all AI news
New Perspectives in Online Contract Design: Heterogeneous, Homogeneous, Non-myopic Agents and Team Production
March 13, 2024, 4:42 a.m. | Shiliang Zuo
cs.LG updates on arXiv.org arxiv.org
Abstract: This work studies the repeated principal-agent problem from an online learning perspective. The principal's goal is to learn the optimal contract that maximizes her utility through repeated interactions, without prior knowledge of the agent's type (i.e., the agent's cost and production functions).
I study three different settings when the principal contracts with a $\textit{single}$ agent each round: 1. The agents are heterogeneous; 2. the agents are homogenous; 3. the principal interacts with the same agent …
abstract agent agents arxiv cost cs.gt cs.lg design functions her interactions knowledge learn online learning perspective perspectives prior production studies team through type utility work
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US
Research Engineer
@ Allora Labs | Remote
Ecosystem Manager
@ Allora Labs | Remote
Founding AI Engineer, Agents
@ Occam AI | New York
AI Engineer Intern, Agents
@ Occam AI | US