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| 베이즈 시스템 다이내믹스× | 확률론적 시스템 동학× | |
|---|---|---|
| 분야 | 시뮬레이션 | 시뮬레이션 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 2000s–2010s | 1980s–2000s |
| 창시자≠ | Rahmandad, H.; Sterman, J. D. and related SD/Bayesian communities | Jay W. Forrester (base SD); stochastic extensions developed through 1980s–2000s by multiple researchers |
| 유형≠ | Simulation with probabilistic parameter learning | Continuous stochastic simulation |
| 원전≠ | Rahmandad, H., & Sterman, J. D. (2008). Heterogeneity and network structure in the dynamics of diffusion: Comparing agent-based and differential equation models. Management Science, 54(5), 998–1014. DOI ↗ | Sterman, J.D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. Irwin McGraw-Hill. ISBN: 978-0072389159 |
| 별칭 | BSD, Bayesian SD, Bayesian SD modeling, Probabilistic System Dynamics | SSD, stochastic stock-flow modelling, probabilistic system dynamics, random system dynamics |
| 관련≠ | 6 | 5 |
| 요약≠ | Bayesian System Dynamics (BSD) integrates Bayesian statistical inference with causal stock-and-flow simulation models. Prior knowledge about model parameters is updated using observed time-series data to produce posterior distributions, which are then propagated through the simulation to yield probabilistic forecasts and policy evaluations rather than single deterministic trajectories. | Stochastic System Dynamics (SSD) extends conventional system dynamics by replacing fixed parameter values and deterministic flow equations with probability distributions and random draws. Running many replications of the stock-flow model yields probabilistic trajectories — confidence bands rather than single lines — enabling rigorous uncertainty quantification and risk analysis in complex feedback systems such as epidemic models, supply chains, and energy policy scenarios. |
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