Learning From Social Interactions: Personalized Pricing and Buyer Manipulation
2026-03-27 • Computer Science and Game Theory
Computer Science and Game TheorySocial and Information Networks
AI summaryⓘ
The authors study how online sellers use buyers' friends' purchase data to guess what new buyers like and set personalized prices. They explore how buyers might try to hide their true preferences by changing who they interact with online. Their findings show that only buyers who really like expensive stuff try to hide, but this often backfires and makes them worse off. Meanwhile, sellers still gain from this learning, even if buyers try to trick the system. The authors suggest sellers should be transparent about using social data, which fits with current privacy rules.
homophilypersonalized pricingsocial interaction signalsinformation asymmetrystrategic behaviorprice discriminationdata privacyinformed consentbuyer-seller modelregulatory policies
Authors
Qinqi Lin, Lingjie Duan, Jianwei Huang
Abstract
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 fundamental 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. Moreover, 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.