AI Paper Atlas

Research map / Learning theory and algorithms / Submodular and game algorithms

Fair division of indivisible goods

Study envy-free and efficient allocation of goods and chores with approximation guarantees.

130 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026) · an approach in Submodular and game algorithms, keyword cluster “voting · allocation · agents”

State History Tried Preprints Who Export

Where it stands

Not enough benchmark data Fewer than four papers report the same benchmark, so there is no record to follow.

24 papers in 2023–24 and 46 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic grew, from 11.8% to 19.0%.

How this is decided

A benchmark is tracked when at least four papers of this approach report their own method on the same dataset and metric in at least two different years. Its record is the best number reported up to 2024 compared with the best reported in 2025–26: still improving means at least 1% better, flat means at most 0.2% better with at least two papers from 2025–26 trying. Numbers on a different scale from the rest (a fraction among percentages) and far outliers are left out. Results come from different papers with different setups, so they are signals, not head-to-head comparisons. The share of papers is counted at ICML, NeurIPS, ICLR, CVPR and AAAI.

How it got here

2016: 3162017: 3172018: 7182019: 9192020: 12202021: 14212022: 12222023: 4232024: 20242025: 16252026: 3026

Most cited paper of each year

Earliest papers here

What has been tried

77 papers here report 547 measured changes (a component added, removed or swapped, compared with a baseline): helped 57%, no clear effect 6%, hurt 33%, mixed 3%. Papers mostly publish changes that helped, so "helped" is over-represented.

Ablations in recent papers

What was changedMeasured onResultEffectPaper
Remove the cleanness requirement from MWHW_xMatroid-rank valuations · TWEF(x,1-x) guaranteeHurtWeighted Envy-Freeness for Submodular Valuations (AAAI 2024 · 5 citations)
use only predictions, without the stealing componentSynthetic two-player instances with accurate predictions · MMS allocation qualityNo clear effectPlant-and-Steal: Truthful Fair Allocations via Predictions (NeurIPS 2024 · 1 citation)
use the greedy-like allocation with a 1/2-MMS stopping thresholdSPLC fair division (example with 4 agents and 8 goods) · MMS approximation factor1/2Hurt1/2-Approximate MMS Allocation for Separable Piecewise Linear Concave Valuations (AAAI 2024)

Recent preprints

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    Who publishes on it

    Institutions on the most papers of this approach since 2023 (of 70 papers; from OpenAlex and the papers' affiliations).

    Use it

    Download BibTeX of 38 key papersOpen the topic in the interactive map

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    Same keyword cluster

    Elsewhere in the topic

    Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).

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