Comparar métodos

	Investigación Causal-Comparativa Asistida por Simulación	Modelado Basado en Agentes (MBA)
Campo	Diseño de investigación	Simulación
Familia	Process / pipeline	Process / pipeline
Año de origen	Late 20th–early 21st century (hybrid approach formalized ~1990s–2000s)	1970s–1990s (formalized as a field)
Autor original	Synthesized from causal-comparative tradition (Donald T. Campbell; Julian Stanley) and simulation methodology	Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s)
Tipo	Hybrid observational-simulation design	Computational simulation method
Fuente seminal	Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2019). How to Design and Evaluate Research in Education (10th ed.). McGraw-Hill. ISBN: 978-1260087352	Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press.
Alias	simulation-augmented causal-comparative design, ex post facto simulation design, SA-CCR, causal-comparative with simulation validation	ABM, Ajan Tabanlı Modelleme (ABM), multi-agent simulation, individual-based modeling
Relacionados	4	5
Resumen	Simulation-assisted causal-comparative research is a hybrid observational design that combines the ex post facto logic of causal-comparative studies — comparing groups that differ on a naturally occurring variable — with computational simulation to strengthen causal inference, test counterfactuals, and assess the robustness of observed group differences. By augmenting real-world comparisons with simulated scenarios, researchers can explore causal mechanisms that cannot be manipulated experimentally.	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.