Multi-agent Contracts
Paul Dütting, Tomer Ezra, Michal Feldman, Thomas Kesselheim
2023年份
12被引次数
18顶会引用
摘要
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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引用它的顶会 Paper18
- Contracting with a Learning AgentGuru Guruganesh, Yoav Kolumbus, Jon Schneider, Inbal Talgam-Cohen 等NeurIPS 2024 · 被引用 38 次
- Deep Contract Design via Discontinuous NetworksTonghan Wang, Paul Duetting, Dmitry Ivanov, Inbal Talgam-Cohen 等NeurIPS 2023 · 被引用 23 次
- Optimal No-Regret Learning for One-Sided Lipschitz FunctionsPaul Duetting, Guru Guruganesh, Jon Schneider, Joshua Ruizhi WangICML 2023 · 被引用 22 次
- Learning Optimal Contracts: How to Exploit Small Action SpacesFrancesco Bacchiocchi, Matteo Castiglioni, Alberto Marchesi, Nicola GattiICLR 2024 · 被引用 21 次
- Delegated ClassificationEden Saig, Inbal Talgam-Cohen, Nir RosenfeldNeurIPS 2023 · 被引用 19 次
相关 Paper
- Optimal Common Contract with Heterogeneous AgentsShenke Xiao, Zihe Wang, Mengjing Chen, Pingzhong Tang 等AAAI 2020 · 被引用 14 次
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- Combinatorial ContractsPaul Dütting, Tomer Ezra, Michal Feldman, Thomas KesselheimFOCS 2021 · 被引用 18 次
- Multi-Agent Combinatorial ContractsPaul Dütting, Tomer Ezra, Michal Feldman, Thomas KesselheimSODA 2025 · 被引用 5 次
- Combinatorial Contracts Beyond Gross SubstitutesPaul Dütting, Michal Feldman, Yoav Gal TzurSODA 2024 · 被引用 7 次
