手法を比較

	ベイズ的個体ベースモデリング	モンテカルロシミュレーション
分野	シミュレーション	意思決定
系統	Process / pipeline	MCDM
提唱年	2000s–2010s	1949
提唱者	Sunnaker et al. / Grazzini & Richiardi (among key contributors)	Metropolis, N., Ulam, S.
種類	Simulation calibration and inference framework	Robustness wrapper — Monte Carlo uncertainty propagation
原典	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
別名	Bayesian ABM, ABC-ABM, Bayesian Calibration of ABM, Bayesian Agent Simulation	—
関連	5	0
概要	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.