We study experimental design in large-scale stochastic systems with substantial uncertainty and structured cross-unit interference . We consider 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 propose a class of local experimentation schemes that can be used to optimize these payments without perturbing the overall market equilibrium. We show that , as the system size grows, our scheme can estimate the gradient of the platform 's utility with respect to p while perturbing the overall market equilibrium by only a vanishingly small amount. We can then use these gradient estimates to optimize p via any stochastic first-order optimization method. These results stem from the insight that, while the system involves a large number of interacting units, any interference can only be channeled through a small number of key statistics, and this structure allows us to accurately predict feedback effects that arise from global system changes using only information collected while remaining in equilibrium .
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 .
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We study experimental design in
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
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substantial uncertainty and structured
considerable
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. We consider the
, 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
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propose a class of local experimentation schemes that can be used to optimize these payments without perturbing the overall market equilibrium. We show that , as the system size grows, our scheme can estimate the gradient of the platform 's utility with respect to p while perturbing the overall market equilibrium by only a vanishingly small amount. We can then use these gradient estimates to optimize p via any stochastic first-order optimization method. These results stem from the insight that, while the system involves a large number of interacting units, any interference can only be channeled through a small number of key statistics, and this structure allows us to accurately predict feedback effects that arise from global system changes using only information collected while remaining in equilibrium
show that our approach enables the platform to optimize p based on perturbations whose magnitude can get vanishingly small in large systems