Learning to Assign Prediction Tasks to Agents with Capacity Constraints
Quick summary
arXiv:2605.27999v2 Announce Type: replace-cross Abstract: We address the problem of learning to assign prediction tasks to one agent from a set of available agents, including human decision-makers and AI models. We focus on sequential learning of agent expertise and assignment policies where each agent is constrained to handle a fraction of tasks. We provide a general theoretical characterization of this problem in terms of agent capacities, differences in agent expertise, and task context. We then develop a framework of sequential explore-exploit policy-learning algorithms that seek to maximi
Key takeaways
- arXiv:2605.27999v2 Announce Type: replace-cross Abstract: We address the problem of learning to assign prediction tasks to one agent from a set of available agents, including human decision-makers and AI models.
- We focus on sequential learning of agent expertise and assignment policies where each agent is constrained to handle a fraction of tasks.
- We provide a general theoretical characterization of this problem in terms of agent capacities, differences in agent expertise, and task context.
Why it matters
“Learning to Assign Prediction Tasks to Agents with Capacity Constraints” may affect what data AI products can use and where accountability sits. Product teams should watch compliance duties, rights holders should watch enforcement, and users should watch transparency and appeal mechanisms.
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