AAMAS Reproducibility

SkillAI & models

This skill helps your AI strengthen the reproducibility evidence behind interaction claims in AAMAS papers. It makes sure the proofs, game definitions, opponent sets, seeds, compute details, and baselines in a paper actually support what the paper says about its agents. The outcome is a paper whose claims and evidence line up.

Available today. Use it from your connected AI after setup.

After adding the skill, ask your AI to strengthen the reproducibility evidence for the interaction claims in your AAMAS paper or draft. It will work through the proofs, seeds, baselines, and related evidence with you.

Then ask your AI: use the AAMAS Reproducibility skill

What your AI can do with it

  • Strengthen reproducibility evidence for interaction claims
  • Check that proofs and game definitions hold up
  • Review opponent and population sets and self-play setups
  • Track random seeds, compute, and uncertainty on strategic outcomes
  • Compare baselines against the paper's claims
  • Spot gaps between what the paper claims about agents and what it shows

What this skill tells your AI

The instructions your AI receives, as published by brycewang-stanford/awesome-journal-skills in AAMAS-Skills/skills/aamas-reproducibility/SKILL.md and read by ahel’s review.

Use this before submission and again before camera-ready. The reproducibility question at AAMAS is not only "can I rerun the model" but "can I reproduce the interaction - the same agents, the same game, the same emergent outcome."

Evidence map

  • Map each theorem, mechanism property, convergence claim, and empirical interaction claim to a verifiable location in the paper, appendix, supplement, or artifact.
  • For theory, state the game, the information structure, the solution concept, assumptions, proof dependencies, and failure modes clearly enough for a game theorist.
  • For experiments, report the environment, number of agents, opponent/population set, training regime, evaluation opponents, metrics, hyperparameter ranges, chosen settings, seeds, repeated runs, compute, and runtime.
  • For small or noisy strategic differences, add uncertainty: standard errors, confidence intervals, or paired tests over seeds and over opponents.
  • Explain any missing code or environment honestly, and describe how a reader could reproduce the interaction in principle.
  • Keep the artifact consistent with the paper; a claim the artifact cannot demonstrate is a review-risk multiplier.

Claim-to-evidence audit table

ClaimPure-theory answerLearning-plus-game answer
Solution concept reachedProof with the game and information structure statedPlus convergence curves under other agents' adaptation
Opponents / populationNA if fully analyticalThe exact opponent set and how it was chosen
Seeds and varianceNA for deterministic resultsRequired for every stochastic curve and payoff table
ComputeNAHardware, per-run time, and total number of self-play runs

Claiming an equilibrium result while the evaluation only shows two fixed agents playing once is the recognizable AAMAS gap: reviewers read the mismatch between the strategic claim and the thinness of the interaction evidence as carelessness about the rest.

Vignette: a MARL-plus-convergence paper

Consider a submission proving convergence to a coarse-correlated equilibrium in a repeated game, validated by self-play. Its reproducibility spine: the game generator and payoff scale, the learning rule and its step sizes, the opponent set, the replication seeds, the convergence metric, and one honest sentence on the regime where convergence is only empirical, not proved.

Degrees of reproducibility

  • Turnkey: one command reruns the game and regenerates each convergence figure from logged seeds.
  • Scripted: scripts exist but require documented manual steps or an external environment.
  • Descriptive: prose detailed enough that a competent reader could rebuild the game and agents.

For AAMAS, the strategic core should be turnkey because reviewers actually re-run small games; large real-world or human-in-the-loop pipelines may stay scripted with deviations documented. Stating the achieved level honestly beats overpromising turnkey behavior that fails on a clean machine.

Output format

[Claim inventory] <claim -> evidence location>
[Artifact consistency] complete / inconsistent / missing
[Interaction reproducibility gaps] <game/opponents/seeds/uncertainty/compute>
[Paper fixes] <must appear in main PDF>
[Supplement fixes] <appendix or artifact additions>

Signals

GitHub stars
1k
Forks
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Last commit
Aug 2026
Advanced
Catalog kind
skill
Gateway key
aamas-reproducibility
Source
github.com/brycewang-stanford/awesome-journal-skills