Dynamic fair division with partial information
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Published version
Date
2022-10-31
DOI
Authors
Benadè, Gerdus
Psomas, Alexandros
Halpern, Daniel
Version
Published version
OA Version
Citation
J.G. Benade, A. Psomas, D. Halpern. "Dynamic Fair Division with Partial Information" Advances in neural information processing systems. NeurIPS 2022
Abstract
We consider the fundamental problem of fairly and efficiently allocating T indivisible items among n agents with additive preferences. The items become available over a sequence of rounds, and every item must be allocated immediately and irrevocably before the next one arrives. Previous work shows that when the agents' valuations for the items are drawn from known distributions, it is possible (under mild technical assumptions) to find allocations that are envy-free with high probability and Pareto efficient ex-post.
We study a partial-information setting, where it is possible to elicit ordinal but not cardinal information. When a new item arrives, the algorithm can query each agent for the relative rank of this item with respect to a subset of the past items.
When values are drawn from i.i.d. distributions, we give an algorithm that is envy-free and (1-𝜖)-welfare-maximizing with high probability. We provide similar guarantees (envy-freeness and a constant approximation to welfare with high probability) even with minimally expressive queries that ask for a comparison to a single previous item. For independent but non-identical agents, we obtain envy-freeness and a constant approximation to Pareto efficiency with high probability. We prove that all our results are asymptotically tight.