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Research map / Multi-agent systems and games / Games and equilibria

Learning with strategic agents

Designs contracts, menus, and mechanisms for learning in principal-agent and strategic interactions.

74 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026) · an approach in Games and equilibria, keyword cluster “principal · strategic · welfare”

State History Tried Checklist 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.

22 papers in 2023–24 and 25 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic held steady, from 17.3% to 17.5%.

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: 1162017: 0172018: 0182019: 2192020: 6202021: 10212022: 8222023: 10232024: 12242025: 13252026: 1226

Most cited paper of each year

Earliest papers here

What has been tried

45 papers here report 324 measured changes (a component added, removed or swapped, compared with a baseline): helped 59%, no clear effect 9%, hurt 24%, mixed 8%. Papers mostly publish changes that helped, so "helped" is over-represented.

Ablations in recent papers

What was changedMeasured onResultEffectPaper
Restrict the endpoints from getting too close to the obstacle (TwoExtremeRestrict)Maximum cost (L=0) · approximation ratio\frac{2}{1+k} -> 2\cdot \max\left(\frac{1-(1-k)c}{1\!+\!k\!-(1-k)c}, \frac{k(2c-c^2)+1-c^2}{2-2c+2ck}, \frac{1+2ck}{2\!HelpedMechanism Design for Connecting Regions Under Disruptions (AAAI 2025 · 0 citations)
Vary the no-conflict weighting factor beta; use a value between 0.7 and 0.8Harvest · GeoMeanMixedFair Cooperation in Mixed-Motive Games via Conflict-Aware Gradient Adjustment (NeurIPS 2025 · 0 citations)

What reviewers will expect

Datasets and baselines named in the 16 papers of this approach from 2025–2026 that have a full-text summary. Leaving out the common ones is a frequent review question.

Datasets

  • Cleanup · 2 of 16 papers

Baselines

  • Ind · 2 of 16 papers

Recent preprints

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    Who publishes on it

    Institutions on the most papers of this approach since 2023 (of 47 papers; from OpenAlex and the papers' affiliations).

    Use it

    Download BibTeX of 37 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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