Skills

Aamas Reproducibility

Use when strengthening AAMAS reproducibility evidence for interaction claims, including...

Install

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aamas-reproducibility -g -a claude-code -y
Category
AI and agents
Updated
1mo ago
License
MIT
Context cost
Lightabout 839 tokens
Runs scripts
No, instructions only

What it does

Use when strengthening AAMAS reproducibility evidence for interaction claims, including proofs and game definitions, opponent and population sets, self-play protocols, random seeds, compute, uncertainty on strategic outcomes, baselines, and consistency between what the paper claims about agents and what the artifact can actually show.

AAMAS Reproducibility

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
Read the whole SKILL.md on GitHub

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