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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 2·5 min read
Keep Everyone Happy: Online Fair Division of Numerous Items with Few Copies
TL;DR
A new approach to online fair division is proposed, addressing the challenge of allocating indivisible items among multiple agents with limited copies. The algorithms, modeled as a contextual bandit problem, demonstrate effective utility estimation and achieve sublinear regret in experiments.
✦ Why It Matters
Implement these algorithms to improve resource allocation strategies in your online platform today.
Key Takeaways
- Explore contextual bandit models for fair resource allocation.
- Utilize algorithms that adapt to limited item interactions.
- Focus on utility as a function of item-agent features.
- Aim for sublinear regret in allocation strategies.
How It Works
The proposed algorithms treat the online fair division problem as a contextual bandit scenario, where each allocation decision is informed by previously observed utilities. By leveraging features of both items and agents, the algorithms can dynamically adjust their strategies to improve fairness and efficiency over time.