Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Equilibrio de Nash× | Equilibrio de Nash Bayesiano× | |
|---|---|---|
| Campo | Teoría de juegos | Teoría de juegos |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 1950 | 1967 |
| Autor original≠ | John Nash | John Harsanyi |
| Tipo | algorithm | algorithm |
| Fuente seminal≠ | Nash, J. F. (1950). Equilibrium points in N-person games. Proceedings of the National Academy of Sciences, 36(1), 48-49. DOI ↗ | 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≠ | Lemke-Howson Equilibrium, Completely Labeled Pair | BNE, Perfect Bayesian Equilibrium, Type-Contingent Equilibrium |
| Relacionados | 4 | 4 |
| Resumen≠ | 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. | 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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