Experimenting in Equilibrium

Experimenting in Equilibrium
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平衡实验

DOI:
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发表时间:
2019
期刊:
Management Sciences
影响因子:
--
通讯作者:
Kuang Xu
Kuang Xu
中科院分区:
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文献类型:
--
作者:
Stefan Wager;Kuang Xu

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经典的实验设计方法假设干预一个单元不会影响其他单元。然而,在许多重要的情况下,这种不干涉假设并不成立,比如在拼车平台上进行供应方激励实验或在能源市场上进行补贴实验。在本文中,我们介绍了一种新的方法,在大规模随机系统的实验设计相当大的跨单位干扰,假设干扰的结构足够,它可以通过平均场建模捕获。我们的方法使我们能够准确地估计系统参数的微小变化的影响,通过结合不引人注目的随机化与轻量级建模,同时保持平衡。然后,我们可以使用这些估计通过梯度下降来优化系统。具体地说,我们专注于一个平台的问题,该平台旨在优化供应方支付[公式:见文字]在一个集中的市场中,不同的供应商通过他们对整体供需平衡的影响进行互动,我们表明,我们的方法使平台能够优化[公式:见文字]在大型系统中使用消失的小扰动。这篇论文被大数据分析师Hamid Nazerzadeh接受。
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.
DOI: 10.1214/20-aos1973
发表时间: 2021-04
影响因子: 4.5
作者:
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通讯作者: Hudgens M
DOI: 10.1038/s42003-022-03898-5
发表时间: 2022-09-08
影响因子: 5.9
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成批强盗的悔恨界限
DOI: --
发表时间: 2021
期刊: Proceedings the AAAI Conference on Human Computation and Crowdsourcing
影响因子: --
作者:
Esfandiari, Hossein;Karbasi, Amin;Mehrabian, Abbas;Mirrokni, Vahab
通讯作者: Mirrokni, Vahab