Сравнение методов

	Байесовское моделирование на основе агентов	Агентное моделирование (АМ)
Область	Имитационное моделирование	Имитационное моделирование
Семейство	Process / pipeline	Process / pipeline
Год появления	2000s–2010s	1970s–1990s (formalized as a field)
Автор метода	Sunnaker et al. / Grazzini & Richiardi (among key contributors)	Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s)
Тип	Simulation calibration and inference framework	Computational simulation method
Основополагающий источник	Sunnaker, M., Busetto, A. G., Numminen, E., Corander, J., Foll, M., Dessimoz, C. (2013). Approximate Bayesian Computation. PLOS Computational Biology, 9(1), e1002803.	Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press.
Другие названия	Bayesian ABM, ABC-ABM, Bayesian Calibration of ABM, Bayesian Agent Simulation	ABM, Ajan Tabanlı Modelleme (ABM), multi-agent simulation, individual-based modeling
Связанные	5	5
Сводка	Bayesian Agent-Based Modeling integrates Bayesian statistical inference with agent-based simulation to calibrate model parameters and quantify uncertainty. Rather than fixing agent rules and parameters by assumption, this approach treats unknown parameters as probability distributions and updates them systematically against observed data, yielding a full posterior over plausible model configurations.	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.