Linganisha mbinu

	Uiguzi wa Chaguo Diskriti	Uchanganuzi wa Pamoja	Uigaji-ndogo
Nyanja	Uigaji	Muundo wa Majaribio	Uigaji
Familia	Process / pipeline	Hypothesis test	Process / pipeline
Mwaka wa asili	1974 (McFadden's Nobel-cited logit); simulation extensions throughout 1990s–2000s	1978	1957
Mwanzilishi	Daniel McFadden (random utility theory); Kenneth Train (simulation methods)	Paul E. Green & V. Srinivasan	Guy Orcutt (concept, 1957); modern tax-transfer frameworks developed through EUROMOD and related projects
Aina	Discrete choice modelling with Monte Carlo simulation	Decomposition-based utility estimation	Policy simulation / computational social science
Chanzo asilia	Train, K.E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press.	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.
Majina mbadala	stated preference simulation, SP simulation, revealed preference modelling, Ayrık Seçim Simülasyonu (Stated Preference / SP Simulation)	CBC conjoint, choice-based conjoint, adaptive conjoint analysis, full-profile conjoint	Mikrosimülasyon, micro-simulation, policy microsimulation
Zinazohusiana	5	6	5
Muhtasari	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.	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.