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| Principal-Agent-Modell× | Bayesianisches Nash-Gleichgewicht× | |
|---|---|---|
| Fachgebiet | Spieltheorie | Spieltheorie |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1976 | 1967 |
| Urheber≠ | Michael Jensen, William Meckling, Bengt Holmstrom | John Harsanyi |
| Typ | algorithm | algorithm |
| Wegweisende Quelle≠ | 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 ↗ | Harsanyi, J. C. (1967). Games with incomplete information played by Bayesian players, Parts I, II, and III. Management Science, 14(3), 159-182. DOI ↗ |
| Aliasnamen | Agency Theory, Hidden Action Problem, Moral Hazard | BNE, Perfect Bayesian Equilibrium, Type-Contingent Equilibrium |
| Verwandt | 4 | 4 |
| Zusammenfassung≠ | 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. | 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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