Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Modelado Robusto Basado en Agentes× | Simulación de Monte Carlo× | |
|---|---|---|
| Campo≠ | Simulación | Toma de decisiones |
| Familia≠ | Process / pipeline | MCDM |
| Año de origen≠ | 2000s | 1949 |
| Autor original≠ | Ligmann-Zielinska, A.; Railsback, S. F.; Grimm, V. | Metropolis, N., Ulam, S. |
| Tipo≠ | Simulation robustness framework | Robustness wrapper — Monte Carlo uncertainty propagation |
| Fuente seminal≠ | 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 ↗ | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Alias≠ | Robust ABM, ABM Robustness Analysis, Uncertainty-Aware ABM, Robust Multi-Agent Simulation | — |
| Relacionados≠ | 5 | 0 |
| Resumen≠ | 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. | 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. |
| ScholarGateConjunto de datos ↗ |
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