Módszerek összehasonlítása

	Véletlen hasznossági modell	Bayes-Nash Egyensúly
Tudományterület	Játékelmélet	Játékelmélet
Módszercsalád	Machine learning	Machine learning
Keletkezés éve	1974	1967
Megalkotó	Daniel McFadden	John Harsanyi
Típus	algorithm	algorithm
Alapmű	McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), Frontiers in Econometrics (pp. 105-142). Academic Press.	Harsanyi, J. C. (1967). Games with incomplete information played by Bayesian players, Parts I, II, and III. Management Science, 14(3), 159-182.
Alternatív nevek	Discrete Choice Model, Probabilistic Choice, Stochastic Utility	BNE, Perfect Bayesian Equilibrium, Type-Contingent Equilibrium
Kapcsolódó	4	4
Összefoglaló	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.	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.