“Using mean-field modeling to
power experimentation”
I will
discuss 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.
While classical approaches to experimental design assume that intervening on
one unit does not affect other units, there are many important settings
where this noninterference assumption does not hold, such as when
running experiments on supply-side incentives on a ride-sharing platform
or subsidies in an energy marketplace. 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 show that our approach enables
the platform to optimize p in large systems using vanishingly
small perturbations.
Paper: https://doi.org/10.1287/mnsc.2020.3844
Kuang Xu is an associate professor in the area of
Operations, Information and Technology at Stanford Graduate School of Business,
and an associate professor by courtesy with the Electrical Engineering
Department, Stanford University. Born in Suzhou, China, he received the B.S.
degree in Electrical Engineering (2009) from the University of Illinois at
Urbana-Champaign, and the Ph.D. degree in Electrical Engineering and
Computer Science (2014) from the Massachusetts Institute of Technology. His
research primarily focuses on understanding fundamental properties and
design principles of large-scale stochastic systems using tools from
probability theory and optimization, with applications in queueing networks,
healthcare, privacy and machine learning. He received First Place in the
INFORMS George E. Nicholson Student Paper Competition (2011), the Best Paper
Award, as well as the Kenneth C. Sevcik Outstanding Student Paper Award
at ACM SIGMETRICS (2013), and the ACM SIGMETRICS Rising Star Research
Award (2020). He currently serves as an Associate Editor for Operations
Research.