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	Model użyteczności losowej	Równowaga Nasha Bayesa
Dziedzina	Teoria gier	Teoria gier
Rodzina	Machine learning	Machine learning
Rok powstania	1974	1967
Twórca	Daniel McFadden	John Harsanyi
Typ	algorithm	algorithm
Źródło pierwotne	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.
Inne nazwy	Discrete Choice Model, Probabilistic Choice, Stochastic Utility	BNE, Perfect Bayesian Equilibrium, Type-Contingent Equilibrium
Pokrewne	4	4
Podsumowanie	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.