So sánh phương pháp

	Tính tới hạn tự tổ chức	Mô hình hóa dựa trên tác nhân (ABM)	Phân tích Fractal	Phân tích Định lượng Hồi quy (RQA)
Lĩnh vực	Hệ thống phức hợp	Mô phỏng	Hệ thống phức hợp	Hệ thống phức hợp
Họ	Regression model	Process / pipeline	Machine learning	Machine learning
Năm ra đời	1987	1970s–1990s (formalized as a field)	1983	2007
Người khởi xướng	Per Bak, Chao Tang & Kurt Wiesenfeld	Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s)	Benoit Mandelbrot	Marwan, Romano, Thiel & Kurths
Loại	Dynamical systems model	Computational simulation method	Geometric complexity quantification	Nonlinear time-series characterization
Công trình gốc	Bak, P., Tang, C., & Wiesenfeld, K. (1987). Self-organized criticality: An explanation of 1/f noise. Physical Review Letters, 59(4), 381–384.	Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press.	Mandelbrot, B. B. (1983). The Fractal Geometry of Nature. W. H. Freeman. ISBN: 978-0-7167-1186-5	Marwan, N., Romano, M. C., Thiel, M., & Kurths, J. (2007). Recurrence plots for the analysis of complex systems. Physics Reports, 438(5–6), 237–329.
Tên gọi khác	SOC, Sandpile Model, Critical Self-Organization, Kendiliğinden Örgütlenen Kritiklik	ABM, Ajan Tabanlı Modelleme (ABM), multi-agent simulation, individual-based modeling	Box-Counting Analysis, Fractal Dimension Estimation, Multifractal Analysis, Fraktal Analiz	RQA, Recurrence Plot Analysis, Nonlinear Recurrence Analysis, Tekrarlama Kantifikasyon Analizi
Liên quan	3	5	2	2
Tóm tắt	Self-Organized Criticality (SOC) is a dynamical systems framework introduced by Per Bak, Chao Tang, and Kurt Wiesenfeld in 1987 to explain how large, dissipative systems spontaneously evolve toward a critical state without external fine-tuning. At the critical state, the system produces scale-invariant fluctuations — avalanches whose size and duration follow power-law distributions — and generates 1/f (pink) noise in its power spectrum.	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.	Fractal Analysis quantifies the self-similar, scale-invariant complexity of geometric objects and time series through the fractal dimension D and the Hurst exponent H. Introduced systematically by Benoit Mandelbrot in his 1983 landmark work, the framework extends classical Euclidean geometry to irregular shapes found in nature, finance, physiology, and materials science. It provides a single dimensionless index that captures how completely a pattern fills space across multiple scales.	Recurrence Quantification Analysis (RQA) is a nonlinear method for characterizing the dynamics of a time series by quantifying the small-scale structure of its recurrence plot. Introduced in its modern, comprehensive form by Marwan, Romano, Thiel, and Kurths in 2007, RQA extracts scalar measures — such as recurrence rate, determinism, laminarity, and Shannon entropy — that capture periodicity, chaos, stationarity, and transitions in complex dynamical systems.