Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Modelo Principal-Agente× | Equilibrio de Nash× | |
|---|---|---|
| Campo | Teoría de juegos | Teoría de juegos |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 1976 | 1950 |
| Autor original≠ | Michael Jensen, William Meckling, Bengt Holmstrom | John Nash |
| Tipo | algorithm | algorithm |
| Fuente seminal≠ | Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305-360. DOI ↗ | Nash, J. F. (1950). Equilibrium points in N-person games. Proceedings of the National Academy of Sciences, 36(1), 48-49. DOI ↗ |
| Alias≠ | Agency Theory, Hidden Action Problem, Moral Hazard | Lemke-Howson Equilibrium, Completely Labeled Pair |
| Relacionados | 4 | 4 |
| Resumen≠ | The Principal-Agent Model analyzes how a principal (e.g., owner, employer, policymaker) can incentivize an agent (e.g., manager, employee, firm) to act in the principal's interest when the agent has private information or can take hidden actions. Formalized by Jensen and Meckling in 1976, the model identifies agency costs arising from moral hazard (the agent exerts less effort than desired) and adverse selection (the agent hides unfavorable information). Optimal contracts balance incentives with risk allocation. | 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. |
| ScholarGateConjunto de datos ↗ |
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