方法对比

	自组织临界性	基于主体的建模（ABM）	循环量化分析 (RQA)
领域	复杂系统	仿真	复杂系统
方法族	Regression model	Process / pipeline	Machine learning
起源年份	1987	1970s–1990s (formalized as a field)	2007
提出者	Per Bak, Chao Tang & Kurt Wiesenfeld	Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s)	Marwan, Romano, Thiel & Kurths
类型	Dynamical systems model	Computational simulation method	Nonlinear time-series characterization
开创性文献	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.	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.
别名	SOC, Sandpile Model, Critical Self-Organization, Kendiliğinden Örgütlenen Kritiklik	ABM, Ajan Tabanlı Modelleme (ABM), multi-agent simulation, individual-based modeling	RQA, Recurrence Plot Analysis, Nonlinear Recurrence Analysis, Tekrarlama Kantifikasyon Analizi
相关	3	5	2
摘要	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.	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.