Competing Bandits in Matching Markets

Lydia T. Liu, Horia Mania, Michael Jordan
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:1618-1628, 2020.

Abstract

Stable matching, a classical model for two-sided markets, has long been studied assuming known preferences. In reality agents often have to learn about their preferences through exploration. With the advent of massive online markets powered by data-driven matching platforms, it has become necessary to better understand the interplay between learning and market objectives. We propose a statistical learning model in which one side of the market does not have a priori knowledge about its preferences for the other side and is required to learn these from stochastic rewards. Our model extends the standard multi-armed bandits framework to multiple players, with the added feature that arms have preferences over players. We study both centralized and decentralized approaches to this problem and show surprising exploration-exploitation trade-offs compared to the single player multi-armed bandits setting.

Cite this Paper


BibTeX
@InProceedings{pmlr-v108-liu20c, title = {Competing Bandits in Matching Markets}, author = {Liu, Lydia T. and Mania, Horia and Jordan, Michael}, booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics}, pages = {1618--1628}, year = {2020}, editor = {Silvia Chiappa and Roberto Calandra}, volume = {108}, series = {Proceedings of Machine Learning Research}, month = {26--28 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v108/liu20c/liu20c.pdf}, url = { http://proceedings.mlr.press/v108/liu20c.html }, abstract = {Stable matching, a classical model for two-sided markets, has long been studied assuming known preferences. In reality agents often have to learn about their preferences through exploration. With the advent of massive online markets powered by data-driven matching platforms, it has become necessary to better understand the interplay between learning and market objectives. We propose a statistical learning model in which one side of the market does not have a priori knowledge about its preferences for the other side and is required to learn these from stochastic rewards. Our model extends the standard multi-armed bandits framework to multiple players, with the added feature that arms have preferences over players. We study both centralized and decentralized approaches to this problem and show surprising exploration-exploitation trade-offs compared to the single player multi-armed bandits setting. } }
Endnote
%0 Conference Paper %T Competing Bandits in Matching Markets %A Lydia T. Liu %A Horia Mania %A Michael Jordan %B Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2020 %E Silvia Chiappa %E Roberto Calandra %F pmlr-v108-liu20c %I PMLR %P 1618--1628 %U http://proceedings.mlr.press/v108/liu20c.html %V 108 %X Stable matching, a classical model for two-sided markets, has long been studied assuming known preferences. In reality agents often have to learn about their preferences through exploration. With the advent of massive online markets powered by data-driven matching platforms, it has become necessary to better understand the interplay between learning and market objectives. We propose a statistical learning model in which one side of the market does not have a priori knowledge about its preferences for the other side and is required to learn these from stochastic rewards. Our model extends the standard multi-armed bandits framework to multiple players, with the added feature that arms have preferences over players. We study both centralized and decentralized approaches to this problem and show surprising exploration-exploitation trade-offs compared to the single player multi-armed bandits setting.
APA
Liu, L.T., Mania, H. & Jordan, M.. (2020). Competing Bandits in Matching Markets. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 108:1618-1628 Available from http://proceedings.mlr.press/v108/liu20c.html .

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