Summary of Incentivized Learning in Principal-agent Bandit Games, by Antoine Scheid et al.
Incentivized Learning in Principal-Agent Bandit Games
by Antoine Scheid, Daniil Tiapkin, Etienne Boursier, Aymeric Capitaine, El Mahdi El Mhamdi, Eric Moulines, Michael I. Jordan, Alain Durmus
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)
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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 The proposed framework extends traditional bandit problems by incorporating repeated interactions between a principal and an agent with misaligned objectives. The principal influences the agent’s decisions using incentives that add up to its rewards, aiming to maximize its total utility. We present nearly optimal learning algorithms for the principal’s regret in both multi-armed and linear contextual settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new framework where a principal tries to influence an agent’s decisions by offering incentives. The goal is to learn what incentives work best to get the desired outcome, considering real-world applications like healthcare or taxation. It presents algorithms that can help the principal minimize regret in different situations and tests these ideas with experiments. |
