Vertaile menetelmiä
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| Stokastinen diskreettitapahtumasimulaatio× | Agenttipohjainen mallinnus (ABM)× | |
|---|---|---|
| Tieteenala | Simulointi | Simulointi |
| Menetelmäperhe | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | 1960s–1970s | 1970s–1990s (formalized as a field) |
| Kehittäjä≠ | Banks, Carson, Nelson, Nicol; Law, A. M. | Thomas Schelling and Robert Axelrod (foundational contributions, 1970s–1990s) |
| Tyyppi≠ | Stochastic simulation model | Computational simulation method |
| Alkuperäislähde≠ | Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall. ISBN: 9780136062127 | Axelrod, R. (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press. DOI ↗ |
| Rinnakkaisnimet | Stochastic DES, SDES, Probabilistic DES, Monte Carlo DES | ABM, Ajan Tabanlı Modelleme (ABM), multi-agent simulation, individual-based modeling |
| Liittyvät≠ | 6 | 5 |
| Tiivistelmä≠ | 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. | Agent-based modeling (ABM) is a computational simulation method, formalized through the work of Thomas Schelling and Robert Axelrod in the 1970s–1990s, that simulates the behavior of complex systems by specifying and running autonomous agents — individuals, firms, cells, or any bounded entity — whose local interactions with each other and with their environment collectively produce global, system-level patterns that could not be predicted from any single agent's rules alone. |
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