Active Learning for Fair and Stable Online Allocations
This work addresses efficient and fair online resource allocation for systems with restricted feedback, offering incremental improvements in algorithm design.
The paper tackles dynamic fair resource allocation with limited feedback by proposing active learning algorithms that select a subset of agents for feedback each epoch, achieving sub-linear regret bounds for fairness and stability metrics.
We explore an active learning approach for dynamic fair resource allocation problems. Unlike previous work that assumes full feedback from all agents on their allocations, we consider feedback from a select subset of agents at each epoch of the online resource allocation process. Despite this restriction, our proposed algorithms provide regret bounds that are sub-linear in number of time-periods for various measures that include fairness metrics commonly used in resource allocation problems and stability considerations in matching mechanisms. The key insight of our algorithms lies in adaptively identifying the most informative feedback using dueling upper and lower confidence bounds. With this strategy, we show that efficient decision-making does not require extensive feedback and produces efficient outcomes for a variety of problem classes.