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Pemodelan Berbasis Agen yang Kokoh×Simulasi Monte Carlo×
BidangSimulasiPengambilan Keputusan
KeluargaProcess / pipelineMCDM
Tahun asal2000s1949
PencetusLigmann-Zielinska, A.; Railsback, S. F.; Grimm, V.Metropolis, N., Ulam, S.
TipeSimulation robustness frameworkRobustness wrapper — Monte Carlo uncertainty propagation
Sumber perintisLigmann-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 ↗
AliasRobust ABM, ABM Robustness Analysis, Uncertainty-Aware ABM, Robust Multi-Agent Simulation
Terkait50
RingkasanRobust 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.
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ScholarGateBandingkan metode: Robust Agent-Based Modeling · MONTE-CARLO-SIMULATION. Diakses 2026-06-17 dari https://scholargate.app/id/compare