NeurIPS 2023poster1 citations
Bandit Social Learning under Myopic Behavior
Kiarash Banihashem, MohammadTaghi Hajiaghayi, Suho Shin, Aleksandrs Slivkins
Abstract
We study social learning dynamics motivated by reviews on online platforms. The agents collectively follow a simple multi-armed bandit protocol, but each agent acts myopically, without regards to exploration. We allow a wide range of myopic behaviors that are consistent with (parameterized) confidence intervals for the arms’ expected rewards. We derive stark exploration failures for any such behavior, and provide matching positive results. As a special case, we obtain the first general results on failure of the greedy algorithm in bandits, thus providing a theoretical foundation for why bandit algorithms should explore.
multi-armed banditsgreedy algorithmsocial learningmyopic behaviorlearning failuresalgorithmic game theory
BibTeX
@inproceedings{
banihashem2023bandit,
title={Bandit Social Learning under Myopic Behavior},
author={Kiarash Banihashem and MohammadTaghi Hajiaghayi and Suho Shin and Aleksandrs Slivkins},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=YeP8osxOht}
}