线上老虎机

TIME: 2025/4/2   09:00-10:00

VENUE: 318 E&F HALL

Title: Practical Performative Policy Learning with Strategic Agents

Abstract: This paper studies the performative policy learning problem, where agents adjust their features in response to a released policy to improve their potential outcomes, inducing an endogenous distribution shift. There has been a growing interest in training machine learning models in strategic environments, including strategic classification [Hardt et al., 2016] and performative prediction [Perdomo et al., 2020]. However, existing approaches often rely on restrictive parametric assumptions: micro-level utility models in strategic classification and macro-level data distribution maps in performative prediction, severely limiting scalability and generalizability. We approach this problem as a complex causal inference task, relaxing parametric assumptions on both micro-level agent behavior and macro-level data distribution. Leveraging bounded rationality, we uncover a practical low-dimensional structure in distribution shifts and construct an effective mediator in the causal path from the deployed model to the shifted data. We then propose a gradient- based policy optimization algorithm with a differentiable classifier serving as a substitute for the high-dimensional distribution map. Our algorithm efficiently utilizes batch feedback and limited manipulation patterns. Our approach achieves high sample efficiency compared to methods reliant on bandit feedback or zero-order optimization. We also provide theoretical guarantees for algorithmic convergence. Extensive and challenging experiments1 on high-dimensional settings demonstrate our method’s practical efficacy.

Bio of the seminar speaker: 

LI Bo, Associate Professor (with Tenure), School of Economics and Management, Tsinghua University