Naive Herding in Location-Based Networks: Induced Social Learning and Optimal Dynamic Pricing

Abstract

This paper studies social learning and optimal pricing in the presence of location-based social networks, such as Foursquare. We provide an analytic model to resolve the following questions: (1) What is the optimal pricing strategy in location-based networks? (2) How do different pricing strategies affect social welfare and the privacy concern of consumers? In the model, we relax the perfect rationality assumption and assume that customers who are embedded in location-based networks can make only naive inferences because of lacking the knowledge about the network structure. Our model shows that the seller could potentially control the information available to future customers and induce social learning by using different pricing strategies. Our results have clear managerial implications. Offering introductory discounts is not always an effective method to boost purchases. It could prevent the social learning that increases future customers' willingness to pay when customers adopt the naive inference rule.

Keywords: Location-Based Networks, Social Learning, Herding, Privacy, Dynamic Pricing

JEL Classification: D83, D85, C72