Information Asymmetry in Queues with Strategic Customers
Shunan Zheng, John Hasenbein
TL;DR
This work analyzes information asymmetry in an unobservable M/G/1 queue where managers know the true arrival rate $\lambda$ and customers hold heterogeneous beliefs about $\lambda$ via the random variable $\Lambda$. It introduces a three-tier hierarchy of information disclosure—classical (full knowledge), shared belief, and private belief—and derives how these levels affect equilibrium joining probabilities, revenue, and social welfare, under general belief distributions with minimal restrictions. The authors establish structural results and threshold functions (notably $M(\xi)$) that govern revenue comparisons across information levels and provide managerial insights on when information revelation improves revenue or welfare. Numerical illustrations validate the theory, showing how belief mean and dispersion interact with congestion to shape decisions on pricing and disclosure. The model offers a flexible framework for regulator-like decisions on information sharing in queueing systems, with practical implications for revenue management and social welfare optimization.
Abstract
This paper studies information asymmetry in an unobservable single-server queueing system. While system managers have knowledge of the true arrival rate, customers may lack this information and instead form arbitrary beliefs. We propose a three-tier hierarchy of information asymmetry with increasing levels of information disclosure:customers keep private beliefs, customers are aware of the beliefs of others, and customers know the true arrival rate. Within this framework, the effects of the belief distribution, which is assumed to be general with minimal restrictions, are analyzed in terms of equilibrium joining probabilities, revenue, and social welfare. Furthermore,strategies for information disclosure are proposed for system managers to regulate the queue.