Fair and Efficient Online Allocations with Normalized Valuations
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Abstract (arXiv)
A set of divisible resources becomes available over a sequence of rounds and needs to be allocated immediately and irrevocably. Our goal is to distribute these resources to maximize fairness and efficiency. Achieving any non-trivial guarantees in an adversarial setting is impossible. However, we show that normalizing the agent values, a very common assumption in fair division, allows us to escape this impossibility. Our main result is an online algorithm for the case of two agents that ensures the outcome is envy-free while guaranteeing 91.6% of the optimal social welfare. We also show that this is near-optimal: there is no envy-free algorithm that guarantees more than 93.3% of the optimal social welfare.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Sep 2020.
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Citing papers per year: 2024: 3; 2025: 3; 2026: 1.
7 papers.
- 2025Online Fair Division with Additional Informationcited by 6
- 2024Keep Everyone Happy: Online Fair Division of Numerous Items with Few Copiescited by 4
- 2024Automating Food Drop: The Power of Two Choices for Dynamic and Fair Food Allocationcited by 3
- 2025Improved Regret Bounds for Online Fair Division with Bandit Learningcited by 3
- 2025Online Fair Division for Personalized 2-Value Instancescited by 3
- 2024Fair Allocation in Dynamic Mechanism Designcited by 1
- 2026Simultaneous Envy and Equitability Guaranteescited by 0
- 2026Simultaneous Envy and Equitability Guarantees26 Aug 2026
- 2025Online Fair Division with Additional Information30 May 2025
- 2025Online Fair Division for Personalized 2-Value Instances28 May 2025
- 2025Improved Regret Bounds for Online Fair Division with Bandit Learning13 Jan 2025
- 2024Keep Everyone Happy: Online Fair Division of Numerous Items with Few Copies23 Aug 2024
- 2024Automating Food Drop: The Power of Two Choices for Dynamic and Fair Food Allocation10 Jun 2024
- 2024Fair Allocation in Dynamic Mechanism Design31 May 2024