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Μοντέλο Κύριου-Εντολοδόχου×Ισορροπία Nash Bayes (BNE)×
ΠεδίοΘεωρία ΠαιγνίωνΘεωρία Παιγνίων
ΟικογένειαMachine learningMachine learning
Έτος προέλευσης19761967
ΔημιουργόςMichael Jensen, William Meckling, Bengt HolmstromJohn Harsanyi
Τύποςalgorithmalgorithm
Θεμελιώδης πηγήJensen, 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 ↗
Εναλλακτικές ονομασίεςAgency Theory, Hidden Action Problem, Moral HazardBNE, Perfect Bayesian Equilibrium, Type-Contingent Equilibrium
Συναφείς44
ΣύνοψηThe 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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ScholarGateΣύγκριση μεθόδων: Principal-Agent Model · Bayesian Nash Equilibrium. Ανακτήθηκε στις 2026-06-18 από https://scholargate.app/el/compare