Seesaw Experimentation: A/B Tests with Spillovers
This paper investigates how a firm's performance can decline even when consistently implementing innovations validated through A/B testing. We introduce the concept of *seesaw …
Dynamic Selection in Algorithmic Decision-making
This paper identifies and addresses dynamic selection problems in online learning algorithms with endogenous data. In a contextual multi-armed bandit model, a novel bias …
High-Dimensional Simulation Optimization via Brownian Fields and Sparse Grids
High-dimensional simulation optimization is notoriously challenging. We propose a new sampling algorithm that converges to a global optimal solution and suffers minimally from the …
Affine Jump-Diffusions: Stochastic Stability and Limit Theorems
Affine jump-diffusions constitute a large class of continuous-time stochastic models that are particularly popular in finance and economics due to their analytical tractability. …
Scalable Stochastic Kriging with Markovian Covariances
Stochastic kriging is a popular technique for simulation metamodeling due to its flexibility and analytical tractability. Its computational bottleneck is the inversion of a …
Stochastic Kriging for Inadequate Simulation Models
Stochastic kriging is a popular metamodeling technique for representing the unknown response surface of a simulation model. However, the simulation model may be inadequate in the …
Lu Zou