
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- Repository
- brycewang-stanford/Awesome-Journal-Skills1.3k
- 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
| Claim | Pure-theory answer | Learning-plus-game answer |
|---|---|---|
| Solution concept reached | Proof with the game and information structure stated | Plus convergence curves under other agents' adaptation |
| Opponents / population | NA if fully analytical | The exact opponent set and how it was chosen |
| Seeds and variance | NA for deterministic results | Required for every stochastic curve and payoff table |
| Compute | NA | Hardware, per-run time, and total number of self-play runs |

