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| Agent-based sensitivity analysis× | MONTE-CARLO-SIMULATION× | |
|---|---|---|
| Tudományterület≠ | Szimuláció | Döntéshozatal |
| Módszercsalád≠ | Process / pipeline | MCDM |
| Keletkezés éve≠ | 2000s–2010s | 1949 |
| Megalkotó≠ | Adapted from global sensitivity analysis (Saltelli et al.) for agent-based models | Metropolis, N., Ulam, S. |
| Típus≠ | Simulation-based sensitivity analysis | Robustness wrapper — Monte Carlo uncertainty propagation |
| Alapmű≠ | 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 DOI ↗ |
| Alternatív nevek≠ | ABM sensitivity analysis, ABSA, SA for ABMs, agent-based model sensitivity testing | — |
| Kapcsolódó≠ | 3 | 0 |
| Összefoglaló≠ | 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. |
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