6 papers
Estimation of Treatment Effects Under Nonstationarity via the Truncated Policy Gradient Estimator
Ramesh Johari, Tianyi Peng, Wenqian Xing
Randomized experiments (or A/B tests) are widely used to evaluate interventions in dynamic systems such as recommendation platforms, marketplaces, and digital health. In these sett…
AI Agents for Inventory Control: Human-LLM-OR Complementarity
Jackie Baek, Yaopeng Fu, Will Ma +1
Inventory control is a fundamental operations problem in which ordering decisions are traditionally guided by theoretically grounded operations research (OR) algorithms. However, s…
Digital Twins as Funhouse Mirrors: Five Key Distortions
Tianyi Peng, George Gui, Melanie Brucks +20
Scientists and practitioners are increasingly moving to deploy digital twins--LLM-based models of real individuals--across social science and policy research. We conduct 19 pre-reg…
Multi-agent Markov Entanglement
Shuze Chen, Tianyi Peng
Value decomposition has long been a fundamental technique in multi-agent dynamic programming and reinforcement learning (RL). Specifically, the value function of a global state $(s…
Differences-in-Neighbors for Network Interference in Experiments
Tianyi Peng, Naimeng Ye, Andrew Zheng
Experiments in online platforms frequently suffer from network interference, in which a treatment applied to a given unit affects outcomes for other units connected via the platfor…
Speeding up Policy Simulation in Supply Chain RL
Vivek Farias, Joren Gijsbrechts, Aryan Khojandi +2
Simulating a single trajectory of a dynamical system under some state-dependent policy is a core bottleneck in policy optimization (PO) algorithms. The many inherently serial polic…