Dynamic Pricing for Two-Sided Marketplaces with Offer Expiration

• 成果类型: Article; Early Access
• 署名作者: Cao, Yufeng; Kleywegt, Anton; Wang, He
• 署名单位: Shanghai Jiao Tong University; University System of Georgia; Georgia Institute of Technology
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2022.00832
• 发表日期: 2025
• 关键词: Discrete choice model; Dynamic pricing; Asymptotic Optimality; two-sided marketplace

摘要

We consider a two-sided marketplace in which a market operator sells services to clients and buys services from vendors. The market operator determines the prices dynamically for both clients and vendors. Services are transacted in discrete units called jobs, and the jobs are characterized by their types, service deadlines, client prices, and vendor prices. Jobs are submitted by clients to the marketplace, and the market operator then lists the available jobs. Vendors can view the available jobs and choose them based on their preferences. We consider an infinite-horizon long-run average reward Markov decision process (MDP) model of the market operator's dynamic pricing problem. The MDP consists of the arrival processes on both sides of the marketplace as well as the choice behavior of both clients and vendors. Because solving this MDP directly is impractical for large market sizes, we study a discrete-time fluid approximation of the problem. This approximation results in a simple pricing policy in which each job's price trajectory depends on the time remaining until the service deadline but does not depend on the other jobs available in the marketplace. We show that this policy is asymptotically optimal with a loss ratio of order O(1=61) on the long-run average reward, where 61 represents the scale of demand and supply. The performance of the fixed price trajectory policy is compared with other heuristics, including a constant pricing policy and a state-dependent pricing policy. We also extend the model to continuous-time and finite-horizon settings.