As the sociological theory of homophily suggests, people tend to interact with those of similar preferences. Motivated by this well-established phenomenon, today's online sellers, such as Amazon, seek to learn a new buyer's private preference from his friends’ purchase records. Although such learning allows the seller to enable personalized pricing and boost revenue, buyers are also increasingly aware of these practices and may alter their social behaviors accordingly. This paper presents the first study regarding how buyers strategically manipulate their social interaction signals considering their preference correlations, and how a seller can take buyers’ strategic social behaviors into consideration when designing the pricing scheme. Starting with the basic two-buyer network, we propose and analyze a parsimonious model that uniquely captures the double-layered information asymmetry between the seller and buyers, integrating both individual buyer information and inter-buyer correlation information. Our analysis reveals that only high-preference buyers tend to manipulate their social interactions to evade the seller's personalized pricing, but surprisingly, their payoffs may actually worsen as a result. Additionally, we demonstrate that the seller can considerably benefit from the learning practice, regardless of whether the buyers are aware of this fact or not. Indeed, our analysis reveals that buyers’ learning-aware strategic manipulation has only a slight impact on the seller's revenue. In light of the tightening regulatory policies concerning data access, it is advisable for sellers to maintain transparency with buyers regarding their access to buyers’ social interaction data for learning purposes. This finding aligns well with current informed-consent industry practices for data sharing. Finally, we explore the seller's dynamic learning process across multiple interconnected buyers, and show that learning previous buyers’ preferences may not necessarily help infer other buyers’ preferences in the seller's subsequent learning phase.

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. Finally, we examine prevalent pricing practices where the seller breaks a pricing promise to personalize final offerings, and show that it only slightly improves the seller’s average revenue while introducing higher variance.

As the sociological theory of homophily suggests, people tend to interact with those of similar preferences. This motivates product sellers to learn buyers’ product preferences from the buyers’ friends’ purchase records. Although such learning allows sellers to enable personalized pricing to improve profits, buyers are also increasingly aware of such practices and may alter their behaviors accordingly. This paper presents the first study regarding how buyers may strategically manipulate their social interaction signals considering their preference correlations, and how an informed seller can take buyers’ strategic social behaviors into consideration when designing the pricing schemes. Our analytical results show that only high-preference buyers tend to manipulate their social interactions to hurdle the seller’s personalized pricing. Surprisingly, these high-preference buyers’ payoff may become worse after their strategic manipulation. Furthermore, we show that the seller can greatly benefit from the learning practice, no matter whether the buyers are aware of such learning or not. In fact, buyers’ learning-aware strategic manipulation only slightly reduces the seller’s revenue. Considering the increasingly stricter policies on data access by authorities, it is thus advisable for sellers to make buyers aware of their access and learning based on social interaction data. This justifies well with current regulatory policies and industry practices regarding informed consent for data sharing.

User profiling allows product sellers to identify users’ willingness to pay and enable personalized pricing. However, users’ information exploited in profiling is usually private and hard to obtain accurately due to users’ privacy concerns. With the increasing popularity of social networks, where users reveal their private information through social interactions, more sellers today profile users through their social data. This paper is the first to study how a seller optimizes personalized pricing through user profiling on social networks, where users proactively react by controlling their social activities and information leakage. We formulate and analyze a dynamic Bayesian game played between users and the seller. First, users decide their social activities by trading off the social network benefit against the potential risk of revealing private information. Then, the seller exploits users’ profiles to determine the personalized prices for the profiled users and a uniform price for the non-profiled users. It is challenging to analyze the Perfect Bayesian Equilibrium (PBE) of this game due to i) the randomness in user profiling, and ii) the coupling among users’ activity levels and that between the seller’s pricing decisions and users’ social activities. Despite the difficulty, we propose to alternate backward induction and forward induction to successfully solve the PBE. We show the surprising result that users’ activity levels do not monotonically decrease as the profiling technology improves. Instead, when user profiling is of high accuracy, the seller strategically chooses a high uniform price to stimulate their increased social activities to profile more users.