Competing Bandits in Matching Markets
| Autor: | Liu, Lydia T., Mania, Horia, Jordan, Michael I. |
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| Rok vydání: | 2019 |
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| Druh dokumentu: | Working Paper |
| Popis: | Stable matching, a classical model for two-sided markets, has long been studied with little consideration for how each side's preferences are learned. 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. Comment: 15 pages, 3 figures. A version appears in the Proceedings of The 23nd International Conference on Artificial Intelligence and Statistics (AISTATS), 2020 |
| Databáze: | arXiv |
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