Summary of Improved Bandits in Many-to-one Matching Markets with Incentive Compatibility, by Fang Kong et al.
Improved Bandits in Many-to-one Matching Markets with Incentive Compatibility
by Fang Kong, Shuai Li
First submitted to arxiv on: 3 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Science and Game Theory (cs.GT)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In the context of two-sided matching markets, researchers have employed online algorithms to learn participants’ preferences through iterative interactions. However, existing approaches in many-to-one settings with responsiveness are suboptimal and lack guarantees of incentive compatibility. This paper proposes the adaptively explore-then-deferred-acceptance (AETDA) algorithm for responsiveness setting, which derives an upper bound for player-optimal stable regret while ensuring incentive compatibility. Additionally, it explores broader substitutable preferences, a condition that ensures the existence of a stable matching and covers responsiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In two-sided matching markets, people are trying to find matches between themselves and others. They don’t always know what they want, so computers help them figure out their preferences. A recent study looked at this problem in a specific situation where one person is matched with many others. However, the results weren’t very good, and it’s hard to get people to do what’s best for everyone. This paper tries to fix these problems by creating an algorithm that works better and makes sure people are happy with their matches. |
