Bandingkan metode

	Pemodelan Berbasis Agen yang Kokoh	Analisis Skenario Robust
Bidang	Simulasi	Simulasi
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	2000s	1950 (foundations); 2003 (modern RDM formulation)
Pencetus	Ligmann-Zielinska, A.; Railsback, S. F.; Grimm, V.	Wald, A. (minimax foundation); Lempert et al. (RDM framework)
Tipe	Simulation robustness framework	Scenario-based robustness evaluation
Sumber perintis	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.	Wald, A. (1950). Statistical Decision Functions. Wiley, New York.
Alias	Robust ABM, ABM Robustness Analysis, Uncertainty-Aware ABM, Robust Multi-Agent Simulation	RSA, Robust Scenario Planning, Worst-Case Scenario Analysis, Minimax Regret Scenario Analysis
Terkait	5	5
Ringkasan	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 Scenario Analysis evaluates a set of candidate strategies across a structured collection of plausible future scenarios and selects the strategy that performs acceptably well — or best in the worst case — regardless of which scenario materializes. It merges scenario planning with robustness criteria such as maximin, minimax regret, or satisficing to support decisions under deep, irreducible uncertainty.