Comparer des méthodes

	Simulation de choix discrets	Régression logistique multinomiale
Domaine	Simulation	Économétrie
Famille	Process / pipeline	Regression model
Année d'origine	1974 (McFadden's Nobel-cited logit); simulation extensions throughout 1990s–2000s	1974
Auteur d'origine	Daniel McFadden (random utility theory); Kenneth Train (simulation methods)	McFadden
Type	Discrete choice modelling with Monte Carlo simulation	Multinomial logistic regression
Source fondatrice	Train, K.E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press.	McFadden, D. (1974). Conditional Logit Analysis of Qualitative Choice Behavior. In P. Zarembka (Ed.), Frontiers in Econometrics (pp. 105-142). Academic Press. ISBN: 978-0127761503
Alias	stated preference simulation, SP simulation, revealed preference modelling, Ayrık Seçim Simülasyonu (Stated Preference / SP Simulation)	multinomial logistic regression, polytomous logistic regression, softmax regression, Çok Kategorili Lojistik Regresyon
Apparentées	5	5
Résumé	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.	Multinomial logistic regression is a maximum-likelihood method for a nominal (unordered) dependent variable with more than two categories. Building on McFadden's 1974 treatment of qualitative choice, it gives each category its own set of coefficients relative to a reference category.