Authors: Kiet Q. H. Vo, Muneeb Aadil, Siu Lun Chau, Krikamol Muandet

Published on: August 30, 2023

Impact Score: 8.12

Arxiv code: Arxiv:2308.16262

Summary

• What is new: This research introduces a new approach to agent selection in causal strategic learning that accounts for the dynamics of agent evaluation and selection, and the interference of multiple decision makers’ selection rules.
• Why this is important: The challenge of selecting agents in a way that encourages their improvement without unfairly reducing their chances of being chosen, especially in environments with multiple decision makers.
• What the research proposes: An analytical form of the optimal selection rule that balances the trade-off between selecting the best agents and incentivizing improvement, alongside a cooperative protocol for decision makers to correctly estimate causal parameters.
• Results: The study shows that a carefully designed selection rule and cooperative protocol can improve agents’ outcomes, provide fair selection chances, and accurately estimate causal parameters in strategic learning environments.

Technical Details

Technological frameworks used: Causal strategic learning

Models used: Simulation studies, Analytical forms

Data used: Observational data

Potential Impact

Recruitment industries, competitive marketplaces, and platforms that rely on strategic agent selection and performance improvement.

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