Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Equilibrio Perfecto en Subjuegos× | Equilibrio de Nash Bayesiano× | |
|---|---|---|
| Campo | Teoría de juegos | Teoría de juegos |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 1965 | 1967 |
| Autor original≠ | Reinhard Selten | John Harsanyi |
| Tipo | algorithm | algorithm |
| Fuente seminal≠ | Selten, R. (1965). Spieltheoretische Behandlung eines Oligopolmodells mit Nachfrageträgheit. Zeitschrift für die gesamte Staatswissenschaft, 121, 301-324. 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 | Backward Induction, Sequential Equilibrium, Extensive-Form Equilibrium | BNE, Perfect Bayesian Equilibrium, Type-Contingent Equilibrium |
| Relacionados | 4 | 4 |
| Resumen≠ | Subgame Perfect Equilibrium (SPE) is a refinement of Nash Equilibrium for sequential games, introduced by Reinhard Selten in 1965. It requires that strategy profiles constitute a Nash Equilibrium in every subgame, eliminating non-credible threats and incredible promises. Backward induction is the primary computational method for finding SPE in finite games. | 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. |
| ScholarGateConjunto de datos ↗ |
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