مقایسهٔ روش‌ها

	مدل‌سازی عامل‌محور (ABM)	تحلیل فرکتالی	تحلیل کمی بازگشت (RQA)
حوزه	شبیه‌سازی	سیستم‌های پیچیده	سیستم‌های پیچیده
خانواده	Process / pipeline	Machine learning	Machine learning
سال پیدایش	1970s–1990s (formalized as a field)	1983	2007
پدیدآور	Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s)	Benoit Mandelbrot	Marwan, Romano, Thiel & Kurths
نوع	Computational simulation method	Geometric complexity quantification	Nonlinear time-series characterization
منبع بنیادین	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.
نام‌های دیگر	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
مرتبط	5	2	2
خلاصه	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.