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| Bayesian Agent-Based Modeling× | Monte-Carlo-Simulation× | |
|---|---|---|
| Fachgebiet≠ | Simulation | Entscheidungsfindung |
| Familie≠ | Process / pipeline | MCDM |
| Entstehungsjahr≠ | 2000s–2010s | 1949 |
| Urheber≠ | Sunnaker et al. / Grazzini & Richiardi (among key contributors) | Metropolis, N., Ulam, S. |
| Typ≠ | Simulation calibration and inference framework | Robustness wrapper — Monte Carlo uncertainty propagation |
| Wegweisende Quelle≠ | Sunnaker, M., Busetto, A. G., Numminen, E., Corander, J., Foll, M., Dessimoz, C. (2013). Approximate Bayesian Computation. PLOS Computational Biology, 9(1), e1002803. DOI ↗ | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Aliasnamen≠ | Bayesian ABM, ABC-ABM, Bayesian Calibration of ABM, Bayesian Agent Simulation | — |
| Verwandt≠ | 5 | 0 |
| Zusammenfassung≠ | 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. |
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