@inproceedings{020956cb1016469d99d6ac36c4372b6a,
title = "Competing bandits: Learning under competition",
abstract = "Most modern systems strive to learn from interactions with users, and many engage in exploration: making potentially suboptimal choices for the sake of acquiring new information. We initiate a study of the interplay between exploration and competition—how such systems balance the exploration for learning and the competition for users. Here the users play three distinct roles: they are customers that generate revenue, they are sources of data for learning, and they are self-interested agents which choose among the competing systems. In our model, we consider competition between two multi-armed bandit algorithms faced with the same bandit instance. Users arrive one by one and choose among the two algorithms, so that each algorithm makes progress if and only if it is chosen. We ask whether and to what extent competition incentivizes the adoption of better bandit algorithms. We investigate this issue for several models of user response, as we vary the degree of rationality and competitiveness in the model. Our findings are closely related to the “competition vs. innovation” relationship, a well-studied theme in economics.",
keywords = "Competition, Exploration, Game theory, Machine learning, Rationality",
author = "Yishay Mansour and Aleksandrs Slivkins and Wu, {Zhiwei Steven}",
note = "Publisher Copyright: {\textcopyright} Yishay Mansour, Aleksandrs Slivkins, and Zhiwei Steven Wu.; 9th Innovations in Theoretical Computer Science, ITCS 2018 ; Conference date: 11-01-2018 Through 14-01-2018",
year = "2018",
month = jan,
day = "1",
doi = "10.4230/LIPIcs.ITCS.2018.48",
language = "אנגלית",
series = "Leibniz International Proceedings in Informatics, LIPIcs",
publisher = "Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing",
editor = "Karlin, {Anna R.}",
booktitle = "9th Innovations in Theoretical Computer Science, ITCS 2018",
address = "גרמניה",
}