Comparer des méthodes

	Modèle d'utilité aléatoire	Modèle Principal-Agent
Domaine	Théorie des jeux	Théorie des jeux
Famille	Machine learning	Machine learning
Année d'origine	1974	1976
Auteur d'origine	Daniel McFadden	Michael Jensen, William Meckling, Bengt Holmstrom
Type	algorithm	algorithm
Source fondatrice	McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), Frontiers in Econometrics (pp. 105-142). Academic Press.	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.
Alias	Discrete Choice Model, Probabilistic Choice, Stochastic Utility	Agency Theory, Hidden Action Problem, Moral Hazard
Apparentées	4	4
Résumé	The Random Utility Model explains discrete choice behavior by assuming agents derive uncertain utilities from alternatives and choose the option yielding highest utility. Introduced by Daniel McFadden in 1974, the model decomposes utility into systematic (observable) and random (idiosyncratic) components, permitting probabilistic choice predictions. The logit model, a parametric specification, yields closed-form choice probabilities that are widely used in marketing, transportation, and environmental valuation.	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.