Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Équilibre de Nash bayésien× | Équilibre de Nash× | |
|---|---|---|
| Domaine | Théorie des jeux | Théorie des jeux |
| Famille | Machine learning | Machine learning |
| Année d'origine≠ | 1967 | 1950 |
| Auteur d'origine≠ | John Harsanyi | John Nash |
| Type | algorithm | algorithm |
| Source fondatrice≠ | Harsanyi, J. C. (1967). Games with incomplete information played by Bayesian players, Parts I, II, and III. Management Science, 14(3), 159-182. DOI ↗ | Nash, J. F. (1950). Equilibrium points in N-person games. Proceedings of the National Academy of Sciences, 36(1), 48-49. DOI ↗ |
| Alias≠ | BNE, Perfect Bayesian Equilibrium, Type-Contingent Equilibrium | Lemke-Howson Equilibrium, Completely Labeled Pair |
| Apparentées | 4 | 4 |
| Résumé≠ | Bayesian Nash Equilibrium (BNE) extends Nash Equilibrium to games with incomplete information, where players lack full knowledge of others' payoff functions. Introduced by John Harsanyi in 1967, BNE models strategic interaction under uncertainty by representing unknown payoffs as players' private types drawn from a probability distribution. Equilibrium is found by solving for type-contingent strategies that are best responses to all possible type realizations. | Nash Equilibrium is a game-theoretic solution concept where no player can unilaterally deviate to improve their payoff. Formalized by John Nash in 1950, the Lemke-Howson algorithm computationally finds equilibria in bimatrix games by identifying completely labeled vertex pairs in the strategy polytopes. |
| ScholarGateJeu de données ↗ |
|
|