So sánh phương pháp

	Nghiên cứu so sánh nhân quả có hỗ trợ mô phỏng	Mô hình hóa dựa trên tác nhân (ABM)
Lĩnh vực	Thiết kế nghiên cứu	Mô phỏng
Họ	Process / pipeline	Process / pipeline
Năm ra đời	Late 20th–early 21st century (hybrid approach formalized ~1990s–2000s)	1970s–1990s (formalized as a field)
Người khởi xướng	Synthesized from causal-comparative tradition (Donald T. Campbell; Julian Stanley) and simulation methodology	Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s)
Loại	Hybrid observational-simulation design	Computational simulation method
Công trình gốc	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.
Tên gọi khác	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
Liên quan	4	5
Tóm tắt	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.