users' willingness to pay. This enables sellers to offer personalized prices to profiled users while charging only a uniform price to non-profiled users. However, the emergence of privacy-enhancing technologies has caused users to actively avoid on-site data tracking. Today, major online sellers have turned to public platforms such as online social networks to better track users' profiles from their product-related discussions. This paper presents the first analytical study on how users should best manage their social activities against potential personalized pricing, and how a seller should strategically adjust her pricing scheme to facilitate user profiling in social networks. We formulate a dynamic Bayesian game played between the seller and users under asymmetric information. The key challenge of analyzing this game comes from the double couplings between the seller and the users as well as among the users. Furthermore, the equilibrium analysis needs to ensure consistency between users' revealed information and the seller's belief under random user profiling. We address these challenges by alternately applying backward and forward induction, and successfully characterize the unique perfect Bayesian equilibrium (PBE) in closed form. Our analysis reveals that as the accuracy of profiling technology improves, the seller tends to raise the equilibrium uniform price to motivate users' increased social activities and facilitate user profiling. However, this results in most users being worse off after the informed consent policy is imposed to ensure users' awareness of data access and profiling practices by potential sellers. This finding suggests that recent regulatory evolution towards enhancing users' privacy awareness may have unintended consequences of reducing users' payoffs.

Problem

• Studying strategic user social activity management against personalized pricing
• Analyzing seller pricing scheme adjustments for user profiling
• Examining equilibrium impacts of profiling accuracy on user payoffs

Innovation

• Dynamic Bayesian game for strategic pricing
• Closed-form perfect Bayesian equilibrium analysis
• Social network profiling for personalized pricing