Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Concurrence de Stackelberg× | Équilibre de Nash bayésien× | |
|---|---|---|
| Domaine | Théorie des jeux | Théorie des jeux |
| Famille | Machine learning | Machine learning |
| Année d'origine≠ | 1934 | 1967 |
| Auteur d'origine≠ | Heinrich von Stackelberg | John Harsanyi |
| Type | algorithm | algorithm |
| Source fondatrice≠ | von Stackelberg, H. (1934). Marktform und Gleichgewicht. Julius Springer. link ↗ | Harsanyi, J. C. (1967). Games with incomplete information played by Bayesian players, Parts I, II, and III. Management Science, 14(3), 159-182. DOI ↗ |
| Alias | Quantity Leadership, Sequential Oligopoly, Stackelberg Equilibrium | BNE, Perfect Bayesian Equilibrium, Type-Contingent Equilibrium |
| Apparentées | 4 | 4 |
| Résumé≠ | Stackelberg Competition models sequential oligopolistic markets where one firm (the leader) commits to a quantity first, and other firms (followers) observe this choice and respond. Introduced by Heinrich von Stackelberg in 1934, the model captures first-mover advantage in quantity-setting competition. The resulting Stackelberg Equilibrium, found by backward induction, yields the leader higher profit than simultaneous (Cournot) competition. | 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. |
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