Compara mètodes
Revisa els mètodes seleccionats l'un al costat de l'altre; les files que difereixen es ressalten.
| Equilibri Perfecte de Subjocs× | Equilibri de Nash Bayesiana× | |
|---|---|---|
| Camp | Teoria de jocs | Teoria de jocs |
| Família | Machine learning | Machine learning |
| Any d'origen≠ | 1965 | 1967 |
| Autor original≠ | Reinhard Selten | John Harsanyi |
| Tipus | algorithm | algorithm |
| Font 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 ↗ |
| Àlies | Backward Induction, Sequential Equilibrium, Extensive-Form Equilibrium | BNE, Perfect Bayesian Equilibrium, Type-Contingent Equilibrium |
| Relacionats | 4 | 4 |
| Resum≠ | 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. |
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