Asymptotic Analysis of Weighted Fair Division
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 3979-3987.
https://doi.org/10.24963/ijcai.2025/443
Keywords
- Game Theory and Economic Paradigms: GTEP: Fair division
- Agent-based and Multi-agent Systems: MAS: Resource allocation
Abstract
Several resource allocation settings involve agents with unequal entitlements represented by weights. We analyze weighted fair division from an asymptotic perspective: if m items are divided among n agents whose utilities are independently sampled from a probability distribution, when is it likely that a fair allocation exist? We show that if the ratio between the weights is bounded, a weighted envy-free allocation exists with high probability provided that m = Omega(n log n / log log n), generalizing a prior unweighted result. For weighted proportionality, we establish a sharp threshold of m = n / (1 - \mu) for the transition from non-existence to existence, where \mu in (0,1) denotes the mean of the distribution. In addition, we prove that for two agents, a weighted envy-free (and weighted proportional) allocation is likely to exist if m = omega(sqrt{r}), where r denotes the ratio between the two weights.
@inproceedings{ijcai2025p443,
title = {Asymptotic Analysis of Weighted Fair Division},
author = {Manurangsi, Pasin and Suksompong, Warut and Yokoyama, Tomohiko},
booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-25}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {James Kwok},
pages = {3979--3987},
year = {2025},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2025/443},
url = {https://doi.org/10.24963/ijcai.2025/443},
}
TY - CPAPER TI - Asymptotic Analysis of Weighted Fair Division AU - Manurangsi, Pasin AU - Suksompong, Warut AU - Yokoyama, Tomohiko T2 - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2025 SP - 3979 EP - 3987 DO - 10.24963/ijcai.2025/443 UR - https://doi.org/10.24963/ijcai.2025/443 ER -Copied
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