Methoden vergleichen

	Simulationsgestützte Trendforschung	Monte-Carlo-Simulation
Fachgebiet	Forschungsdesign	Entscheidungsfindung
Familie	Process / pipeline	MCDM
Entstehungsjahr	1990s–2000s (convergence of computational simulation with survey-based trend designs)	1949
Urheber	Synthesized from trend research (Creswell) and Monte Carlo / agent-based simulation traditions (Mooney, 1997)	Metropolis, N., Ulam, S.
Typ	Quantitative research design with computational augmentation	Robustness wrapper — Monte Carlo uncertainty propagation
Wegweisende Quelle	Creswell, J. W., & Creswell, J. D. (2023). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (6th ed.). SAGE Publications. ISBN: 978-1071817971	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Aliasnamen	simulation-augmented trend study, Monte Carlo trend research, computational trend analysis, simulation-based longitudinal trend design	—
Verwandt	4	0
Zusammenfassung	Simulation-assisted trend research combines repeated cross-sectional survey data collected at multiple time points with computational simulation techniques — such as Monte Carlo methods or agent-based modeling — to project, validate, and stress-test observed trends. It extends classic trend research by replacing or supplementing extrapolation with probabilistic scenario modeling, allowing researchers to quantify uncertainty around trend trajectories and explore counterfactual futures under varying assumptions.	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.