Salīdzināt metodes

	Diskrētās izvēles simulācija	Jauktais logit modelis	Monte Carlo simulācija
Nozare	Simulācija	Ekonometrija	Lēmumu pieņemšana
Saime	Process / pipeline	Regression model	MCDM
Izcelsmes gads	1974 (McFadden's Nobel-cited logit); simulation extensions throughout 1990s–2000s	2000	1949
Autors	Daniel McFadden (random utility theory); Kenneth Train (simulation methods)	Daniel McFadden & Kenneth Train	Metropolis, N., Ulam, S.
Tips	Discrete choice modelling with Monte Carlo simulation	Random-parameters discrete choice model	Robustness wrapper — Monte Carlo uncertainty propagation
Pirmavots	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	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Citi nosaukumi	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	—
Saistītās	5	3	0
Kopsavilkums	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.	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.