Fair Resource Allocation in a Volatile Marketplace
针对供应不确定的平台环境，设计了一种基于Fisher市场均衡的公平分配方案，在理论上保证常数因子近似最优，并在真实广告数据上验证了效果。
In settings where a platform must allocate finite supplies of goods to buyers, balancing overall platform revenues with the fairness of the individual allocations to platform participants is paramount to the well-functioning of the platform. This is made even more difficult by the fact that the supply of goods is in practice stochastic and difficult to forecast, such as in the case of online ad allocation, where the platform manages a supply of impressions that varies over time. In this paper, we design a fair allocation scheme that works in the presence of supply uncertainty. Algorithmically, the scheme repeatedly solves for Fisher market equilibria in a model predictive control fashion and is proved to admit constant factor guarantees versus the offline optimal. In addition, the scheme is tested on a sequence of real ad datasets, showing strong empirical performance.