In a Fisher market, agents (users) spend a budget of (artificial) currency to buy goods that maximize their utilities while a central planner sets prices on capacity-constrained goods such that the market clears. However, the efficacy of pricing schemes in achieving an equilibrium outcome in Fisher markets typically relies on complete knowledge of users' budgets and utilities and requires that transactions happen in a static market wherein all users are present simultaneously. As a result, we study an online variant of Fisher markets, wherein budget-constrained users with privately known utility and budget parameters, drawn i.i.d. from a distribution $\mathcal{D}$, enter the market sequentially. In this setting, we develop an algorithm that adjusts prices solely based on observations of user consumption, i.e., revealed preference feedback, and achieves a regret and capacity violation of $O(\sqrt{n})$, where $n$ is the number of users and the good capacities scale as $O(n)$. Here, our regret measure is the optimality gap in the objective of the Eisenberg-Gale program between an online algorithm and an offline oracle with complete information on users' budgets and utilities. To establish the efficacy of our approach, we show that any uniform (static) pricing algorithm, including one that sets expected equilibrium prices with complete knowledge of the distribution $\mathcal{D}$, cannot achieve both a regret and constraint violation of less than $\Omega(\sqrt{n})$. While our revealed preference algorithm requires no knowledge of the distribution $\mathcal{D}$, we show that if $\mathcal{D}$ is known, then an adaptive variant of expected equilibrium pricing achieves $O(\log(n))$ regret and constant capacity violation for discrete distributions. Finally, we present numerical experiments to demonstrate the performance of our revealed preference algorithm relative to several benchmarks.
翻译:在渔业市场中,代理商(用户)花费了一种(人工)货币预算{(人工)货币{(比例)货币{(比例)货币来购买能最大限度地提高公用事业的商品,而中央计划商则对受能力限制的商品设定价格,使市场变得清晰。然而,定价计划在Fish市场取得平衡结果的功效通常取决于对用户预算和公用事业的全面了解,并要求交易发生在一个所有用户同时在场的静态市场中。结果,我们研究了Fisher市场的一种在线变价(美元是用户数量和良好能力规模,以私人已知的公用事业和预算参数为单位,例如,从一个分配单位的美元=(市值{D})基准中抽取,按顺序进入市场。在此环境下,我们开发一种算法,仅根据用户消费的观察,即披露了优惠反馈反馈,并实现了对美元(sqrqral)的绩效的违反率。 美元是用户数量和能力的完整数据比例,而我们当时需要的是(n)市值-Gale的汇率和市值(我们所知道的市值的汇率和市值)的汇率和市值(我们所显示的汇率的正确的汇率的汇率和市值)的汇率,显示,我们最后的汇率和市值的汇率的汇率和市值能实现一个完整的分析。