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aamas-experiments
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-Skills/skills/aamas-experiments/SKILL.md
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# 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,