Comparar métodos

	Microsimulação	Modelo Logit Misto	Simulação de Monte Carlo
Área	Simulação	Econometria	Tomada de decisão
Família	Process / pipeline	Regression model	MCDM
Ano de origem	1957	2000	1949
Autor original	Guy Orcutt (concept, 1957); modern tax-transfer frameworks developed through EUROMOD and related projects	Daniel McFadden & Kenneth Train	Metropolis, N., Ulam, S.
Tipo	Policy simulation / computational social science	Random-parameters discrete choice model	Robustness wrapper — Monte Carlo uncertainty propagation
Fonte seminal	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	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Outros nomes	Mikrosimülasyon, micro-simulation, policy microsimulation	Random Parameters Logit, Mixed Multinomial Logit, Error Components Logit, Karma Logit Modeli	—
Relacionados	5	3	0
Resumo	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.	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.