विधियों की तुलना करें

	Robust Agent-Based Modeling	Robust Scenario Analysis
क्षेत्र	अनुकरण	अनुकरण
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	2000s	1950 (foundations); 2003 (modern RDM formulation)
प्रवर्तक	Ligmann-Zielinska, A.; Railsback, S. F.; Grimm, V.	Wald, A. (minimax foundation); Lempert et al. (RDM framework)
प्रकार	Simulation robustness framework	Scenario-based robustness evaluation
मौलिक स्रोत	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.
उपनाम	Robust ABM, ABM Robustness Analysis, Uncertainty-Aware ABM, Robust Multi-Agent Simulation	RSA, Robust Scenario Planning, Worst-Case Scenario Analysis, Minimax Regret Scenario Analysis
संबंधित	5	5
सारांश	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.