Comparer des méthodes

	Modélisation bayésienne à base d'agents	Simulation de Monte-Carlo
Domaine	Simulation	Prise de décision
Famille	Process / pipeline	MCDM
Année d'origine	2000s–2010s	1949
Auteur d'origine	Sunnaker et al. / Grazzini & Richiardi (among key contributors)	Metropolis, N., Ulam, S.
Type	Simulation calibration and inference framework	Robustness wrapper — Monte Carlo uncertainty propagation
Source fondatrice	Sunnaker, M., Busetto, A. G., Numminen, E., Corander, J., Foll, M., Dessimoz, C. (2013). Approximate Bayesian Computation. PLOS Computational Biology, 9(1), e1002803.	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Alias	Bayesian ABM, ABC-ABM, Bayesian Calibration of ABM, Bayesian Agent Simulation	—
Apparentées	5	0
Résumé	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.	MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.