Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Microsimulação Estocástica× | Simulação Estocástica de Eventos Discretos× | |
|---|---|---|
| Área | Simulação | Simulação |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 1957 | 1960s–1970s |
| Autor original≠ | Guy H. Orcutt | Banks, Carson, Nelson, Nicol; Law, A. M. |
| Tipo≠ | Stochastic individual-level simulation | Stochastic simulation model |
| Fonte seminal≠ | Orcutt, G. H. (1957). A new type of socio-economic system. The Review of Economics and Statistics, 39(2), 116–123. DOI ↗ | Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall. ISBN: 9780136062127 |
| Outros nomes | Probabilistic Microsimulation, Monte Carlo Microsimulation, Stochastic Micro-simulation, SMSM | Stochastic DES, SDES, Probabilistic DES, Monte Carlo DES |
| Relacionados | 6 | 6 |
| Resumo≠ | Stochastic Microsimulation tracks a large population of individual units — people, households, or firms — through time by applying random draws from empirically estimated probability distributions at each transition event. Unlike deterministic counterparts, every state change is decided by chance, preserving realistic heterogeneity and allowing rigorous uncertainty quantification across multiple simulation runs. | Stochastic Discrete-Event Simulation (Stochastic DES) models complex systems by advancing simulated time from one discrete event to the next, drawing event durations and inter-arrival times from fitted probability distributions. It is the standard technique for analyzing queues, manufacturing lines, healthcare pathways, and logistics networks under uncertainty, producing output statistics with confidence intervals. |
| ScholarGateConjunto de dados ↗ |
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