Stochastic Online Fisher Markets: Static Pricing Limits and Adaptive Enhancements
研究了用户按顺序到达的在线费雪市场，证明了静态定价的局限性，并设计了仅依赖用户消费观察（显示偏好反馈）的动态定价算法，在公平性对数目标下取得了已知最优的遗憾保证。
Dynamic Pricing in Fisher Markets Using Revealed Preferences In many markets, agents arrive online where complete information on user attributes is unavailable because of uncertainty or privacy issues. However, deriving equilibrium prices in Fisher markets, a canonical resource allocation framework, relies on complete knowledge of user attributes and requires a static market where all users are present simultaneously. In “Stochastic Online Fisher Markets: Static Pricing Limits and Adaptive Enhancements,” D. Jalota and Y. Ye address these limitations of classical Fisher markets by studying their online variant where users with privately known utility and budget parameters, drawn i.i.d. from a distribution, arrive sequentially. In this novel market, the authors establish the limitations of static pricing and design dynamic posted-price algorithms with improved guarantees. Their main result is a posted-price algorithm that solely relies on revealed preference (RP) feedback, that is, observations of user consumption, achieving the best-known guarantees for first-order algorithms in the RP setting while providing a regret analysis of a fairness-promoting logarithmic objective, unlike typical nonnegative and bounded efficiency-promoting objectives in online learning.