Robustness of Online Proportional Response in Stochastic Online Fisher Markets: a Decentralized Approach

作     者：Yang, Yongge Lee, Yu-Ching Chen, Po-An Lin, Chuang-Chieh 

作者机构：Economics and Management School Wuhan University Hubei Wuhan China Department of Industrial Engineering National Tsing Hua University 101 Sec 2 Kuang-Fu Rd Hsinchu City300 Taiwan Institute of Information Management National Yang Ming Chiao Tung University 1001 University Rd Hsinchu City300 Taiwan Department of Computer Science and Information Engineering Tamkang University 151 Yingzhuan Rd New Taipei City251301 Taiwan 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Stochastic systems 

摘      要：Problem Definition: This study is focused on periodic Fisher markets where items with time-dependent and stochastic values are regularly replenished and buyers aim to maximize their utilities by spending budgets on these items. Traditional approaches of finding a market equilibrium in the single-period Fisher market rely on complete information about buyers’ utility functions and budgets. However, it is impractical to consistently enforce buyers to disclose this private information in a periodic setting. Methodology/results: We introduce a distributed auction algorithm, online proportional response, wherein buyers update bids solely based on the randomly fluctuating values of items in each period. The market then allocates items based on the bids provided by the buyers. We show connections between the online proportional response and the online mirror descent algorithm. Utilizing the known Shmyrev convex program, a variant of the Eisenberg-Gale convex program that establishes market equilibrium of a Fisher market, two performance metrics are proposed: the fairness regret is the cumulative difference in the objective value of a stochastic Shmyrev convex program between an online algorithm and an offline optimum, and the individual buyer’s regret gauges the deviation in terms of utility for each buyer between the online algorithm and the offline optimum. Our algorithm attains a problem-dependent upper bound in fairness regret under stationary inputs. This bound is contingent on the number of items and buyers. Additionally, we conduct analysis of regret under various non-stationary stochastic input models to demonstrate the algorithm’s efficiency across diverse scenarios. Managerial implications: The online proportional response algorithm addresses privacy concerns by allowing buyers to update bids without revealing sensitive information and ensures decentralized decision-making, fostering autonomy and potential improvements in buyer satisfaction. Furthermore, our algo