Comparar métodos

	Simulación de Elección Discreta	Modelo Logit Mixto
Campo	Simulación	Econometría
Familia	Process / pipeline	Regression model
Año de origen	1974 (McFadden's Nobel-cited logit); simulation extensions throughout 1990s–2000s	2000
Autor original	Daniel McFadden (random utility theory); Kenneth Train (simulation methods)	Daniel McFadden & Kenneth Train
Tipo	Discrete choice modelling with Monte Carlo simulation	Random-parameters discrete choice model
Fuente seminal	Train, K.E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press.	Train, K. E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press. ISBN: 978-0-521-74738-7
Alias	stated preference simulation, SP simulation, revealed preference modelling, Ayrık Seçim Simülasyonu (Stated Preference / SP Simulation)	Random Parameters Logit, Mixed Multinomial Logit, Error Components Logit, Karma Logit Modeli
Relacionados	5	3
Resumen	Discrete choice simulation is a behavioural modelling method — grounded in random utility theory formalised by Daniel McFadden in the 1970s and extended to simulation-based estimation by Kenneth Train — that estimates how individuals choose among mutually exclusive alternatives and then uses those estimated preference parameters to forecast how choice shares would shift under hypothetical policy or market scenarios. It is the dominant quantitative tool in transport demand analysis, health economics, environmental valuation, and marketing research.	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.