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

Matching markets and welfare

Analyze matching and social-welfare optimization with strategic or learning agents in platforms.

119 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.

28 papers in 2023–24 and 27 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic shrank, from 13.8% to 11.2%.

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: 7162017: 9172018: 10182019: 8192020: 11202021: 9212022: 10222023: 13232024: 15242025: 14252026: 1326

Most cited paper of each year

Earliest papers here

What has been tried

90 papers here report 597 measured changes (a component added, removed or swapped, compared with a baseline): helped 62%, no clear effect 7%, hurt 24%, mixed 7%. Papers mostly publish changes that helped, so "helped" is over-represented.

Ablations in recent papers

What was changedMeasured onResultEffectPaper
Use binary cross-entropy instead of mean-squared error for the lossUniform utility model · manipulation performanceNo clear effectLearning to Manipulate Under Limited Information (AAAI 2025 · 4 citations)
Remove the trick of running MIPs only when neededPublic project, 6–10 agents · gap to theoretical upper boundWCT-1 min gaps: 0.1830, 0.0933, 0.0939, 0.1917, 0.3086; WCT min gaps: 0.1775, 0.0888, 0.0973, 0.1329, 0.2576MixedWorst-Case VCG Redistribution Mechanism Design Based on the Lottery Ticket Hypothesis (AAAI 2024 · 2 citations)
replace direct rounding with partition roundingmulti-task peer prediction · sensitivityexactly \sqrt{K} timesHelpedStochastically Dominant Peer Prediction (NeurIPS 2025 · 0 citations)

Recent preprints

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    Who publishes on it

    Institutions on the most papers of this approach since 2023 (of 55 papers; from OpenAlex and the papers' affiliations).

    Use it

    Download BibTeX of 40 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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