The Perils of Misspecified Priors and Optional Stopping in Multi-Armed Bandits

Frontiers in Artificial Intelligence

关键词: multi-armed bandits;    sequential testing;    A/B testing;    American options;    optional stopping;   

DOI  :  10.3389/frai.2021.715690

来源: DOAJ

【 摘 要 】

The connection between optimal stopping times of American Options and multi-armed bandits is the subject of active research. This article investigates the effects of optional stopping in a particular class of multi-armed bandit experiments, which randomly allocates observations to arms proportional to the Bayesian posterior probability that each arm is optimal (Thompson sampling). The interplay between optional stopping and prior mismatch is examined. We propose a novel partitioning of regret into peri/post testing. We further show a strong dependence of the parameters of interest on the assumed prior probability density.

【 授权许可】

Unknown