AWhat changed
Grew empirical meta-games of policies by learning approximate best responses to policy mixtures, then applied meta-strategy solvers; a decoupled hierarchy reduced the full payoff-tensor bottleneck.
Scientific scope
Grew empirical meta-games of policies by learning approximate best responses to policy mixtures, then applied meta-strategy solvers; a decoupled hierarchy reduced the full payoff-tensor bottleneck. Generalizes prior double-oracle, empirical game theory and fictitious-play ideas. Approximate response quality, finite policy populations and choice of meta-solver limit guarantees; a meta-game solution is not automatically a full-game Nash equilibrium. Full empirical payoff tensors have combinatorial cost, not free scaling.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Generalizes prior double-oracle, empirical game theory and fictitious-play ideas. Approximate response quality, finite policy populations and choice of meta-solver limit guarantees; a meta-game solution is not automatically a full-game Nash equilibrium. Full empirical payoff tensors have combinatorial cost, not free scaling.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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