手法を比較

	ロバストなエージェントベースモデリング	深遠な不確実性下における最悪ケースとミニマックス後悔評価を用いた頑健シナリオ分析
分野	シミュレーション	シミュレーション
系統	Process / pipeline	Process / pipeline
提唱年	2000s	1950 (foundations); 2003 (modern RDM formulation)
提唱者	Ligmann-Zielinska, A.; Railsback, S. F.; Grimm, V.	Wald, A. (minimax foundation); Lempert et al. (RDM framework)
種類	Simulation robustness framework	Scenario-based robustness evaluation
原典	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.
別名	Robust ABM, ABM Robustness Analysis, Uncertainty-Aware ABM, Robust Multi-Agent Simulation	RSA, Robust Scenario Planning, Worst-Case Scenario Analysis, Minimax Regret Scenario Analysis
関連	5	5
概要	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.