With buffers of size linear in k and number of agents, algorithms achieve EF1 at every step and EF at most steps for personalized k-value mixed manna instances, extending to general additives with ratio dependence.
Temporal Fair Division of Indivisible Goods with Scheduling
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abstract
We study temporal fair division, where agents receive goods over multiple rounds and cumulative fairness is required. We investigate Temporal Envy-Freeness Up to One Good (TEF1) and Up to Any Good (TEFX), its approximation $\alpha$-TEFX, and Temporal Maximin Share (TMMS). Motivated by known impossibilities in standard settings, we consider the model in various restricted settings and extend it by introducing scheduling. Our main contributions draw the boundary between possibility and impossibility. First, regarding temporal fair division without scheduling, we prove that while constant-factor $\alpha$-TEFX is impossible in general, a $1/2$-approximation is achievable for generalized binary valuations and identical days with two agents. Second, regarding temporal fair division with scheduling, we demonstrate that a scheduling buffer of size at least $n/2$ enables TEF1 for identical days. However, we establish that TEFX and TMMS remain largely impossible even with scheduling or restricted domains. These results highlight the inherent difficulty of strict temporal fairness and quantify the trade-offs required to achieve approximation guarantees.
fields
cs.GT 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Online Fair Division Meets Reordering Buffers
With buffers of size linear in k and number of agents, algorithms achieve EF1 at every step and EF at most steps for personalized k-value mixed manna instances, extending to general additives with ratio dependence.