Experimenting in Equilibrium
针对存在跨单元干扰的大规模随机系统，提出一种基于平均场建模的实验设计方法，在保持系统均衡的同时估计参数变化的影响，并用于优化平台供给侧支付。
Classical approaches to experimental design assume that intervening on one unit does not affect other units. There are many important settings, however, where this noninterference assumption does not hold, as when running experiments on supply-side incentives on a ride-sharing platform or subsidies in an energy marketplace. In this paper, we introduce a new approach to experimental design in large-scale stochastic systems with considerable cross-unit interference, under an assumption that the interference is structured enough that it can be captured via mean-field modeling. Our approach enables us to accurately estimate the effect of small changes to system parameters by combining unobtrusive randomization with lightweight modeling, all while remaining in equilibrium. We can then use these estimates to optimize the system by gradient descent. Concretely, we focus on the problem of a platform that seeks to optimize supply-side payments [Formula: see text] in a centralized marketplace where different suppliers interact via their effects on the overall supply-demand equilibrium, and we show that our approach enables the platform to optimize [Formula: see text] in large systems using vanishingly small perturbations. This paper was accepted by Hamid Nazerzadeh, big data analytics.