Optimal Nonlinear Pricing in Social Networks Under Asymmetric Network Information

• 成果类型: Article
• 署名作者: Zhang, Yang; Chen, Ying-Ju
• 署名单位: National University of Singapore; Hong Kong University of Science & Technology; Hong Kong University of Science & Technology
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2019.1915
• 发表日期: 2020
• 页码: 818-833
• 关键词: Network formation; local network effects; game theory; information asymmetry; nonlinear pricing

摘要

We study the optimal nonlinear pricing of products and services in social networks, in which customers are strategic and their consumption exhibits local externality. Customers know about their local network characteristics (which are positively affiliated across neighbors), but the selling firm only has knowledge of the global network. We develop a solution approach based on calculus of variations and positive neighbor affiliation to tackle this nonstandard principal-agent problem faced by the firm. We show that the optimal pricing compromises the capitalization of the susceptibility to neighbor consumption with the motivation of one's own consumption, which gives rise to a menu of quantity premium or quantity discount. In the Erdos and Renyi graph (a special case of the social network model we use), we find that the pricing scheme does not screen network positions; consequently, the firm can offer a simple uniform price. We conduct robustness checks of our results with two-way connections, in which the firm-optimal consumption becomes linear in customer degree in the scale-free network. Compared with linear pricing, we show that nonlinear pricing allows the firm to respond more effectively to the changes of network topology and economic factors.