Сравнение на методи

	Устойчиво моделиране, базирано на агенти	Анализ на устойчиви сценарии
Област	Симулационно моделиране	Симулационно моделиране
Семейство	Process / pipeline	Process / pipeline
Година на възникване	2000s	1950 (foundations); 2003 (modern RDM formulation)
Създател	Ligmann-Zielinska, A.; Railsback, S. F.; Grimm, V.	Wald, A. (minimax foundation); Lempert et al. (RDM framework)
Тип	Simulation robustness framework	Scenario-based robustness evaluation
Основополагащ източник	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.
Други названия	Robust ABM, ABM Robustness Analysis, Uncertainty-Aware ABM, Robust Multi-Agent Simulation	RSA, Robust Scenario Planning, Worst-Case Scenario Analysis, Minimax Regret Scenario Analysis
Свързани	5	5
Резюме	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.