Aamas artifact evaluation

AAMAS Artifact Evaluation

Use this for evidence packaging around AAMAS. Because the venue is about interaction, an artifact must make a multiagent claim inspectable: the game, the other agents, and the protocol, not just a single trained model.

Artifact plan

• Decide what a reviewer needs to believe the interaction claim: game or environment code, opponent/population definitions, the training regime, seeds, payoff logs, proofs, or qualitative episode traces.
• Keep decision-critical evidence in the main paper or appendix; optional bulk runs can live in the supplementary zip.
• Anonymize repository history, paths, environment names, license headers, cluster paths, and commit authors for the review version.
• Include a minimal reproduction map: environment build, dependencies, hardware, commands, expected outputs, per-run wall-clock, seeds, and known nondeterminism (especially in self-play).
• For a deployed or human-subject setting, give enough provenance for credible reproduction without violating data-use terms.
• After acceptance, replace anonymous archives with a public, licensed, citable artifact.

What AAMAS evidence reviewers open first

The single fact that shapes packaging: a reviewer will re-run a small game far sooner than they will retrain a large policy, so make the strategic core turnkey before polishing anything.

Claim type	First artifact inspected	Common failure caught
Convergence to an equilibrium	The game definition and the learning-rule code	Solution concept named in the paper but not encoded in the evaluation
Emergent cooperation/defection	The environment and reward specification	Result depends on an undocumented reward-shaping constant
Beats other agents	The opponent/population set and match protocol	Only self-play reported; no held-out opponents
Mechanism is truthful	The payment rule plus a strategic-deviation test	No script that lets an agent try to game the mechanism

Worked vignette: packaging a self-play study

A hypothetical submission claims a learning rule that converges to a correlated equilibrium in a repeated congestion game, shown by self-play.

• Ship the game as one parameterized generator (number of agents, capacity, payoff scale) rather than constants buried in a notebook, so reviewers can vary the interaction.
• Record the exact seed sequence and replication count behind every convergence plot; an equilibrium-convergence claim without seeds is unfalsifiable.
• Emit payoff and regret tables directly from logged results so PDF and artifact numbers cannot drift.
• Include a strategic-deviation harness: a script that drops in a non-conforming agent and measures whether it profits, because that is exactly what a game-theory reviewer will try.

Calibration anchors

• Supplement inspection at AAMAS is at reviewer discretion; assume only the README and one entry script get opened, and design the top level accordingly.
• Supplement size and format caps vary by cycle (25 MB single zip in 2026); verify against the current OpenReview form rather than a past year.

Output format

[Artifact role] anonymous supplement / camera-ready release / public archive
[Contents] <game/env/opponents/seeds/proofs/logs>
[Anonymity risks] <paths/metadata/licenses/URLs>
[Reproduction level] turnkey / scripted / descriptive / weak
[Fixes before upload] <ordered list>