Сравнение методов

	Имитационное моделирование дискретного выбора	Микросимуляция	Модель смешанного логита
Область	Имитационное моделирование	Имитационное моделирование	Эконометрика
Семейство	Process / pipeline	Process / pipeline	Regression model
Год появления	1974 (McFadden's Nobel-cited logit); simulation extensions throughout 1990s–2000s	1957	2000
Автор метода	Daniel McFadden (random utility theory); Kenneth Train (simulation methods)	Guy Orcutt (concept, 1957); modern tax-transfer frameworks developed through EUROMOD and related projects	Daniel McFadden & Kenneth Train
Тип	Discrete choice modelling with Monte Carlo simulation	Policy simulation / computational social science	Random-parameters discrete choice model
Основополагающий источник	Train, K.E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press.	O'Donoghue, C. (Ed.) (2014). Handbook of Microsimulation Modelling. Emerald.	Train, K. E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press. ISBN: 978-0-521-74738-7
Другие названия	stated preference simulation, SP simulation, revealed preference modelling, Ayrık Seçim Simülasyonu (Stated Preference / SP Simulation)	Mikrosimülasyon, micro-simulation, policy microsimulation	Random Parameters Logit, Mixed Multinomial Logit, Error Components Logit, Karma Logit Modeli
Связанные	5	5	3
Сводка	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.	Microsimulation is a computational method that simulates policy effects by operating directly on a population of individual micro-units — households, firms, patients — and applying rules to each unit according to its own demographic, economic, and behavioural characteristics. Developed conceptually by Guy Orcutt in 1957, it has become the standard tool for evaluating tax reform, pension systems, and health policy before implementation.	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.