Multi-agent Contracts
Paul Dütting, Tomer Ezra, Michal Feldman, Thomas Kesselheim
2023Year
12Citations
18Top-tier citations
Abstract
We study a natural combinatorial single-principal multi-agent contract design problem, in which a principal motivates a team of agents to exert effort toward a given task. At the heart of our model is a reward function, which maps the agent efforts to an expected reward of the principal. We seek to design computationally efficient algorithms for finding optimal (or near-optimal) linear contracts for reward functions that belong to the complement-free hierarchy.
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Cited by top-tier papers18
- Contracting with a Learning AgentGuru Guruganesh, Yoav Kolumbus, Jon Schneider, Inbal Talgam-Cohen et al.NeurIPS 2024 · 38 citations
- Deep Contract Design via Discontinuous NetworksTonghan Wang, Paul Duetting, Dmitry Ivanov, Inbal Talgam-Cohen et al.NeurIPS 2023 · 23 citations
- Optimal No-Regret Learning for One-Sided Lipschitz FunctionsPaul Duetting, Guru Guruganesh, Jon Schneider, Joshua Ruizhi WangICML 2023 · 22 citations
- Learning Optimal Contracts: How to Exploit Small Action SpacesFrancesco Bacchiocchi, Matteo Castiglioni, Alberto Marchesi, Nicola GattiICLR 2024 · 21 citations
- Delegated ClassificationEden Saig, Inbal Talgam-Cohen, Nir RosenfeldNeurIPS 2023 · 19 citations
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