Comparer des méthodes

	Analyse de sensibilité basée sur les agents	Simulation de Monte-Carlo
Domaine	Simulation	Prise de décision
Famille	Process / pipeline	MCDM
Année d'origine	2000s–2010s	1949
Auteur d'origine	Adapted from global sensitivity analysis (Saltelli et al.) for agent-based models	Metropolis, N., Ulam, S.
Type	Simulation-based sensitivity analysis	Robustness wrapper — Monte Carlo uncertainty propagation
Source fondatrice	Saltelli, A., Tarantola, S., Campolongo, F., & Ratto, M. (2004). Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. John Wiley & Sons. ISBN: 9780470870938	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Alias	ABM sensitivity analysis, ABSA, SA for ABMs, agent-based model sensitivity testing	—
Apparentées	3	0
Résumé	Agent-based sensitivity analysis (ABSA) applies sensitivity analysis techniques to agent-based models (ABMs) to determine which input parameters most strongly influence emergent outputs. Because ABMs are stochastic and nonlinear, standard analytical derivatives are unavailable; ABSA uses designed simulation experiments — screening methods, variance-based indices, or regression-based surrogates — to rank parameter importance and guide model calibration and validation.	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.