AWhat changed
Ranks finite strategy profiles through the stationary distribution of a mutation-selection Markov chain, retaining intransitive cycles rather than forcing a scalar transitive dominance interpretation.
Scientific scope
Ranks finite strategy profiles through the stationary distribution of a mutation-selection Markov chain, retaining intransitive cycles rather than forcing a scalar transitive dominance interpretation. The ranking depends on the supplied empirical strategy pool, payoffs and selection parameters. Polynomial work in the explicit payoff table can still be combinatorial in player count. The finite-intensity stationary ranking is not a Nash equilibrium or universal agent ordering. Equal-contributor marks identify Papadimitriou, Piliouras and Tuyls, not the first author.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. The ranking depends on the supplied empirical strategy pool, payoffs and selection parameters. Polynomial work in the explicit payoff table can still be combinatorial in player count. The finite-intensity stationary ranking is not a Nash equilibrium or universal agent ordering. Equal-contributor marks identify Papadimitriou, Piliouras and Tuyls, not the first author.
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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