Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uundaji Imara wa Mawakala× | Uchambuzi Imara wa Usikivu× | |
|---|---|---|
| Nyanja | Uigaji | Uigaji |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 2000s | 1990s–2000s |
| Mwanzilishi≠ | Ligmann-Zielinska, A.; Railsback, S. F.; Grimm, V. | Saltelli, A. and colleagues |
| Aina≠ | Simulation robustness framework | Simulation-based robustness assessment pipeline |
| Chanzo asilia≠ | Ligmann-Zielinska, A., Cheetham, W. (2006). Spatially-explicit sensitivity analysis of an agent-based model of land use change. International Journal of Geographical Information Science, 20(12), 1355-1377. link ↗ | Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley. ISBN: 9780470059975 |
| Majina mbadala | Robust ABM, ABM Robustness Analysis, Uncertainty-Aware ABM, Robust Multi-Agent Simulation | RSA, Robust SA, Sensitivity Analysis under Uncertainty, Uncertainty-robust sensitivity analysis |
| Zinazohusiana≠ | 5 | 3 |
| Muhtasari≠ | Robust Agent-Based Modeling (Robust ABM) integrates systematic uncertainty quantification and sensitivity analysis into agent-based simulation workflows. Rather than relying on a single parameter configuration, it explores the full parameter space to identify which inputs drive model outcomes, ensuring that conclusions hold across plausible input ranges and model structures. | Robust Sensitivity Analysis (RSA) systematically evaluates how much variation in model outputs can be attributed to uncertainty or variation in model inputs, with an explicit focus on conclusions that remain valid across a wide range of plausible input conditions. It goes beyond standard sensitivity analysis by asking not only which inputs matter most, but which findings are truly robust — stable regardless of assumptions made under uncertainty. |
| ScholarGateSeti ya data ↗ |
|
|