Porovnat metody

	Konstruktová analýza	Mikrosimulace	Model smíšeného logitu
Obor	Plánování experimentů	Simulace	Ekonometrie
Rodina	Hypothesis test	Process / pipeline	Regression model
Rok vzniku	1978	1957	2000
Tvůrce	Paul E. Green & V. Srinivasan	Guy Orcutt (concept, 1957); modern tax-transfer frameworks developed through EUROMOD and related projects	Daniel McFadden & Kenneth Train
Typ	Decomposition-based utility estimation	Policy simulation / computational social science	Random-parameters discrete choice model
Původní zdroj	Green, P.E. & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5(2), 103–123.	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
Další názvy	CBC conjoint, choice-based conjoint, adaptive conjoint analysis, full-profile conjoint	Mikrosimülasyon, micro-simulation, policy microsimulation	Random Parameters Logit, Mixed Multinomial Logit, Error Components Logit, Karma Logit Modeli
Příbuzné	6	5	3
Shrnutí	Conjoint analysis is a preference-measurement technique that decomposes overall product evaluations into the separate utility values — called part-worths — that respondents assign to each attribute level. Formalised by Green and Srinivasan in their seminal 1978 Journal of Consumer Research paper, the method has become the dominant tool in marketing research and product design for quantifying what buyers truly trade off when they choose between options.	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.