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Experimenting in Equilibrium

Author

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  • Stefan Wager

    (Graduate School of Business, Stanford University, Stanford, California 94305)

  • Kuang Xu

    (Graduate School of Business, Stanford University, Stanford, California 94305)

Abstract

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 p 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 p in large systems using vanishingly small perturbations.

Suggested Citation

  • Stefan Wager & Kuang Xu, 2021. "Experimenting in Equilibrium," Management Science, INFORMS, vol. 67(11), pages 6694-6715, November.
    • Handle: RePEc:inm:ormnsc:v:67:y:2021:i:11:p:6694-6715
    DOI: 10.1287/mnsc.2020.3844
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    References listed on IDEAS

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    Cited by:

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    2. Nian Si, 2023. "Tackling Interference Induced by Data Training Loops in A/B Tests: A Weighted Training Approach," Papers 2310.17496, arXiv.org, revised Feb 2024.

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