방법 비교

	컨조인트 분석	미시모의	Mixed Logit Model
분야	실험설계	시뮬레이션	계량경제학
계열	Hypothesis test	Process / pipeline	Regression model
기원 연도	1978	1957	2000
창시자	Paul E. Green & V. Srinivasan	Guy Orcutt (concept, 1957); modern tax-transfer frameworks developed through EUROMOD and related projects	Daniel McFadden & Kenneth Train
유형	Decomposition-based utility estimation	Policy simulation / computational social science	Random-parameters discrete choice model
원전	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
별칭	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
관련	6	5	3
요약	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.