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Experimenting in Equilibrium

Stefan Wager and Kuang Xu

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Abstract: Classical approaches to experimental design assume that intervening on one unit does not affect other units. Recently, however, there has been considerable interest in settings where this non-interference assumption does not hold, e.g., 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 using mean-field asymptotics. Our approach enables us to accurately estimate the effect of small changes to system parameters by combining unobstrusive randomization with light-weight 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 based on perturbations whose magnitude can get vanishingly small in large systems.

New Economics Papers: this item is included in nep-exp
Date: 2019-03, Revised 2019-08
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