Robustness of online proportional response in stochastic online fisher markets: A decentralized approach
研究周期性费雪市场中，买家仅根据物品随机波动价值更新出价，无需披露隐私信息，通过在线比例响应算法实现去中心化决策，并分析其公平性遗憾和个体买家遗憾的上界。
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. We introduce a distributed bidding 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 nonstationary stochastic input models to demonstrate the algorithm’s efficiency across diverse scenarios. 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 algorithm is universally applicable to many worlds and shows the robustness of performance guarantees.