Stochastic Approximation of Symmetric Nash Equilibria in Queueing Games

• 成果类型: Article
• 署名作者: Ravner, Liron; Snitkovsky, Ran I.
• 署名单位: University of Haifa; Tel Aviv University
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2021.0306
• 发表日期: 2024
• 页码: 2698-2725
• 关键词: s-modular games; social optimization; gi/g/1 queue; strategies; BEHAVIOR; CONVERGENCE; simulation; customers; systems; size

摘要

We suggest a novel stochastic-approximation algorithm to compute a symmetric Nash-equilibrium strategy in a general queueing game with a finite action space. The algorithm involves a single simulation of the queueing process with dynamic updating of the strategy at regeneration times. Under mild assumptions on the utility function and on the regenerative structure of the queueing process, the algorithm converges to a symmetric equilibrium strategy almost surely. This yields a powerful tool that can be used to approximate equilibrium strategies in a broad range of strategic queueing models in which direct analysis is impracticable.