Optimizing Adaptive Experiments: A Unified Approach to Regret Minimization and Best-Arm Identification

arXiv:2402.10592v1 Announce Type: new
Abstract: Practitioners conducting adaptive experiments often encounter two competing priorities: reducing the cost of experimentation by effectively assigning treatments during the experiment itself, and gathering information swiftly to conclude the experiment and implement a treatment across the population. Currently, the literature is divided, with studies on regret minimization addressing the former priority in isolation, and research on best-arm identification focusing solely on the latter. This paper proposes a unified model that accounts for both within-experiment performance …

abstract arm arxiv cost cs.lg econ.em experiment experimentation identification information literature population stat.ml treatment type