Repeated Fair Allocation of Indivisible Items

Abstract

The problem of fairly allocating a set of indivisible items is a well-known challenge in the field of (computational) social choice. In this scenario, there is a fundamental incompatibility between notions of fairness (such as envy-freeness and proportionality) and economic efficiency (such as Pareto-optimality). However, in the real world, items are not always allocated once and for all, but often repeatedly. For example, the items may be recurring chores to distribute in a household. Motivated by this, we initiate the study of the repeated fair division of indivisible goods and chores, and propose a formal model for this scenario. In this paper, we show that, if the number of repetitions is a multiple of the number of agents, there always exists a sequence of allocations that is proportional and Pareto-optimal. On the other hand, irrespective of the number of repetitions, an envy-free and Pareto-optimal sequence of allocations may not exist. For the case of two agents, we show that if the number of repetitions is even, it is always possible to find a sequence of allocations that is overall envy-free and Pareto-optimal. We then prove even stronger fairness guarantees, showing that every allocation in such a sequence satisfies some relaxation of envy-freeness. Finally, in case that the number of repetitions can be chosen freely, we show that envy-free and Pareto-optimal allocations are achievable for any number of agents.

Cite

Text

Igarashi et al. "Repeated Fair Allocation of Indivisible Items." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I9.28837

Markdown

[Igarashi et al. "Repeated Fair Allocation of Indivisible Items." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/igarashi2024aaai-repeated/) doi:10.1609/AAAI.V38I9.28837

BibTeX

@inproceedings{igarashi2024aaai-repeated,
  title     = {{Repeated Fair Allocation of Indivisible Items}},
  author    = {Igarashi, Ayumi and Lackner, Martin and Nardi, Oliviero and Novaro, Arianna},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2024},
  pages     = {9781-9789},
  doi       = {10.1609/AAAI.V38I9.28837},
  url       = {https://mlanthology.org/aaai/2024/igarashi2024aaai-repeated/}
}