পদ্ধতির তুলনা করুন

	স্ব-সংগঠিত সংকট (Self-Organized Criticality - SOC)	এজেন্ট-ভিত্তিক মডেলিং (ABM)	ফ্র্যাক্টাল বিশ্লেষণ
ক্ষেত্র	জটিল ব্যবস্থা	অনুকরণ	জটিল ব্যবস্থা
পরিবার	Regression model	Process / pipeline	Machine learning
উদ্ভবের বছর	1987	1970s–1990s (formalized as a field)	1983
প্রবর্তক	Per Bak, Chao Tang & Kurt Wiesenfeld	Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s)	Benoit Mandelbrot
ধরন	Dynamical systems model	Computational simulation method	Geometric complexity quantification
মৌলিক উৎস	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
অপর নাম	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
সম্পর্কিত	3	5	2
সারসংক্ষেপ	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.