Сравнение методов

	Агентное моделирование (АМ)	Анализ Количественной Оценки Рекуррентности (АКОР)
Область	Имитационное моделирование	Сложные системы
Семейство	Process / pipeline	Machine learning
Год появления	1970s–1990s (formalized as a field)	2007
Автор метода	Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s)	Marwan, Romano, Thiel & Kurths
Тип	Computational simulation method	Nonlinear time-series characterization
Основополагающий источник	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.
Другие названия	ABM, Ajan Tabanlı Modelleme (ABM), multi-agent simulation, individual-based modeling	RQA, Recurrence Plot Analysis, Nonlinear Recurrence Analysis, Tekrarlama Kantifikasyon Analizi
Связанные	5	2
Сводка	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.