  Classical approaches to experimental design assume that intervening on one
unit does not affect other units. There are many important settings, however,
where this non-interference 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 unobstrusive 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 p in a centralized
marketplace where different suppliers interact via their effects on the overall
supply-demand equilibrium, and show that our approach enables the platform to
optimize p in large systems using vanishingly small perturbations.
