Disentangling Exploration from Exploitation

Fuente: arXiv
Enregistré dans:
Détails bibliographiques
Auteurs principaux: Lizzeri, Alessandro, Shmaya, Eran, Yariv, Leeat
Format: Preprint
Publié: 2024
Sujets:
Accès en ligne:
Tags: Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
_version_ 1866910428467036160
author Lizzeri, Alessandro
Shmaya, Eran
Yariv, Leeat
author_facet Lizzeri, Alessandro
Shmaya, Eran
Yariv, Leeat
contents Starting from Robbins (1952), the literature on experimentation via multi-armed bandits has wed exploration and exploitation. Nonetheless, in many applications, agents' exploration and exploitation need not be intertwined: a policymaker may assess new policies different than the status quo; an investor may evaluate projects outside her portfolio. We characterize the optimal experimentation policy when exploration and exploitation are disentangled in the case of Poisson bandits, allowing for general news structures. The optimal policy features complete learning asymptotically, exhibits lots of persistence, but cannot be identified by an index a la Gittins. Disentanglement is particularly valuable for intermediate parameter values.
format Preprint
id arxiv_https___arxiv_org_abs_2404_19116
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Disentangling Exploration from Exploitation
Lizzeri, Alessandro
Shmaya, Eran
Yariv, Leeat
Theoretical Economics
Computer Science and Game Theory
Starting from Robbins (1952), the literature on experimentation via multi-armed bandits has wed exploration and exploitation. Nonetheless, in many applications, agents' exploration and exploitation need not be intertwined: a policymaker may assess new policies different than the status quo; an investor may evaluate projects outside her portfolio. We characterize the optimal experimentation policy when exploration and exploitation are disentangled in the case of Poisson bandits, allowing for general news structures. The optimal policy features complete learning asymptotically, exhibits lots of persistence, but cannot be identified by an index a la Gittins. Disentanglement is particularly valuable for intermediate parameter values.
title Disentangling Exploration from Exploitation
topic Theoretical Economics
Computer Science and Game Theory
url https://arxiv.org/abs/2404.19116