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	Modello Logit Misto	Simulazione Monte Carlo
Campo	Econometria	Processo decisionale
Famiglia	Regression model	MCDM
Anno di origine	2000	1949
Ideatore	Daniel McFadden & Kenneth Train	Metropolis, N., Ulam, S.
Tipo	Random-parameters discrete choice model	Robustness wrapper — Monte Carlo uncertainty propagation
Fonte seminale	Train, K. E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press. ISBN: 978-0-521-74738-7	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Alias	Random Parameters Logit, Mixed Multinomial Logit, Error Components Logit, Karma Logit Modeli	—
Correlati	3	0
Sintesi	The Mixed Logit model, introduced formally by McFadden and Train (2000) and elaborated in Train (2009), is a flexible discrete choice framework that allows preference parameters to vary randomly across decision-makers. By integrating standard logit probabilities over a mixing distribution of coefficients, it overcomes the restrictive independence of irrelevant alternatives (IIA) property and accommodates unobserved taste heterogeneity, panel data correlation, and complex substitution patterns across alternatives.	MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.