Compara mètodes

	Anàlisi de Sensibilitat Basada en Agents	Mostreig d'Hipercub Llatí
Camp	Simulació	Simulació
Família	Process / pipeline	Process / pipeline
Any d'origen	2000s–2010s	1979
Autor original	Adapted from global sensitivity analysis (Saltelli et al.) for agent-based models	—
Tipus	Simulation-based sensitivity analysis	Stratified space-filling sampling design
Font seminal	Saltelli, A., Tarantola, S., Campolongo, F., & Ratto, M. (2004). Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. John Wiley & Sons. ISBN: 9780470870938	McKay, M.D., Beckman, R.J. & Conover, W.J. (1979). A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code. Technometrics, 21(2), 239-245.
Àlies	ABM sensitivity analysis, ABSA, SA for ABMs, agent-based model sensitivity testing	LHS, Latin Hiperküp Örnekleme (LHS) ve Duyarlılık Analizi, stratified sampling design, space-filling design
Relacionats	3	4
Resum	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.	Latin Hypercube Sampling (LHS) is a stratified space-filling design for computer experiments, introduced by McKay, Beckman, and Conover in 1979. It divides each input variable's range into equally probable strata and draws exactly one sample per stratum, ensuring that the full input space is covered with far fewer model evaluations than standard Monte Carlo simulation requires. It is routinely paired with global sensitivity analysis — particularly Sobol indices — to quantify how much each input drives output variability.