aamas-experiments

name	aamas-experiments
description	Use when designing or auditing AAMAS experiments - self-play and population-based training, opponent selection, equilibrium and regret metrics, game-theoretic simulations, ablations, seeds, hyperparameters, compute, and claim-to-evidence fit - with emphasis on experiments that probe the interaction rather than chase a single-agent leaderboard.

AAMAS Experiments

Use this before submission when the empirical or simulation story is not yet locked. At AAMAS the experiment exists to test the interaction claim, not to top a benchmark.

Experiment audit

• Map each empirical claim to a game, a self-play run, a population sweep, an ablation, or a deviation test.
• Choose opponents deliberately: self-play alone rarely suffices; include held-out opponents, population sets, or classical strategies as the claim requires.
• Separate simulations that validate a solution concept (where the equilibrium is known) from real or applied studies that show practical multiagent behavior.
• Report uncertainty for stochastic results over both seeds and opponents: standard errors, confidence intervals, or paired tests.
• Report the environment, number of agents, training regime, evaluation protocol, metrics, hyperparameter ranges, chosen settings, seeds, hardware, software versions, and runtime.
• Add ablations for the interaction mechanism (communication, reward sharing, the payment rule), not just cosmetic variants.
• Audit for the mismatch between the strategic claim and the setup: an equilibrium claim tested against only one fixed opponent, or a cooperation claim that hides a reward-shaping constant.

What experiments are for at this venue

• The strongest design shows the interaction under stress: agents that can deviate, opponents the method did not train against, and populations that vary in size or composition.
• One experiment that lets agents try to exploit the mechanism and fails to profit is worth more than five extra environments where nothing strategic is tested.
• Reviewers, often game theorists, check whether the metric matches the claim: convergence to a named solution concept, exploitability, social welfare, or regret - not just episodic return.

Interaction-validation design table

Interaction claim	Matching experiment	Reject pattern avoided
Converges to equilibrium	Convergence/exploitability curve under simultaneous adaptation	"Equilibrium asserted, never measured"
Mechanism is truthful	Strategic-deviation test: an agent tries to misreport	"Truthfulness proved, never stress-tested"
Beats other agents	Round-robin vs held-out opponents and a population	"Self-play only"
Emergent cooperation	Sweep over reward/opponent settings with variance	"One seed, one setting, one story"

Vignette: a coordination-protocol study

Suppose the paper claims a learned protocol raises cooperation in a repeated public-goods game. The matching plan: sweep group size and defector fraction for cooperation curves, add held-out opponents that never appeared in training, and inject a free-rider agent to measure whether it profits - every panel tied to a numbered claim or definition.

Statistical reporting floor

• Seeds and replication counts for every stochastic curve; captions must state whether bands are standard errors, confidence intervals, or quantiles, and how many opponents were averaged.
• Report the compute actually consumed by self-play, not vague feasibility language.

Output format

[Experiment readiness] strong / adequate / weak
[Claim -> evidence map] <claim: game / self-play / population / deviation test>
[Missing interaction evidence] <opponents / deviation test / seeds / metric>
[Reproducibility gaps] <hyperparameters / compute / env / seeds>
[Decision-critical next run] <one experiment or simulation>