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Agentowa analiza wrażliwości×Symulacja Monte Carlo×
DziedzinaSymulacjaPodejmowanie decyzji
RodzinaProcess / pipelineMCDM
Rok powstania2000s–2010s1949
TwórcaAdapted from global sensitivity analysis (Saltelli et al.) for agent-based modelsMetropolis, N., Ulam, S.
TypSimulation-based sensitivity analysisRobustness wrapper — Monte Carlo uncertainty propagation
Źródło pierwotneSaltelli, A., Tarantola, S., Campolongo, F., & Ratto, M. (2004). Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. John Wiley & Sons. ISBN: 9780470870938Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗
Inne nazwyABM sensitivity analysis, ABSA, SA for ABMs, agent-based model sensitivity testing
Pokrewne30
PodsumowanieAgent-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.
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ScholarGatePorównaj metody: Agent-based sensitivity analysis · MONTE-CARLO-SIMULATION. Pobrano 2026-06-15 z https://scholargate.app/pl/compare