---
title: "Online Fair Division of Chores with Upgrade Recourse"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.00432v1"
permalink: "https://feed.astralor.com/items/itm_1ffced0d72ab634136f69a4c"
published: "2026-10-30T22:30:14.000Z"
score: 55
channel: "ai"
tags: ["AI Agent","多模态","Online Fair Division","Chores","Upgrade Recourse","EF1","MMS"]
---

# Online Fair Division of Chores with Upgrade Recourse

## At a glance

We study the online fair division of indivisible chores among $n$ agents under upgrade recourse, where earlier assignments may be revised but each chore previously assigned to an agent must be replaced by a distinct cho…

## Key points

- We study the online fair division of indivisible chores among $n$ agents under upgrade recourse, where earlier assignments may be revised but each chore previously assigned to an agent must be replaced by a distinct cho…
- We show that a simple adaptation of round robin maintains envy-freeness up to one chore (EF1) together with a $(2-1/n)$-approximation of the maximin share (MMS), and that no deterministic algorithm without predictions c…
- This stands in contrast to the allocation of goods, where the optimal MMS factor vanishes as $n$ grows and cannot be achieved together with EF1.

## Full text

We study the online fair division of indivisible chores among $n$ agents under upgrade recourse, where earlier assignments may be revised but each chore previously assigned to an agent must be replaced by a distinct chore that she finds no more costly. We show that a simple adaptation of round robin maintains envy-freeness up to one chore (EF1) together with a $(2-1/n)$-approximation of the maximin share (MMS), and that no deterministic algorithm without predictions can do better, even when all agents rank the chores in the same order. This stands in contrast to the allocation of goods, where the optimal MMS factor vanishes as $n$ grows and cannot be achieved together with EF1. Moreover, knowing only the number of chores modulo a period of length $O(n)$ (and nothing about the costs) suffices to improve upon this factor at the final configuration, while the same algorithm retains a factor below $3$ even if this information is wrong. On the other hand, envy-freeness up to any chore (EFX) essentially requires the exact number of chores: uncertainty between two consecutive values already rules out every finite approximation, and even exact knowledge does not allow EFX to be combined with an MMS guarantee better than the trivial factor of $n$.

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