Comparar métodos

	Modelagem Robusta Baseada em Agentes	Modelagem Baseada em Agentes (ABM)
Área	Simulação	Simulação
Família	Process / pipeline	Process / pipeline
Ano de origem	2000s	1970s–1990s (formalized as a field)
Autor original	Ligmann-Zielinska, A.; Railsback, S. F.; Grimm, V.	Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s)
Tipo	Simulation robustness framework	Computational simulation method
Fonte seminal	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.	Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press.
Outros nomes	Robust ABM, ABM Robustness Analysis, Uncertainty-Aware ABM, Robust Multi-Agent Simulation	ABM, Ajan Tabanlı Modelleme (ABM), multi-agent simulation, individual-based modeling
Relacionados	5	5
Resumo	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.	Agent-based modeling (ABM) is a computational simulation method, formalized through the work of Thomas Schelling and Robert Axelrod in the 1970s–1990s, that simulates the behavior of complex systems by specifying and running autonomous agents — individuals, firms, cells, or any bounded entity — whose local interactions with each other and with their environment collectively produce global, system-level patterns that could not be predicted from any single agent's rules alone.